Coding for A/B testing: Run more AB tests, find more winners
Monday, February 28, 2022
Coding for A/B testing: Run more AB tests, find more winners - Learn HTML, CSS, JS & data tracking for website AB testing and build your own AB tests without the help of a developer
- Bestseller
What you'll learn
- Run more A/B tests and find more A/B test winners
- Build your own A/B tests without the help of a developer
- Set up reliable data tracking for A/B testing
- Do a proper quality assurance to make sure your A/B tests work perfectly
- Learn HTML, CSS and Javascript to run many more A/B tests
Requirements
- A testing tool (like VWO and Optimizely) is not required, but it is useful. Free options are discussed in the course. (Learnings are applicable for all testing tools)
- Eagerness to learn coding for A/B testing
- Eagerness to run many more A/B tests
- No coding knowledge required
- No prior knowledge is needed
- Useful (free) tools will be discussed in the course
Description
** Bestselling A/B testing course on Udemy in the category: A/B testing**
As a Conversion Optimization specialist or as someone working on website A/B testing, your success is often determined by the number of A/B tests you run and the number of test winners you find. So the more tests you run, the higher your chance of success. But very often, the IT department is a major bottleneck for us. When we want to set up a test, it ends up at the bottom of the IT backlog, costing you valuable time and decreasing your chance to be successful in AB testing. If you recognize this situation, then this is the right course for you.
By taking this course you will learn:
HTML
CSS for website AB testing
JavaScript (jQuery) for website AB testing
Data tracking for website AB testing
Setting up 60 to 90% of your own A/B tests without the help of a developer
This helps you to set up and run many more A/B tests and greatly increase your success. The course is set up in a practical and engaging manner. Together, we will start with the absolute basics before building many different kinds of A/B tests on your own website, while we increase the complexity step by step. The course is designed for people with no coding skills, and for those that have some experience already. The learnings in this course are applicable to every testing tool on the market.
Coding for A/B testing course structure
Section 1: Introduction
Section 2: HTML
Section 3: CSS for AB testing
Section 4: Javascript (jQuery) for AB testing
Section 5: Data gathering for AB testing
Section 6: Quality assurance for AB testing
Section 7: Thank you
Of course, there are many web development courses out there. But there is one big difference: As a Conversion specialist, you don’t build websites, you chance them. This requires a completely different focus on coding while also making sure you obtain reliable and trustworthy data. Web development courses help you to build websites, this course helps you to build and run reliable and successful A/B tests.
The course is designed for anyone working on A/B testing. Conversion specialists, growth hackers, and even front-end developers can hugely benefit from this course.
Now let’s get started. Feel free to check out the free preview lectures, or start taking this course right now.
Who this course is for:
- Conversion specialists who want to run more A/B tests
- Anyone working on A/B testing
- Online marketeers, growth hackers and designers who want to learn how to code A/B tests
Azure Application Gateway Deep Dive
Sunday, February 27, 2022
Hands On! Step-by-Step guide to learn Azure Application Gateway in depth
- New
What you'll learn
- Azure Application Gateway
- Azure Web Application Firewall (WAF)
- Create an Azure Application Gateway
- Multi-Site Hosting
- URL Routing / Path-based Routing
- Redirection
- Rewriting Sets
- Internal Load Balancer
- Auto Scaling & Zone Redundancy
- SSL Termination
- End-to-End SSL Encryption
- Mutual Authentication
- SSL Policies
- Ingress Controller Add-on for AKS
- Web Application Firewall (WAF)
- Web Application Firewall (WAF) Policies
- Diagnostic Settings
- Metrics
- Alert Rules
- Log Analytics
- Health Probes
- Backend Health
- High Traffic Support
- Pricing & Tiers
- Cookie Affinity
- Connection Draining
- Custom Error Pages
- WebSockets
- Infrastructure Configuration
- Front-end IP
- Listeners
- Routing Rules
- HTTP Settings
- Backend Pools
Description
Welcome to Azure Application Gateway Deep Dive Course. This course will get you up to speed with Azure Application Gateway, and you'll learn the best practices to implement Azure Application Gateway. With a deep focus on routing & security, you'll get to learn how you can use different routing rules to direct traffic through your Application Gateway to the right backend target. Covering different SSL implementations (SSL Termination, End-to-End SSL Encryption) to secure your Application Gateway as needed. Getting into the details of AKS Ingress Controller and see how you can setup your Application Gateway to route traffic onto AKS cluster using two different deployment styles (Greenfield & Brownfield). You'll learn how to configure your Application Gateway as a Web Application Firewall (WAF) to add additional layer of protection for your web applications, and how to configure your own WAF policies. Then, coming to the monitoring part, where you'll see how to setup the Diagnostic Settings for your Application Gateway, and what are the key Metrics you need to focus on to ensure you've a health Application Gateway instance(s), and how to setup alert rules to notify you when a certain Metric has exceeded a certain threshold. Then moving into a more advanced topics on how to configure your Application Gateway so It would be able to support high traffic workloads coming through your systems. Happy Learning!
Who this course is for:
- Cloud Engineers
- Cloud Architects
- Software Engineers
- Developers
- Software Architects
- Network Engineers
- Security Engineers
- Network Archiects
- Security Architects
Complete PySpark Developer Course (Spark with Python)
Saturday, February 26, 2022
Complete PySpark Developer Course (Spark with Python) - Learn PySpark in depth with hundreds of Practical examples. Be a complete PySpark Developer. Set up a Hadoop Cluster.
- Bestseller
What you'll learn
- Complete Curriculum for a successful PySpark Developer
- Hadoop Single Node Cluster Set up and Integrate with Spark 2.x and Spark 3.x
- Complete Flow of Installation of PySpark (Windows and Unix)
- Detailed HDFS Course
- Python Crash Course
- Introduction to Spark
- Understand SparkSession
- Spark RDD Fundamentals, Operations, Persistence. Practical Examples to solve problems.
- Spark Cluster Architecture - Execution, YARN, JVM Processes, DAG Scheduler, Task Scheduler
- Spark Shared Variables
- Spark SQL Architecture, Catalyst Optimizer, Volcano Iterator Model, Tungsten Execution Engine
- DataFrame Fundamentals
- DataFrame Rows, Columns and DataTypes. Practical examples.
- ETL Using DataFrame (Extraction APIs, Transformation APIs, and Loading APIs). Practical Examples.
- Optimization and Management - Join Strategies, Driver Conf, Executor Conf etc
Description
This is a complete PySpark Developer course for Data Engineers and Data Scientists and others who wants to process Big Data in an effective manner. We will cover below topics and more:
Complete Curriculum for a successful PySpark Developer
Set up Hadoop Single Node Cluster and Integrate it with Spark 2.x and Spark 3.x
Complete Flow of Installation of Standalone PySpark (Unix and Windows Operating System)
Detailed HDFS Commands and Architecture.
Python Crash Course
Introduction to Spark (Why Spark was Developed, Spark Features, Spark Components)
Understand SparkSession
Spark RDD Fundamentals
How to Create RDDs
RDD Operations (Transformations & Actions)
Spark Cluster Architecture - Execution, YARN, JVM Processes, DAG Scheduler, Task Scheduler
RDD Persistence
Spark Shared Variables - Broadcast
Spark Shared Variables - Accumulators)
Spark SQL Architecture, Catalyst Optimizer, Volcano Iterator Model, Tungsten Execution Engine, Different Benchmarks
Difference between Catalyst Optimizer and Volcano Iterator Model
Spark Commonly Used Functions - Version, range, createDataFrame, sql, table, SparkContext, conf, read, udf, newSession, stop, catalog etc
DataFrame Built-in functions - new column functions, encryption functions, string functions, regexp functions, date functions, null functions, collection functions, na functions, math and statistics functions, explode functions, flatten functions, formatting and json functions
What is Partition,
What is Repartition
What is Coalesce
Repartition Vs Coalesce
Extraction - csv file, text file, Parquet File, orc file, json file, avro file, hive, jdbc
DataFrame Fundamentals
What is a DataFrame
DataFrame Sources
DataFrame Features
DataFrame Organization
DataFrame Rows,
DataFrame Columns
DataTypes. Practical examples.
