JavaScript from Beginner to Expert

Monday, November 28, 2022

Udemy Free Discount - JavaScript from Beginner to Expert, Become a JavaScript expert in 30 days, even if you are a JS beginner. Become a front-end developer of websites in JS

4.4 (788 ratings), Created by Arkadiusz Włodarczyk, English

PREVIEW THIS COURSE - GET COUPON CODE

javascript-from-beginner-to-expert-bring-life-to-your-site

What you'll learn

  • You will be able to create tooltips, slideshows, galleries with thumbnails and many more
  • You will understand events, variables, objects, arrays, functions, loops, conditional statements, DOM, RegExp, Cookies and use that info in your scripts.
  • You will know how to pre-validate all kind of forms and give users information if something is wrong without reloading the website
  • You will know how to debug and keep your code performing well
  • Quizzes and exercises
  • Support from the author
  • Organised material taking you from the Beginner to Expert level in Javascript


Posted by free courses at November 28, 2022

The Complete Digital Marketing Course - 12 Courses in 1

Friday, November 25, 2022

Free Coupon Discount - The Complete Digital Marketing Course - 12 Courses in 1, Master Digital Marketing: Strategy, Social Media Marketing, SEO, YouTube, Email, Facebook Marketing, Analytics & More! | Created by Rob Percival, Daragh Walsh, Codestars by Rob Percival

learn-digital-marketing-course

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Preview this Udemy Course GET COUPON CODE

Description
Join 300,000+ students in the bestselling digital marketing course on Udemy!

With over 20 hours of training, quizzes and practical steps you can follow - this is one of the most comprehensive digital marketing courses available. We'll cover SEO, YouTube Marketing, Facebook Marketing, Google Adwords, Google Analytics and more!

Learn By Doing

The course is hugely interactive with projects, checklists & actionable lectures built into every section.

Learn step by step how to market a business online from scratch across all the major marketing channels.

Follow the steps on screen to get results at work, for own business or for your digital marketing clients.

12 Courses in 1

Covering 12 major online marketing topics and comprising of 20+ hours of clear cut lectures & practice activities - this course is "incredible value for money!" as one student said. We'll cover:

Market Research. Ask 3 simple questions to validate your business idea.

WordPress. Build a world-class website in 1 hour without any coding.

Email Marketing. Build a mailing list of 1000 people in 30 days from scratch.

Copywriting. Write sales pages that make the cash register ring!

SEO (Search Engine Optimisation). Get free traffic to your website with SEO.

YouTube Marketing. Drive traffic & sales with simple "how to" videos.

Social Media Marketing (Instagram, Facebook, Twitter, Pinterest & Quora).

Linkedin Marketing. Go viral on Linkedin and 400x your connections.

App Marketing. Discover 43 Ways To Promote Your App.

Google Adwords. Avoid common mistakes and set up profitable campaigns first time.

Facebook Ads. Make money with Facebook Ads without spending a fortune.

Google Analytics. Improve your marketing with Google Analytics data.

By the end of this course, you will be confidently implementing marketing strategies across the major online marketing channels.

All the strategies, tips and tools recommended are either free or very cost effective.

You'll Also Get:

✔ Lifetime Access to course updates

✔ Fast & Friendly Support in the Q&A section

✔ Udemy Certificate of Completion Ready for Download

Don't Miss Out!

Every second you wait is costing you valuable leads and sales.

This courses come with a 30 day money-back guarantee - so there's no risk to get started.

Go ahead and hit the "take this course" button to start growing a business online today!

Who this course is for:
Pre launch business owners who don't know where to get started
Website owners who are struggling to get traffic and sales
Anyone looking to start a pick up highly paid freelancing skills

100% Off Udemy Coupon . Free Udemy Courses . Online Classes

Posted by free courses at November 25, 2022

Selenium 4 WebDriver with Java(Basics + Advance + Architect)

Wednesday, November 23, 2022

 

Selenium 4 WebDriver with Java(Basics + Advance + Architect)

Selenium 4 WebDriver with Java(Basics + Advance + Architect) - 
#1 2022 TOP RATED, BEST SELLER Course on SELENIUM 4.0, Trusted by 500,000+ students with Many Live Projects & Frameworks

In depth Course on Selenium WebDriver 4.0 Latest version Trusted by 500,000+ students, Includes many Live Projects & End 2 End Frameworks