Perform ETL Using DataFrame
-- Extraction APIs
-- Transformation APIs
-- Loading APIs
-- Practical Examples.
Optimization and Management - Join Strategies, Driver Conf, Parallelism Configurations, Executor Conf etc
Who this course is for:
- Any IT professional willing to learn advanced Big Data Technologies like PySpark.
- Python Developers who wants to learn Spark.
- Data Engineers and Data Scientists.
SQL for Healthcare
SQL for Healthcare
Let's Learn SQL and Healthcare Analytics Concepts Together
Hot & New
What you'll learn
- Using SQL to answer questions in healthcare
- Reading SQL
- Writing SQL
- Navigating a data enviornment
Description
SQL for Healthcare looks to provide students a single course to learn SQL and United States Healthcare concepts together. Through the use of MySQL (an open source and free tool) we will explore various use cases like patient volumes, emergency room throughput, and procedures to learn new SQL and healthcare analytics skills. I will be taking an approach of presenting healthcare topics briefly before diving into working examples using SQL. My intent is to help us apply technical concepts of the SQL language while at the same time learning new healthcare knowledge. Following each lecture, there will be an assignment for students to really apply and grow beyond what we cover over video content. I have found this approach really helps with long term retainment of skills. This course is great for anyone new to healthcare and/or SQL and is meant to help others jump start a career in healthcare analytics.
This course was created by me - Mark Connolly, MEng. I'm currently a Business Intelligence Lead and co-lead for the virtual Tableau Healthcare User Group. I have almost 10 years of experience in the healthcare analytics space in general. I have used SQL heavily in my most recent role to answer many healthcare clinical analytics questions.
Beyond my experience working directly with SQL, I have also developed internal Tableau training programs and provided 100's (if not 1000's) of hours of training support to analysts both at his current organization and externally. I am also the creator for the strongly reviewed Tableau for Healthcare course on Udemy.
Please note that this course utilizes training data and is not reflective of any current or past performance or clinical data for any patient, health system, insurance provider, or any other entity. All data is fake and made available for educational purposes only.
Who this course is for:
Beginner or Inspiring to be Healthcare Analysts
Facebook Marketing 2022. Promote Your Business on Facebook!
Friday, February 25, 2022
Facebook Marketing 2022. Promote Your Business on Facebook! - Learn how to promote your brand on Facebook, create engaging content, launch ads and increase the number of followers!
- Hot & New
What you'll learn
- Understand the Facebook algorithms
- Create Facebook personal and business Pages
- Create, set up, and optimize a Facebook page for a brand or community
- Create engaging content
- Use Facebook analytics tool and other social media analytics tools to monitor competitors advertising activities
- Set up Facebook Business Manager
- Activate Ads Manager
- Create a Facebook or Instagram ad on mobile and in Ads Manager
- Set up events with Facebook Pixel
- Select the right Audience for Facebook ads
- Make effective creatives for advertising
Requirements
- There are no requirements, but it is recommended to have an active online project to work on.
- Completing the practical tasks within the course increases its effectiveness!
Description
According to Hootsuite, 55% of the population uses social media actively, which equals 4,33 billion people. Nowadays Facebook is the biggest social media network that numbers more than 2 billion users. An average user spends 22 minutes on Facebook.
Thus the presence on Facebook is an integral part of the overall marketing plan of different businesses.
To help you build successful Facebook marketing we have created this Facebook Marketing course together with a renowned SMM expert and an instructor, Vlad Bogutskiy.
In this Facebook Marketing course you will learn how to:
Create Facebook personal and business Pages;
Set up and optimize a Facebook page for a brand or community;
Create engaging content;
Use the Facebook analytic tool and other social media analytics tools to monitor competitors advertising activities;
Work with Facebook Business Manager;
Activate Ads Manager;
Create a Facebook or Instagram ad on mobile and in Ads Manager;
Set up events with Facebook Pixel;
Work with Audiences in Ads Manager;
Make engaging creatives for effective advertising.
Why should you choose this course?
Why should you choose us over other online Facebook marketing courses?
You will have the opportunity to learn about Facebook Marketing from the top internet marketing professionals.
We are industry experts! WebPromoExperts Academy and Skillsbooster Academy have over 12 years of experience in internet marketing. Our digital agency, WebPromo, is a Google Premier Partner and a Facebook Marketing Partner.
During our career at the digital agency, we have launched over 1,500 successful marketing strategies and marketing campaigns.
More than 300 000 internet marketers have enrolled in our online courses both on Udemy and at offline and online WebPromoExperts Academy.
Our internet marketing courses are easy to understand. We train specialists for strategy, digital agency management, SMM, SEO, content marketing, PPC advertising, SERM, email marketing, web analytics, and other areas of digital marketing.
Upon completion of the course, you will receive a Facebook Marketing Certification from Udemy.
What else do you get?
lifetime access to the course and its updates
a certificate from Udemy upon completion of the course
Enroll now!
You have nothing to lose and everything to gain. This course comes with a 30-day money-back guarantee!
Want to start now? Click the "Buy now" button and learn how to use Facebook Marketing to promote your business!
Who this course is for:
- Everyone who wants to promote their projects on Facebook. You will learn how to implement Facebook marketing into your overall marketing strategy.
- Marketers - This course will expand your skills and teach you how to create a Facebook page, grow the number of followers, create effective creatives and launch ad campaigns. You will gain knowledge that is necessary to advance your career in SMM.
- Entrepreneurs - Entrepreneurs will be able to use their newly acquired skills in Facebook marketing to promote their business, choose the right ad objectives, create engaging content, set up effective ad campaigns and analyze results.
- SMM Specialists - This course will help you use Facebook marketing to grow your audience on Facebook. You will learn to work with Facebook Business Manager and Ads Manager and analyze results. After completing this course, you will bring more value to your company and advance your career.
Nest JS Advance Course
Wednesday, February 23, 2022
Nest JS Advance Course - Nest JS Advance Topics
Highest Rated
What you'll learn
- Becoming familiar with the NestJS framework and its components
- Designing and developing REST APIs performing CRUD operations
- Authentication and Authorization for back-end applications
- Using TypeORM for database interaction
- Security best practices, password hashing and storing sensitive information
- Persisting data using a database
- Deploying back-end applications at a production-ready state to Amazon Web Services
- Writing clean, maintainable code in-line with industry standards
- Utilising the NestJS Command Line Interface (CLI)
- Using Postman for testing back-end services
- Using pgAdmin as an interface tool to manage PostgreSQL databases
- Implement efficient logging in a back-end application
- Environment-based configuration management and environment variables
- Implementing data validation and using Pipes
- Guarding endpoints for authorized users using Guards
- Modelling entities for the persistence layer
- TypeScript best practices
- Handling asynchronous operations using async-await
- Using Data Transfer Objects (DTO)
- Hands-on experience with JSON Web Tokens (JWT)
- Unit testing NestJS applications
- Using GraphQL with NestJS
- Database persistence with MongoDB
Requirements
- Having a basic understanding of JavaScript and/or NodeJS
- Having basic knowledge of TypeScript is recommended, but not required
Description
NestJS is a Node.js back-end development framework built upon Express, leveraging the power of TypeScript.
NestJS leverages the incredible popularity and robustness of JavaScript as a language and Node.js as a technology. It is inspired by common libraries and frameworks such as Angular, React and Vue which improve developer productivity and experience.
Even considering the amount of superb libraries, helpers and tools that exist for server-side Node.js, none of them effectively solve the main problem - the architecture of an application.
NestJS provides an out-of-the-box application architecture which allows developers and teams to create highly testable, scalable, loosely coupled and easily maintainable applications.
Recently, the NestJS framework is gaining extreme popularity due to its incredible features;
Leverages TypeScript - strongly typed language which is a super-set of JavaScript
Simple to use, easy to learn and easy to master
Powerful Command Line Interface (CLI) tool that boosts productivity and ease of development
Detailed, well-maintained documentation
Active codebase development and maintenance
Open-source (MIT license)
Supports dozens nest-specific modules that help you easily integrate with common technologies and concepts such as TypeORM, Mongoose, GraphQL, Logging, Validation, Caching, Websockets and much more
Easy of unit-testing applications
Made for Monoliths and Micro-services (entire section in the documentation regarding the Microservice type of a NestJS application, as well as techniques and recipes).
In this course I am going to guide you through the process of planning, developing and deploying a fully-featured back-end application, based on my experience developing and maintaining systems that support dozens of millions of concurrent users at scale.
Who this course is for:
- Intermediate JavaScript developers who want to dive into back-end development
- Any developers willing to apply TypeScript on the back-end
- Developers eager to learn how to develop performant, secure and production-ready REST APIs following best practices
- Developers who want to learn how to deploy their application to the cloud (Amazon Web Services)
- Developers who want to follow building a practical, real-world application from zero to production
2 Real World Azure Data Engineer Project End to End
2 Real World Azure Data Engineer Project End to End - Engineering Project Building from scratch including designing, architecting, implementing solution and overall testing.
Highest Rated
What you'll learn
- You will learn how to Architect, Design and build a real-world enterprise level data platform solution including multiple services.
- You will learn design solution using ADF, Azure Function, Databricks, pyspark, Azure Data lake storage Gen 2 (ADLS), Azure SQL Server
- You will learn how to build a real-world data pipeline in Azure Data Factory (ADF). This course has been taught using 2 real world use case scenarios.
- You will learn how to transform data using Databricks Notebook Activity in Azure Data Factory (ADF) and load into Azure Data Lake Storage Gen2
- You will learn how to build production ready pipelines and good practices and naming standards
- You will learn how to integrate Databricks with ADF and send the response back from Databricks to ADF
- You will learn how to develop the triggered based Azure Function to validate files.
- You will learn how to create Azure Key vault and use it to store secret credentials and SAS token
- You will learn how to connect the Azure SQL Database and Databricks cluster using the Key Vault
- You will learn how to mount he Azure Storage Account in the Databricks to access the files and preform transformation on it.
- You will learn how to transform the data in the Azure Databricks using the pyspark.
Requirements
- Basic understanding about cloud computing will be useful, but not necessary.
- Experience in Azure is not required, I will take you through everything necessary to learn this course and build the project
Description
This course will help you in preparing and mastering your Azure Data engineering Concepts.
It is not like any random project like covid, or twitter analysis. These project is real world projects on which I personally worked and developed it for big clients.
Highlights of the Course:
Designed to keep only précised information no beating around the bush. (To save your time).
Real time implementation, no dummy use case.
Can be added as part of your resume.
It will help you to showcase your experience in interviews and discussion.
Involve complex architecture solution which is aligned with industry best practices.
Single projects involve various component integration like ADF, ADLS, Databricks, Azure SQL DB, Key Vault.
Solves the problem of real time experience for new Data engineers.
This course has been developed in mind to keep all the best practices followed in the Industry as an data engineering project and solution.
Technologies involved:
Azure Data Lake Storage Gen 2
Azure Data Factory
Data Factory Pipeline
Azure Functions
Azure Key Vault
Azure SQL DB
SSMS
AWS S3 Bucket
Connect ADF to Databricks
Connect Databricks to SQL Server
Connect Databricks to ADLS
Connect S3 to Azure Cloud
Triggers
SAS token
Create Secrets scope in Databricks
Store secretes in Key Vault and access them
What you will learn after this course:
How to think, design and develop the solution in the data engineering world.
How to create the architecture diagram for data engineering projects.
How to Create Azure Data Factory Account
How to Create Azure Data Lake Storage Gen 2 account.
How to Create Azure Databricks Workspace.
How to create S3 storage account.
How to create Azure Function.
How to implement logic in the Databricks notebook using pyspark.
How to connect ADF to Databricks.
How to chain the multiple pieces together in project.
How to create Azure SQL Server.
How to load the data from file to Azure SQL server.
How to connect Databricks notebook with Azure SQL Server.
How to Store secrets in the Azure Key Vault.
Who this course is for:
- Aspiring Data engineer who are searching for project to add in resume
- Someone who is looking for Real World uses cases to implement as Data engineering Solution
- University students looking for a career in Data Engineering
- IT developers working on other disciplines trying to move to Data Engineering
- Data Engineers/ Data Warehouse Developers currently working on on-premises technologies, or other cloud platforms such as AWS or GCP who want to learn Azure Technologies
- Data Architects looking to gain an understanding about Azure Data Engineering stack
- Data Scientists who want extend their knowledge into data engineering
SQL Mastery For Data Science
SQL Mastery For Data Science - Up Your Skills with SQL Tips and Tricks for Data Science
New
What you'll learn
- SQL for Data Science
- Learn practical applications of SQL queries for data analysis
- How to join tables and calculate rolling averages
- How to use window functions, aggregate and filter data
- Learn how to retrieve data
- Much more
Description
Gain the career-building SQL skills you need with this course. Through hands-on learning you’ll load, extract, and manipulate data from relational databases. Study at your own pace and grow your SQL skills.
In this course, we'll go over the most common data science and analytics questions that you'll receive, such as how to find the top products per category, how to find active employee counts by month, how to calculate rolling average of sales and much more.
We'll start by showing you how to retrieve data from a database using SQL Server and AdventureWorks, then show you how to aggregate, join, and filter your results to create context for your analysis.
We'll also get into answering more complex questions with ranking, moving averages, and window functions.
Learn how to retrieve data, join tables, calculate rolling averages and rankings, work with dates and times, use window functions, aggregate and filter data, and much more.
SQL is one of the most requested skills in Data Science. This course is great for anyone looking to build their skills and take it to the next level.
Learn to use Structured Query Language (SQL) to extract and analyze data stored in databases. You’ll first learn to extract data, join tables together, and perform aggregations. Then you’ll learn to do more complex analysis and manipulations using subqueries, and window functions.
By the end of the course, you’ll be able to write efficient SQL queries to successfully handle a variety of data analysis tasks.
Who this course is for:
- Programmers, Developers, DevOps, Data miners
ChatBots: Messenger ChatBot - DialogFlow and nodejs
Monday, February 21, 2022
Udemy Free Discount - ChatBots: Messenger ChatBot - DialogFlow and nodejs, Use DialogFlow to train chatbot to have dialogs. Develop backend app to connect chatbot to web services and databases
BESTSELLER, 4.3 (2,194 ratings), Created by Jana Bergant, English, Italian [Auto-generated], 3 more
PREVIEW THIS COURSE - GET COUPON CODE
Do you want to build a chatbot, so a bot that can talk? Yes, a bot that can talk to your friends or customers or fans while you sleep or do something else. You can make one for your customer that keep on asking the same questions. Or if you have a community for your fans and followers that want to know your details. Use your imagination, any time you have to reply the same thing over and over again, someone else like a bot can do it for you.
In the first part of the course, we'll make a chatbot without programming skills. We'll build a ChatBot that can answer frequently asked questions, and I'll show you how to teach your bot to have any other dialogs. We'll learn this by teaching our ChatBot to make job interviews.
We'll use DialogFlow to process natural language. DialogFlow will help the bot to understand what users want.