NO OTHER COURSE IN THE INDUSTRY TO COVER THESE MANY IN-DEPTH TOPICS ON SELENIUM - 140+ HOURS, 450+ LECTURES


What you'll learn


  • ****By the End of the course you will be as much trained to automate any web based application using Selenium*****
  • *****You will be working on many Live projects, Design complex frameworks and Design interactive Reports using ReportNG, XSLT, Extent Reports etc*****
  • *****You should be able to work with utilities like: ANT, MAVEN, JENKINS, DOCKERS, GRID for Parallel Execution, LOG4J API, JAVAMAIL API, APACHE POI API, JDBC Connection for Database Testing etc****
  • ****Many pdf files, course code & other reference material will be provided along with the video lectures*****
  • ****By the end of the course you should be able to Master Selenium Automation & crack any interview*****
  • By the end of this course you should be able to design major frameworks from scratch like: Data Driven, Keyword Driven, Hybrid, Page Object Model, Page Factories, CucumberBDD etc
  • You should be able to justify 2-3 years of your existing experience in Selenium
  • You should be able to work on Live Projects, Manipulate complex Xpath, CSS and important locators
  • Many pdf files, course code and other reference material will be provided along with the video lectures

Preview this Course

Complete Salesforce Certified Platform Developer 1 Course

Tuesday, November 22, 2022

 

Complete Salesforce Certified Platform Developer 1 Course

Complete Salesforce Certified Platform Developer 1 Course - 
Salesforce Developer I Certification Course - Covers Apex, Configurations, Salesforce Flows and Everything In-Between

Highest rated, Created by Anthony Wheeler, Mike Wheeler

Learn all required knowledge to pass your Salesforce Platform Developer I Certification Exam. This course contains more than 13 and a half hours of material and covers:

What you'll learn


  • Apex
  • Lightning Web Components
  • Salesforce Platform Developer I
  • Salesforce Configurations
  • Salesforce
  • Salesforce Development
  • Salesforce Developer Skills
  • Salesforce Platform Developer 1

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Posted by free courses at November 22, 2022

Building Bulk SQL Queries with Excel ,SQL, Oracle, TOAD

Saturday, November 19, 2022

Building Bulk SQL Queries with Excel ,SQL, Oracle, TOAD

Building Bulk SQL Queries with Excel ,SQL, Oracle, TOAD, 
Use Excel and SQL to build bulk SQL queries

New, Created by 247 Learning

SQL is a domain-specific language used in programming and designed for managing data held in a relational database management system, or for stream processing in a relational data stream management system.

SQL stands for Structured Query Language. SQL lets you access and manipulate databases.

SQL became a standard of the American National Standards Institute (ANSI) in 1986, and of the International Organization for Standardization (ISO) in 1987

What you'll learn


  • Install Oracle Database Server
  • Install TOAD
  • Connect TOAD to Oracle
  • Use excel formulas to build bulk SQL Queries
  • Build bulk SQL INSERT Statements with excel
  • Build bulk SQL UPDATE Statements with excel
  • Build bulk SQL DELETE Statements with excel
  • Create database table

Preview this Course

Posted by free courses at November 19, 2022

Machine Learning Real World projects in Python

Machine Learning Real World projects in Python

Machine Learning Real World projects in Python - 
Build a Portfolio of Machine Learning projects with Python, Regression algos , Unsupervised Machine Learning & More!

Preview this Course GET COUPON CODE

What you'll learn
  • Machine Learning Engineers earn on average $164,000 - become Job Ready ML Engineer with this course!
  • Go from zero to hero in Entire Pipeline of Machine learning from Data Collection to building a Machine Learning Model
  • Solve any problem in your business, job or in real-time with powerful Machine Learning algorithms
  • Mathematics behind All Machine Learning algos ( Linear Regression , logistic , Decision Tree , Ensemble algos , KNN , Naive Bayes & many more !
  • Various Feature selection Techniques & how to apply it in Real-World
  • How to Approach a problem in Real-world..
  • Case studies

Description
Machine Learning is one of the hottest technology field in the world right now! This field is exploding with opportunities and career prospects. Machine Learning techniques are widely used in several sectors now a days such as banking, healthcare, finance, education transportation and technology.



This course covers several technique in a practical manner, the projects include coding sessions as well as Algorithm Intuition:
So, if you’ve ever wanted to play a role in the future of technology development, then here’s your chance to get started with Machine Learning. Because in a practical life, machine learning seems to be complex and tough,thats why we’ve designed a course to help break it down into real world use-cases that are easier to understand.

1.Task #1 @Predicting the Hotel booking  : Predict Whether booking  is going to cancel or not

3.Task #2 @Predict Whether Person has a Chronic Disease or not: Develop a Machine learning  Model that predicts whether person has kidney disease or not

2.Task #3 @Predict the Prices of Flight: Predict the prices of Flght using Regression & Ensemble Algorithms..





The course covers a number of different machine learning algorithms such as Regression and Classification algorithms. From there you will learn how to incorporate these algorithms into actual projects so you can see how they work in action! But, that’s not all. In addition to quizzes that you’ll find at the end of each section, the course also includes a 3 brand new projects that can help you experience the power of Machine Learning using real-world examples!