The chatbot will communicate to its customers via Facebook Messenger.
And in the second part, we'll use NodeJS to upgrade the bot. So the basic knowledge of javascript and NodeJS is needed.
With the new app, our bot will be able to remember things, that is, store information into a database or connect to other API services. With this, the bot will gain external knowledge and functionality.
And remember, I'LL BE THERE FOR YOU. I ANSWER EVERY QUESTION AND HELP EVERY STUDENT.
At the end of the course, you'll have a fully functional ChatBot. And you'll also know how to teach the bot to have other dialogs with customers. You'll know how to make a bot for yourself and other people.
My name is Jana Bergant, and I'm a developer with over 20 years of experience. I'm an IT instructor teaching people new tech skills. Over 17000 people are already taking my course.
I help all my students at every step of development. And I'll be here for you!
So if the predictions turn right, this will open up a new channel for businesses to reach a large audience. And here is a BIG OPPORTUNITY FOR YOU! Be one of the first people that know how to build chatbots. You can build it for your business or other people.
This course will show you how to create a ChatBot!
In the course, we use the latest version of DialogFlow. Also, I update videos regularly to stay up to date.
The last update of the course was on 15th November 2019
I added: REGEXP entities, an automated expansion for entities and fuzzy matching
100% Off Udemy Coupon . Free Udemy Courses . Online Classes
Deep Learning Computer Vision™ CNN, OpenCV, YOLO, SSD & GANs
Free Coupon Discount - Deep Learning Computer Vision™ CNN, OpenCV, YOLO, SSD & GANs
Go from Beginner to Expert using Deep Learning for Computer Vision (Keras, TF & Python) with 28 Real World Projects
4.3 (781 ratings), Created by Rajeev D. Ratan
REVIEW THIS COURSE - GET COUPON CODE
Description
Update: June-2020
TensorFlow 2.0 Compatible Code
Windows install guide for TensorFlow2.0 (with Keras), OpenCV4 and Dlib
Deep Learning Computer Vision™ Use Python & Keras to implement CNNs, YOLO, TFOD, R-CNNs, SSDs & GANs + A Free Introduction to OpenCV.
If you want to learn all the latest 2019 concepts in applying Deep Learning to Computer Vision, look no further - this is the course for you! You'll get hands the following Deep Learning frameworks in Python:
Keras
Tensorflow 2.0
TensorFlow Object Detection API
YOLO (DarkNet and DarkFlow)
OpenCV4
All in an easy to use virtual machine, with all libraries pre-installed!
======================================================
Apr 2019 Updates:
How to set up a Cloud GPU on PaperSpace and Train a CIFAR10 AlexNet CNN almost 100 times faster!
Build a Computer Vision API and Web App and host it on AWS using an EC2 Instance!
Mar 2019 Updates:
Newly added Facial Recognition & Credit Card Number Reader Projects
Recognize multiple persons using your webcam
Facial Recognition on the Friends TV Show Characters
Take a picture of a Credit Card, extract and identify the numbers on that card!
======================================================
Computer vision applications involving Deep Learning are booming!
Having Machines that can 'see' will change our world and revolutionize almost every industry out there. Machines or robots that can see will be able to:
Perform surgery and accurately analyze and diagnose you from medical scans.
Enable self-driving cars
Radically change robots allowing us to build robots that can cook, clean and assist us with almost any task
Understand what's being seen in CCTV surveillance videos thus performing security, traffic management and a host of other services
Create Art with amazing Neural Style Transfers and other innovative types of image generation
Simulate many tasks such as Aging faces, modifying live video feeds and realistically replace actors in films
Huge technology companies such as Facebook, Google, Microsoft, Apple, Amazon, and Tesla are all heavily devoting billions to computer vision research.
As a result, the demand for computer vision expertise is growing exponentially!
However, learning computer vision with Deep Learning is hard!
Tutorials are too technical and theoretical
Code is outdated
Beginners just don't know where to start
That's why I made this course!
I spent months developing a proper and complete learning path.
I teach all key concepts logically and without overloading you with mathematical theory while using the most up to date methods.
I created a FREE Virtual Machine with all Deep Learning Libraries (Keras, TensorFlow, OpenCV, TFODI, YOLO, Darkflow etc) installed! This will save you hours of painfully complicated installs
I teach using practical examples and you'll learn by doing 18 projects!
Projects such as:
Handwritten Digit Classification using MNIST
Image Classification using CIFAR10
Dogs vs Cats classifier
Flower Classifier using Flowers-17
Fashion Classifier using FNIST
Monkey Breed Classifier
Fruit Classifier
Simpsons Character Classifier
Using Pre-trained ImageNet Models to classify a 1000 object classes
Age, Gender and Emotion Classification
Finding the Nuclei in Medical Scans using U-Net
Object Detection using a ResNet50 SSD Model built using TensorFlow Object Detection
Object Detection with YOLO V3
A Custom YOLO Object Detector that Detects London Underground Tube Signs
DeepDream
Neural Style Transfers
GANs - Generate Fake Digits
GANs - Age Faces up to 60+ using Age-cGAN
Face Recognition
Credit Card Digit Reader
Using Cloud GPUs on PaperSpace
Build a Computer Vision API and Web App and host it on AWS using an EC2 Instance!
And OpenCV Projects such as:
Live Sketch
Identifying Shapes
Counting Circles and Ellipses
Finding Waldo
Single Object Detectors using OpenCV
Car and Pedestrian Detector using Cascade Classifiers
So if you want to get an excellent foundation in Computer Vision, look no further.
This is the course for you!
In this course, you will discover the power of Computer Vision in Python, and obtain skills to dramatically increase your career prospects as a Computer Vision developer.
======================================================
As for Updates and support:
I will be active daily in the 'questions and answers' area of the course, so you are never on your own.
So, are you ready to get started? Enroll now and start the process of becoming a Master in Computer Vision using Deep Learning today!
======================================================
What previous students have said my other Udemy Course:
"I'm amazed at the possibilities. Very educational, learning more than what I ever thought was possible. Now, being able to actually use it in a practical purpose is intriguing... much more to learn & apply"
"Extremely well taught and informative Computer Vision course! I've trawled the web looking for OpenCV python tutorials resources but this course was by far the best amalgamation of relevant lessons and projects. Loved some of the projects and had lots of fun tinkering them."
"Awesome instructor and course. The explanations are really easy to understand and the materials are very easy to follow. Definitely a really good introduction to image processing."
"I am extremely impressed by this course!! I think this is by far the best Computer Vision course on Udemy. I'm a college student who had previously taken a Computer Vision course in undergrad. This 6.5 hour course blows away my college class by miles!!"
"Rajeev did a great job on this course. I had no idea how computer vision worked and now have a good foundation of concepts and knowledge of practical applications. Rajeev is clear and concise which helps make a complicated subject easy to comprehend for anyone wanting to start building applications."
======================================================
Who this course is for:
Programmers, college students or anyone enthusiastic about computer vision and deep learning
Those wanting to be on the forefront of the job market for the AI Revolution
Those who have an amazing startup or App idea involving computer vision
Enthusiastic hobbyists wanting to build fun Computer Vision applications
100% Off Udemy Coupon . Free Udemy Courses . Online Classes
Update: June-2020
TensorFlow 2.0 Compatible Code
Windows install guide for TensorFlow2.0 (with Keras), OpenCV4 and Dlib
Deep Learning Computer Vision™ Use Python & Keras to implement CNNs, YOLO, TFOD, R-CNNs, SSDs & GANs + A Free Introduction to OpenCV.
If you want to learn all the latest 2019 concepts in applying Deep Learning to Computer Vision, look no further - this is the course for you! You'll get hands the following Deep Learning frameworks in Python:
Keras
Tensorflow 2.0
TensorFlow Object Detection API
YOLO (DarkNet and DarkFlow)
OpenCV4
All in an easy to use virtual machine, with all libraries pre-installed!