Who this course is for:
  • Data Scientists who want to apply their knowledge on Real World Case Studies
  • Machine Learning Enthusiasts who look to add more projects to their Portfolio

Posted by free courses at November 19, 2022

A Practical Approach to Timeseries Forecasting using Python

Wednesday, November 16, 2022

A Practical Approach to Timeseries Forecasting using Python

 A Practical Approach to Timeseries Forecasting using Python - 
A Complete Course on Time Series Forecasting using Machine Learning and Recursive Neural Networks with Projects


New, Created by AI Sciences, AI Sciences Team


What you'll learn


• Learn the basics

Comprehensive Course Description:


Have you ever wondered, how weather predictions are made?


Have you ever thought to estimate the global population in 2050!


What if, someone told you that you can predict the expected life of our universe by just sitting next to your laptop in your home.


Its all true! Just because of the Time Series Forecasting pedagogies by using state-of-the-art and robust models of Machine Learning and Deep Learning.


You might have searched for many relevant courses, but this course is different!


This course is a complete package for the beginners to learn time series, data analysis and forecasting methods from scratch. Every module has engaging content, a complete practical approach is used in along with brief theoretical concepts. At the end of every module, we assign you a hand-on exercise or quiz, the solution to the quizzes is also available in the next video.


We will be starting with the theoretical concepts of time series analysis, after a brief overview of its features, examples, mechanism of time series data collection and its scope in the real world, we will learn the basic bench marked steps to compute time series forecasting.


This complete package will enable you to learn the basic to advance data analysis and visualization with respect to time series data by using Numpy, Pandas and Matplotlib. We’ll be using Python as a programming language in this course, which is the hottest language nowadays if we talk about machine leaning. Python will be taught from elementary level up to an advanced level so that any machine learning concept can be implemented.


This comprehensive course will be your guide to learning how to use the power of Python to evaluate your time series datasets on the basis of seasonality, trend, noise, autocorrelation, mean overtime, correlation, and on stationarity. Moreover, the impact and role of feature engineering will make you capable of performing exceptional data handling for your forecasting models. Based on this learning you will be able to prepare your time series data for the applied Machine Learning and RNNs Models to test, train and evaluate your forecasted scores.


We’ll learn all the basic and necessary concepts for the applied machine learning models such as Auto-Regression, Moving Average, ARIMA, Auto-ARIMA, SARIMA, Auto-SARIMA and SARIMAX in the perspective of the time series forecasting. Moreover, the performance comparison of these models will also be comprehensively discussed.


Machine learning has been ranked as one of the hottest jobs on Glassdoor, and the average salary of a machine learning engineer is over $110,000 in the United States, according to Indeed! Machine Learning is a rewarding career that allows you to solve some of the world's most interesting problems!


In the RNNs Module, we’ll be learning a complete mechanism of building GRU, LSTM, Stacked LSTM, BiLSTM and Stacked BiLSTM models along with the practical concepts of the underfitting, overfitting, bias, variance, dropout, role of dense layers, impact of batch sizes, and performance of different activation functions on the RNN models of multiple different layers. Each concept of the “Recursive Neural Networks” (RNNs) will be taught theoretically and will be implemented using Python.


This course is designed for both beginners with some programming experience or even those who know nothing about Data Analysis, ML and RNNs!


This comprehensive course is comparable to other Time Series Courses using Machine Learning and RNNs courses that usually cost thousands of dollars, but now you can learn all that information at a fraction of the cost in only one course! With over 12 hours of HD video lectures that are divided into more than 120 videos and detailed code notebooks for every address this is one of the most comprehensive courses for Time Series Forecasting with Machine Learning and RNNs on Udemy!


of Time Series Analysis and Forecasting.

• Learn basics of Data Analysis Techniques and to Handle Time Series Forecasting.

• Learn to implement the basics of Data Visualization Techniques using Matplotlib

• Learn to Evaluate and Analyze Time Series Forecasting Parameters i.e., Seasonality, Trend, and Stationarity etc.

• Learn to compute and visualize the auto correlation, mean over time, standard deviation and gaussian noise in time series datasets.

• Learn to evaluate applied machine learning in Time Series Forecasting

• Learn to implement Machine Learning Techniques for Time Series Forecasting i.e., Auto Regression, ARIMA, Auto ARIMA, SARIMA, and SARIMAX

• Learn basics of RNN Models i.e., GRU, LSTM, BiLSTM

• Learn to model LSTM, Stacked LSTM, BiLSTM and Stacked BiLSTM models for time series forecasting.

• Learn the impact of Overfitting, Underfitting, Bias and Variance on the performance of RNN Models

• Learn how to implement ML and RNN Models with three state-of-the-art projects.