======================================================
Apr 2019 Updates:
How to set up a Cloud GPU on PaperSpace and Train a CIFAR10 AlexNet CNN almost 100 times faster!
Build a Computer Vision API and Web App and host it on AWS using an EC2 Instance!
Mar 2019 Updates:
Newly added Facial Recognition & Credit Card Number Reader Projects
Recognize multiple persons using your webcam
Facial Recognition on the Friends TV Show Characters
Take a picture of a Credit Card, extract and identify the numbers on that card!
======================================================
Computer vision applications involving Deep Learning are booming!
Having Machines that can 'see' will change our world and revolutionize almost every industry out there. Machines or robots that can see will be able to:
Perform surgery and accurately analyze and diagnose you from medical scans.
Enable self-driving cars
Radically change robots allowing us to build robots that can cook, clean and assist us with almost any task
Understand what's being seen in CCTV surveillance videos thus performing security, traffic management and a host of other services
Create Art with amazing Neural Style Transfers and other innovative types of image generation
Simulate many tasks such as Aging faces, modifying live video feeds and realistically replace actors in films
Huge technology companies such as Facebook, Google, Microsoft, Apple, Amazon, and Tesla are all heavily devoting billions to computer vision research.
As a result, the demand for computer vision expertise is growing exponentially!
However, learning computer vision with Deep Learning is hard!
Tutorials are too technical and theoretical
Code is outdated
Beginners just don't know where to start
That's why I made this course!
I spent months developing a proper and complete learning path.
I teach all key concepts logically and without overloading you with mathematical theory while using the most up to date methods.
I created a FREE Virtual Machine with all Deep Learning Libraries (Keras, TensorFlow, OpenCV, TFODI, YOLO, Darkflow etc) installed! This will save you hours of painfully complicated installs
I teach using practical examples and you'll learn by doing 18 projects!
Projects such as:
Handwritten Digit Classification using MNIST
Image Classification using CIFAR10
Dogs vs Cats classifier
Flower Classifier using Flowers-17
Fashion Classifier using FNIST
Monkey Breed Classifier
Fruit Classifier
Simpsons Character Classifier
Using Pre-trained ImageNet Models to classify a 1000 object classes
Age, Gender and Emotion Classification
Finding the Nuclei in Medical Scans using U-Net
Object Detection using a ResNet50 SSD Model built using TensorFlow Object Detection
Object Detection with YOLO V3
A Custom YOLO Object Detector that Detects London Underground Tube Signs
DeepDream
Neural Style Transfers
GANs - Generate Fake Digits
GANs - Age Faces up to 60+ using Age-cGAN
Face Recognition
Credit Card Digit Reader
Using Cloud GPUs on PaperSpace
Build a Computer Vision API and Web App and host it on AWS using an EC2 Instance!
And OpenCV Projects such as:
Live Sketch
Identifying Shapes
Counting Circles and Ellipses
Finding Waldo
Single Object Detectors using OpenCV
Car and Pedestrian Detector using Cascade Classifiers
So if you want to get an excellent foundation in Computer Vision, look no further.
This is the course for you!
In this course, you will discover the power of Computer Vision in Python, and obtain skills to dramatically increase your career prospects as a Computer Vision developer.
======================================================
As for Updates and support:
I will be active daily in the 'questions and answers' area of the course, so you are never on your own.
So, are you ready to get started? Enroll now and start the process of becoming a Master in Computer Vision using Deep Learning today!
======================================================
What previous students have said my other Udemy Course:
"I'm amazed at the possibilities. Very educational, learning more than what I ever thought was possible. Now, being able to actually use it in a practical purpose is intriguing... much more to learn & apply"
"Extremely well taught and informative Computer Vision course! I've trawled the web looking for OpenCV python tutorials resources but this course was by far the best amalgamation of relevant lessons and projects. Loved some of the projects and had lots of fun tinkering them."
"Awesome instructor and course. The explanations are really easy to understand and the materials are very easy to follow. Definitely a really good introduction to image processing."
"I am extremely impressed by this course!! I think this is by far the best Computer Vision course on Udemy. I'm a college student who had previously taken a Computer Vision course in undergrad. This 6.5 hour course blows away my college class by miles!!"
"Rajeev did a great job on this course. I had no idea how computer vision worked and now have a good foundation of concepts and knowledge of practical applications. Rajeev is clear and concise which helps make a complicated subject easy to comprehend for anyone wanting to start building applications."
======================================================
Who this course is for:
Programmers, college students or anyone enthusiastic about computer vision and deep learning
Those wanting to be on the forefront of the job market for the AI Revolution
Those who have an amazing startup or App idea involving computer vision
Enthusiastic hobbyists wanting to build fun Computer Vision applications
100% Off Udemy Coupon . Free Udemy Courses . Online Classes
WhatsApp Automation - Become a WhatsApp Genius (2022)
WhatsApp Automation - Become a WhatsApp Genius (2022) - Learn to automate WhatsApp and start building multi-step chatbots in just 2 hours!
What you'll learn
- Basics of WhatsApp Automation
- Create a Basic WhatsApp Automation Bot
- Create a Multi-step Advanced Automation WhatsApp Bot
- Send Photos/Videos/Files (Automatically) as a Response in WhatsApp
- Store WhatsApp Messages in a Database
- Use Twilio and MongoDB to Automate WhatsApp
- Deploy Bots to a Remote server and Host it in the Cloud for FREE!
- Use Your Own WhatsApp Number as a Chatbot
Requirements
- Basic IT skills
- Basics of any programming language
Description
Welcome to this course on WhatsApp automation. In this course, you will learn how to send messages or media automatically in WhatsApp without any human intervention. You will also be able to create conversational chatbots through this course. Considering WhatsApp has billions of users worldwide, it can also be used for business or personal automation. So if you work for any business, or you own a business, you can use this skill to automate a lot of your customer service, like giving out information about your business or about what items are available or even taking orders. You can also use it on a personal scale like sending reminders or tracking your habits or anything you want to achieve.
First, you will learn the basics of automation and how to create a basic WhatsApp automation bot using Python and Twilio. Apart from sending messages, you will also learn how to store messages in a database called MongoDB. After learning the basics you will learn to create a relatively advanced multi-step WhatsApp chat bot. Take a look at the Demo video to see what the bot is capable of. Once you build the bot, you will learn how to deploy it to a remote server and host it in the cloud.
Data Structures & Algorithms - Python
Sunday, February 20, 2022
Free Udemy Coupon
Data Structures & Algorithms - Python - The Ultimate Python Coding Interview Bootcamp
- Hot & New
- Created by Scott Barrett
- English [Auto]
What you'll learn
- Mastery of Data Structures and Algorithms
- Confidently answer technical interview questions
- Time and Space Complexity of Data Structures and Algorithms
- Strengthen your skills as a developer
Description
This course is different…
After each line of code, an animation of the data structure or algorithm is updated to show exactly what that line of code did.
The animations provide some huge advantages to students:
Increased understanding of the concepts
Greater rate of retention
The material can be covered in a fraction of the time
That means that you can actually learn more material in less time and have higher retention of the material.
That is the key combination of factors to prepare you for the technical interview that lands you your dream job!
I invite you to watch a few of the videos in this course to see what I mean. The difference will be noticeable right away!
I spent over a year to create this course with the goal that an absolute beginner can take it and understand all of the concepts the first time through.
What you will get in this course…
Over 100 hand crafted HD videos that use animations to illustrate technical concepts.
Here is what you will learn in this course:
Technical
Big O notation
Data Structures
Lists
Linked Lists
Doubly Linked Lists
Stacks & Queues
Binary Trees
Hash Tables
Graphs
Algorithms
Sorting
Bubble Sort
Selection Sort
Insertion Sort
Merge Sort
Quick Sort
Searching
Breadth First Search
Depth First Search
I am excited to help you move forward with your coding and career goals.
Let's get started!
Who this course is for:
- Python programmers preparing for an interview
- University students taking a data structures and algorithms course
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Algorithms and Data Structures in Python (INTERVIEW Q&A)
Friday, February 18, 2022
Free Udemy Coupon - Algorithms and Data Structures in Python (INTERVIEW Q&A), A guide to implement data structures, graph algorithms and sorting algorithms from scratch with interview questions!
- Bestseller
- Created by Holczer Balazs
- English [Auto], Polish [Auto]
Online Courses Udemy GET COUPON
What you'll learn
- Understand arrays and linked lists
- Understand stacks and queues
- Understand tree like data structures (binary search trees)
- Understand balances trees (AVL trees and red-black trees)
- Understand heap data structures
- Understand hashing, hash tables and dictionaries
- Understand the differences between data structures and abstract data types
- Understand graph traversing (BFS and DFS)
- Understand shortest path algorithms such as Dijkstra's approach or Bellman-Ford method
- Understand minimum spanning trees (Prims's algorithm)
- Understand sorting algorithms
- Be able to develop your own algorithms
- Have a good grasp of algorithmic thinking
- Be able to detect and correct inefficient code snippets
Description
This course is about data structures, algorithms and graphs. We are going to implement the problems in Python programming language. I highly recommend typing out these data structures and algorithms several times on your own in order to get a good grasp of it.
So what are you going to learn in this course?
Section 1:
setting up the environment
differences between data structures and abstract data types
Section 2 - Arrays:
what is an array data structure
arrays related interview questions
Section 3 - Linked Lists:
linked list data structure and its implementation
doubly linked lists
linked lists related interview questions
Section 4 - Stacks and Queues:
stacks and queues
stack memory and heap memory
how the stack memory works exactly?
stacks and queues related interview questions
Section 5 - Binary Search Trees:
what are binary search trees
practical applications of binary search trees
problems with binary trees
Section 6 - Balanced Binary Trees (AVL Trees and Red-Black Trees):
why to use balanced binary search trees
AVL trees
red-black trees
Section 7 - Priority Queues and Heaps:
what are priority queues
what are heaps
heapsort algorithm overview
Section 8 - Hashing and Dictionaries:
associative arrays and dictionaries
how to achieve O(1) constant running time with hashing
Section 9 - Graph Traversal:
basic graph algorithms
breadth-first
depth-first search
stack memory visualization for DFS
Section 10 - Shortest Path problems (Dijkstra's and Bellman-Ford Algorithms):
shortest path algorithms
Dijkstra's algorithm
Bellman-Ford algorithm
how to detect arbitrage opportunities on the FOREX?
Section 11 - Spanning Trees (Kruskal's and Prim's Approaches):
what are spanning trees
what is the union-find data structure and how to use it
Kruskal's algorithm theory and implementation as well
Prim's algorithm
Section 12 - Sorting Algorithms
sorting algorithms
bubble sort, selection sort and insertion sort
quicksort and merge sort
non-comparison based sorting algorithms
counting sort and radix sort
In the first part of the course we are going to learn about basic data structures such as linked lists, stacks, queues, binary search trees, heaps and some advanced ones such as AVL trees and red-black trees.. The second part will be about graph algorithms such as spanning trees, shortest path algorithms and graph traversing. We will try to optimize each data structure as much as possible.
In each chapter I am going to talk about the theoretical background of each algorithm or data structure, then we are going to write the code step by step in Python.
Most of the advanced algorithms relies heavily on these topics so it is definitely worth understanding the basics. These principles can be used in several fields: in investment banking, artificial intelligence or electronic trading algorithms on the stock market. Research institutes use Python as a programming language in the main: there are a lot of library available for the public from machine learning to complex networks.
Thanks for joining the course, let's get started!
Who this course is for:
Beginner Python developers curious about graphs, algorithms and data structures
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Deep Learning: Advanced NLP and RNNs
Tuesday, February 15, 2022
Free Coupon Discount - Deep Learning: Advanced NLP and RNNs, Natural Language Processing with Sequence-to-sequence (seq2seq), Attention, CNNs, RNNs, and Memory Networks! | Created by Lazy Programmer Inc.
Students also bought
- Data Science: Deep Learning in Python
- Deep Learning Prerequisites: Logistic Regression in Python
- The Complete Neural Networks Bootcamp: Theory, Applications
- TensorFlow 2.0 Practical Advanced
- Machine Learning and AI: Support Vector Machines in Python
Preview this Udemy Course GET COUPON CODE
Description
It’s hard to believe it's been been over a year since I released my first course on Deep Learning with NLP (natural language processing).
A lot of cool stuff has happened since then, and I've been deep in the trenches learning, researching, and accumulating the best and most useful ideas to bring them back to you.
So what is this course all about, and how have things changed since then?
In previous courses, you learned about some of the fundamental building blocks of Deep NLP. We looked at RNNs (recurrent neural networks), CNNs (convolutional neural networks), and word embedding algorithms such as word2vec and GloVe.
This course takes you to a higher systems level of thinking.
Since you know how these things work, it’s time to build systems using these components.
At the end of this course, you'll be able to build applications for problems like:
text classification (examples are sentiment analysis and spam detection)
neural machine translation
question answering
We'll take a brief look chatbots and as you’ll learn in this course, this problem is actually no different from machine translation and question answering.
To solve these problems, we’re going to look at some advanced Deep NLP techniques, such as:
bidirectional RNNs
seq2seq (sequence-to-sequence)
attention
memory networks
All of the materials of this course can be downloaded and installed for FREE. We will do most of our work in Python libraries such as Keras, Numpy, Tensorflow, and Matpotlib to make things super easy and focus on the high-level concepts. I am always available to answer your questions and help you along your data science journey.
This course focuses on "how to build and understand", not just "how to use". Anyone can learn to use an API in 15 minutes after reading some documentation. It's not about "remembering facts", it's about "seeing for yourself" via experimentation. It will teach you how to visualize what's happening in the model internally. If you want more than just a superficial look at machine learning models, this course is for you.
See you in class!
Suggested Prerequisites:
Decent Python coding skills
Understand RNNs, CNNs, and word embeddings
Know how to build, train, and evaluate a neural network in Keras
TIPS (for getting through the course):
Watch it at 2x.
Take handwritten notes. This will drastically increase your ability to retain the information.
Write down the equations. If you don't, I guarantee it will just look like gibberish.
Ask lots of questions on the discussion board. The more the better!
The best exercises will take you days or weeks to complete.
Write code yourself, don't just sit there and look at my code. This is not a philosophy course!
Who this course is for:
Students in machine learning, deep learning, artificial intelligence, and data science
Professionals in machine learning, deep learning, artificial intelligence, and data science
Anyone interested in state-of-the-art natural language processing
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Complete Machine Learning & Data Science with Python | A-Z
Monday, February 14, 2022
Free Udemy Coupon
Complete Machine Learning & Data Science with Python | A-Z - Use Scikit, learn NumPy, Pandas, Matplotlib, Seaborn and dive into machine learning A-Z with Python and Data Science.
- Created by Oak Academy
- English [Auto]
Online Courses Udemy GET COUPON CODE
What you'll learn
- Machine learning isn’t just useful for predictive texting or smartphone voice recognition. Machine learning is constantly being applied to new industries.
- Learn Machine Learning with Hands-On Examples
- What is Machine Learning?
- Machine Learning Terminology
- Evaluation Metrics
- What are Classification vs Regression?
- Evaluating Performance-Classification Error Metrics
- Evaluating Performance-Regression Error Metrics
- Supervised Learning
- Cross Validation and Bias Variance Trade-Off
- Use matplotlib and seaborn for data visualizations
- Machine Learning with SciKit Learn
- Linear Regression Algorithm
- Logistic Regresion Algorithm
- K Nearest Neighbors Algorithm
- Decision Trees And Random Forest Algorithm
- Support Vector Machine Algorithm
- Unsupervised Learning
- K Means Clustering Algorithm
- Hierarchical Clustering Algorithm
- Principal Component Analysis (PCA)
- Recommender System Algorithm
- Python instructors on OAK Academy specialize in everything from software development to data analysis, and are known for their effective.
- Python is a general-purpose, object-oriented, high-level programming language.
- Python is a multi-paradigm language, which means that it supports many programming approaches. Along with procedural and functional programming styles
- Python is a widely used, general-purpose programming language, but it has some limitations. Because Python is an interpreted, dynamically typed language
- Python is a general programming language used widely across many industries and platforms. One common use of Python is scripting, which means automating tasks.
- Python is a popular language that is used across many industries and in many programming disciplines. DevOps engineers use Python to script website.
- Python has a simple syntax that makes it an excellent programming language for a beginner to learn. To learn Python on your own, you first must become familiar
- Machine learning describes systems that make predictions using a model trained on real-world data.
- Machine learning is being applied to virtually every field today. That includes medical diagnoses, facial recognition, weather forecasts, image processing.
- It's possible to use machine learning without coding, but building new systems generally requires code.
- Python is the most used language in machine learning. Engineers writing machine learning systems often use Jupyter Notebooks and Python together.
- Machine learning is generally divided between supervised machine learning and unsupervised machine learning. In supervised machine learning.
- Machine learning is one of the fastest-growing and popular computer science careers today. Constantly growing and evolving.
- Machine learning is a smaller subset of the broader spectrum of artificial intelligence. While artificial intelligence describes any "intelligent machine"
- A machine learning engineer will need to be an extremely competent programmer with in-depth knowledge of computer science, mathematics, data science.
Description
Hello there,
Welcome to the “Complete Machine Learning & Data Science with Python | A-Z” course.
Use Scikit, learn NumPy, Pandas, Matplotlib, Seaborn and dive into machine learning A-Z with Python and Data Science.
Machine learning isn’t just useful for predictive texting or smartphone voice recognition. Machine learning is constantly being applied to new industries and new problems. Whether you’re a marketer, video game designer, or programmer, my course on OAK Academy here to help you apply machine learning to your work.
It’s hard to imagine our lives without machine learning. Predictive texting, email filtering, and virtual personal assistants like Amazon’s Alexa and the iPhone’s Siri, are all technologies that function based on machine learning algorithms and mathematical models.
Python instructors on OAK Academy specialize in everything from software development to data analysis, and are known for their effective, friendly instruction for students of all levels.
Whether you work in machine learning or finance, or are pursuing a career in web development or data science, Python is one of the most important skills you can learn. Python's simple syntax is especially suited for desktop, web, and business applications. Python's design philosophy emphasizes readability and usability. Python was developed upon the premise that there should be only one way (and preferably one obvious way) to do things, a philosophy that has resulted in a strict level of code standardization. The core programming language is quite small and the standard library is also large. In fact, Python's large library is one of its greatest benefits, providing a variety of different tools for programmers suited for many different tasks.
Do you know data science needs will create 11.5 million job openings by 2026?
Do you know the average salary is $100.000 for data science careers!
Data Science Careers Are Shaping The Future
Data science experts are needed in almost every field, from government security to dating apps. Millions of businesses and government departments rely on big data to succeed and better serve their customers. So data science careers are in high demand.
If you want to learn one of the employer’s most request skills?
If you are curious about Data Science and looking to start your self-learning journey into the world of data with Python?
If you are an experienced developer and looking for a landing in Data Science!
In all cases, you are at the right place!
We've designed for you “Complete Machine Learning & Data Science with Python | A-Z” a straightforward course for Python Programming Language and Machine Learning.
In the course, you will have down-to-earth way explanations with projects. With this course, you will learn machine learning step-by-step. I made it simple and easy with exercises, challenges, and lots of real-life examples.
We will open the door of the Data Science and Machine Learning a-z world and will move deeper. You will learn the fundamentals of Machine Learning A-Z and its beautiful libraries such as Scikit Learn.
Throughout the course, we will teach you how to use Python to analyze data, create beautiful visualizations, and use powerful machine learning python algorithms.
This Machine Learning course is for everyone!
My "Machine Learning with Hands-On Examples in Data Science" is for everyone! If you don’t have any previous experience, not a problem! This course is expertly designed to teach everyone from complete beginners, right through to professionals ( as a refresher).
Why we use a Python programming language in Machine learning?
Python is a general-purpose, high-level, and multi-purpose programming language. The best thing about Python is, it supports a lot of today’s technology including vast libraries for Twitter, data mining, scientific calculations, designing, back-end server for websites, engineering simulations, artificial learning, augmented reality and what not! Also, it supports all kinds of App development.
What you will learn?
In this course, we will start from the very beginning and go all the way to the end of "Machine Learning" with examples.
Before each lesson, there will be a theory part. After learning the theory parts, we will reinforce the subject with practical examples.
During the course you will learn the following topics:
What is Machine Learning?
More About Machine Learning
Machine Learning Terminology
Evaluation Metrics
What is Classification vs Regression?
Evaluating Performance-Classification Error Metrics
Evaluating Performance-Regression Error Metrics
Machine Learning with Python
Supervised Learning
Cross-Validation and Bias Variance Trade-Off
Use Matplotlib and seaborn for data visualizations
Machine Learning with SciKit Learn
Linear Regression Theory
Logistic Regression Theory
Logistic Regression with Python
K Nearest Neighbors Algorithm Theory
K Nearest Neighbors Algorithm With Python
K Nearest Neighbors Algorithm Project Overview
K Nearest Neighbors Algorithm Project Solutions
Decision Trees And Random Forest Algorithm Theory
Decision Trees And Random Forest Algorithm With Python
Decision Trees And Random Forest Algorithm Project Overview
Decision Trees And Random Forest Algorithm Project Solutions
Support Vector Machines Algorithm Theory
Support Vector Machines Algorithm With Python
Support Vector Machines Algorithm Project Overview
Support Vector Machines Algorithm Project Solutions
Unsupervised Learning Overview
K Means Clustering Algorithm Theory
K Means Clustering Algorithm With Python
K Means Clustering Algorithm Project Overview
K Means Clustering Algorithm Project Solutions
Hierarchical Clustering Algorithm Theory
Hierarchical Clustering Algorithm With Python
Principal Component Analysis (PCA) Theory
Principal Component Analysis (PCA) With Python
Recommender System Algorithm Theory
Recommender System Algorithm With Python
With my up-to-date course, you will have a chance to keep yourself up-to-date and equip yourself with a range of Python programming skills. I am also happy to tell you that I will be constantly available to support your learning and answer questions.
What is machine learning?
Machine learning describes systems that make predictions using a model trained on real-world data. For example, let's say we want to build a system that can identify if a cat is in a picture. We first assemble many pictures to train our machine learning model. During this training phase, we feed pictures into the model, along with information around whether they contain a cat. While training, the model learns patterns in the images that are the most closely associated with cats. This model can then use the patterns learned during training to predict whether the new images that it's fed contain a cat. In this particular example, we might use a neural network to learn these patterns, but machine learning can be much simpler than that. Even fitting a line to a set of observed data points, and using that line to make new predictions, counts as a machine learning model.
What is machine learning used for?
Machine learning a-z is being applied to virtually every field today. That includes medical diagnoses, facial recognition, weather forecasts, image processing, and more. In any situation in which pattern recognition, prediction, and analysis are critical, machine learning can be of use. Machine learning is often a disruptive technology when applied to new industries and niches. Machine learning engineers can find new ways to apply machine learning technology to optimize and automate existing processes. With the right data, you can use machine learning technology to identify extremely complex patterns and yield highly accurate predictions.
Does Machine learning require coding?
It's possible to use machine learning data science without coding, but building new systems generally requires code. For example, Amazon’s Rekognition service allows you to upload an image via a web browser, which then identifies objects in the image. This uses a pre-trained model, with no coding required. However, developing machine learning systems involves writing some Python code to train, tune, and deploy your models. It's hard to avoid writing code to pre-process the data feeding into your model. Most of the work done by a machine learning practitioner involves cleaning the data used to train the machine. They also perform “feature engineering” to find what data to use and how to prepare it for use in a machine learning model. Tools like AutoML and SageMaker automate the tuning of models. Often only a few lines of code can train a model and make predictions from it
What is the best language for machine learning?
Python is the most used language in machine learning using python. Engineers writing machine learning systems often use Jupyter Notebooks and Python together. Jupyter Notebooks is a web application that allows experimentation by creating and sharing documents that contain live code, equations, and more. Machine learning involves trial and error to see which hyperparameters and feature engineering choices work best. It's useful to have a development environment such as Python so that you don't need to compile and package code before running it each time. Python is not the only language choice for machine learning. Tensorflow is a popular framework for developing neural networks and offers a C++ API. There is a complete machine learning framework for C# called ML. NET. Scala or Java are sometimes used with Apache Spark to build machine learning systems that ingest massive data sets.
What are the different types of machine learning?
Machine learning is generally divided between supervised machine learning and unsupervised machine learning. In supervised machine learning, we train machine learning models on labeled data. For example, an algorithm meant to detect spam might ingest thousands of email addresses labeled 'spam' or 'not spam.' That trained model could then identify new spam emails even from data it's never seen. In unsupervised learning, a machine learning model looks for patterns in unstructured data. One type of unsupervised learning is clustering. In this example, a model could identify similar movies by studying their scripts or cast, then group the movies together into genres. This unsupervised model was not trained to know which genre a movie belongs to. Rather, it learned the genres by studying the attributes of the movies themselves. There are many techniques available within.
Is Machine learning a good career?
Machine learning python is one of the fastest-growing and popular computer science careers today. Constantly growing and evolving, you can apply machine learning to a variety of industries, from shipping and fulfillment to medical sciences. Machine learning engineers work to create artificial intelligence that can better identify patterns and solve problems. The machine learning discipline frequently deals with cutting-edge, disruptive technologies. However, because it has become a popular career choice, it can also be competitive. Aspiring machine learning engineers can differentiate themselves from the competition through certifications, boot camps, code repository submissions, and hands-on experience.
What is the difference between machine learning and artifical intelligence?
Machine learning is a smaller subset of the broader spectrum of artificial intelligence. While artificial intelligence describes any "intelligent machine" that can derive information and make decisions, machine learning describes a method by which it can do so. Through machine learning, applications can derive knowledge without the user explicitly giving out the information. This is one of the first and early steps toward "true artificial intelligence" and is extremely useful for numerous practical applications. In machine learning applications, an AI is fed sets of information. It learns from these sets of information about what to expect and what to predict. But it still has limitations. A machine learning engineer must ensure that the AI is fed the right information and can use its logic to analyze that information correctly.
What skills should a machine learning engineer know?
A python machine learning engineer will need to be an extremely competent programmer with in-depth knowledge of computer science, mathematics, data science, and artificial intelligence theory. Machine learning engineers must be able to dig deep into complex applications and their programming. As with other disciplines, there are entry-level machine learning engineers and machine learning engineers with high-level expertise. Python and R are two of the most popular languages within the machine learning field.
What is python?
Machine learning python is a general-purpose, object-oriented, high-level programming language. Whether you work in artificial intelligence or finance or are pursuing a career in web development or data science, Python bootcamp is one of the most important skills you can learn. Python's simple syntax is especially suited for desktop, web, and business applications. Python's design philosophy emphasizes readability and usability. Python was developed on the premise that there should be only one way (and preferably, one obvious way) to do things, a philosophy that resulted in a strict level of code standardization. The core programming language is quite small and the standard library is also large. In fact, Python's large library is one of its greatest benefits, providing different tools for programmers suited for a variety of tasks.
Python vs. R: What is the Difference?
Python and R are two of today's most popular programming tools. When deciding between Python and R in data science , you need to think about your specific needs. On one hand, Python is relatively easy for beginners to learn, is applicable across many disciplines, has a strict syntax that will help you become a better coder, and is fast to process large datasets. On the other hand, R has over 10,000 packages for data manipulation, is capable of easily making publication-quality graphics, boasts superior capability for statistical modeling, and is more widely used in academia, healthcare, and finance.
What does it mean that Python is object-oriented?
Python is a multi-paradigm language, which means that it supports many data analysis programming approaches. Along with procedural and functional programming styles, Python also supports the object-oriented style of programming. In object-oriented programming, a developer completes a programming project by creating Python objects in code that represent objects in the actual world. These objects can contain both the data and functionality of the real-world object. To generate an object in Python you need a class. You can think of a class as a template. You create the template once, and then use the template to create as many objects as you need. Python classes have attributes to represent data and methods that add functionality. A class representing a car may have attributes like color, speed, and seats and methods like driving, steering, and stopping.
What are the limitations of Python?
Python is a widely used, general-purpose programming language, but it has some limitations. Because Python in machine learning is an interpreted, dynamically typed language, it is slow compared to a compiled, statically typed language like C. Therefore, Python is useful when speed is not that important. Python's dynamic type system also makes it use more memory than some other programming languages, so it is not suited to memory-intensive applications. The Python virtual engine that runs Python code runs single-threaded, making concurrency another limitation of the programming language. Though Python is popular for some types of game development, its higher memory and CPU usage limits its usage for high-quality 3D game development. That being said, computer hardware is getting better and better, and the speed and memory limitations of Python are getting less and less relevant.
How is Python used?
Python is a general programming language used widely across many industries and platforms. One common use of Python is scripting, which means automating tasks in the background. Many of the scripts that ship with Linux operating systems are Python scripts. Python is also a popular language for machine learning, data analytics, data visualization, and data science because its simple syntax makes it easy to quickly build real applications. You can use Python to create desktop applications. Many developers use it to write Linux desktop applications, and it is also an excellent choice for web and game development. Python web frameworks like Flask and Django are a popular choice for developing web applications. Recently, Python is also being used as a language for mobile development via the Kivy third-party library.
What jobs use Python?
Python is a popular language that is used across many industries and in many programming disciplines. DevOps engineers use Python to script website and server deployments. Web developers use Python to build web applications, usually with one of Python's popular web frameworks like Flask or Django. Data scientists and data analysts use Python to build machine learning models, generate data visualizations, and analyze big data. Financial advisors and quants (quantitative analysts) use Python to predict the market and manage money. Data journalists use Python to sort through information and create stories. Machine learning engineers use Python to develop neural networks and artificial intelligent systems.
How do I learn Python on my own?
Python has a simple syntax that makes it an excellent programming language for a beginner to learn. To learn Python on your own, you first must become familiar with the syntax. But you only need to know a little bit about Python syntax to get started writing real code; you will pick up the rest as you go. Depending on the purpose of using it, you can then find a good Python tutorial, book, or course that will teach you the programming language by building a complete application that fits your goals. If you want to develop games, then learn Python game development. If you're going to build web applications, you can find many courses that can teach you that, too. Udemy’s online courses are a great place to start if you want to learn Python on your own.
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Who this course is for:
- Machine learning isn’t just useful for predictive texting or smartphone voice recognition. Machine learning is constantly being applied to new industries and new problems. It is for everyone
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