• And much more…


Preview this Course

Posted by free courses at November 16, 2022

100 Days Of Code - 2023 Web Development Bootcamp

 

100 Days Of Code - 2023 Web Development Bootcamp

100 Days Of Code - 2023 Web Development Bootcamp - 
Learn web development from A to Z in 100 days (or at your own pace) - from "basic" to "advanced", it's all included!


Created by Academind by Maximilian Schwarzmüller, Maximilian Schwarzmüller, Manuel Lorenz


Join the most comprehensive web development bootcamp on Udemy!


This course will teach you web development and turn you into a web developer in 100 days - or allow you to refresh key essentials and expand your existing knowledge!


Becoming a web developer is a great choice because web development opens up many career paths and web development skills are required in pretty much every business that exists today - and of course this will only increase in the future!


It's not just about websites - it's also about "behind the scenes" services used by mobile apps like Uber or AirBnB. It's about rich web apps like Google Docs as well as browser games. And of course also about regular websites like Facebook, online blogs, online shops like Amazon and much, much more!


Hence it's no wonder, that web developers are in high demand! And, besides great job perspectives, as a web developer, you can of course also easily build your own digital business!


This Course Is For You!


This course will teach you web development from the ground up and thanks to the "100 Days Of Code Challenge" which is incorporated into this course (though it's optional to commit to it!), you can become a web developer in 100 days with help of this course!


No prior web development knowledge is required at all to get started with this course. We will explore all important basics, all fundamentals and all key concepts together, step by step.


But this course is also for advanced students who already do have web development knowledge! It's a deep-dive course and hence you will be able to expand your existing knowledge by diving deeper into key fundamentals like HTML, CSS or NodeJS and also by exploring advanced concepts like handling payments, building REST APIs or website security.


Since it's a huge course, the course is built in a modular way. This means, that you can take it step by step, lecture by lecture but you can also jump right into the course sections that are most interesting to you. Of course, we recommend the "step-by-step" approach for beginners - simply because all the lectures and sections build up on each other. But as a more experienced developer, you can of course skip basics that aren't interesting to you!


The 100 Days Of Code Challenge


We built this course with the "100 Days Of Code Challenge" in mind - a challenge (not invented by us) that aims to keep you motivated to code for at least 1 hour per day for 100 days.


Since this is a huge course (with around 80 hours of content!) it can be very overwhelming. And we know that many students never finish a course.


That's a pitty, because this course is packed with content, exercises, quizzes, assignments and demo projects! We build a browser-game, a blog, a travel website, an online shop and much, much more.


Therefore, we provide clear guidance on how you can take this course from A to Z within 100 days by spending 1 to 2 hours per day watching videos and learning. As part of the course, you get access to a companion website that provides a clear structure and you also find annotations right in the course curriculum.


Of course taking this "100 Days" challenge is totally optional though! You can take the course at your own pace as well and skip any content you're not interested in!


What you'll learn


  • How the web works and how to get started as a web developer
  • Learn web development in 100 days (optional - you can also pick a different pace)
  • Build websites, web apps and web services (and understand what these "things" are)
  • Build frontend user interfaces with HTML, CSS & JavaScript
  • Build backend processes with NodeJS, Express & SQL + NoSQL databases
  • Add advanced features like user authentication, file upload or database queries to websites

Preview this Course

Posted by free courses at November 16, 2022

Full Stack React Js with Redux, Node.Js, Express.Js, MongoDB

 

Full Stack React Js with Redux, Node.Js, Express.Js, MongoDB

Full Stack React Js with Redux, Node.Js, Express.Js, MongoDB - 
Learn React JS with Redux, Hooks, Context, Node.js, Express js and Mongo DB from scratch. Dive in React, Express, nodejs

Created by Oak Academy

Welcome to the "Full Stack React Js with Redux, Node.Js, Express.Js, MongoDB" course.


Learn React JS with Redux, Hooks, Context, Node.js, Express js and Mongo DB from scratch. Dive in React, Express, nodejs


In this course, you will learn to develop a web application with React JS, Redux, Hooks & Context, Node.Js, Express.Js and Mongo DB from scratch.


If you are thinking to start to learn ReactJS, this course is the best match for you.


We have explained React from beginner to all levels. We have explained all the topics as simple as possible with examples, slides, and diagrams.


We have created a lot of projects while explaining the subjects. Because we believe that applied educations are much more useful than other teaching methods.


We explained all the subjects with simple examples and applications, and explanatory diagrams in a way that the student can understand at all levels.


We paid attention to explain all the topics in order. Because we think that the order of presentation of the subject is as important as the content of education. We have seen this shortcoming in many pieces of training we have examined and tried to explain these issues to you in the best way.


React is an essential Javascript framework for web development. It is the most popular framework for developing web, mobile, and desktop app user interfaces.


Whether you’re interested in adding React to your existing dev skillset, or you want to develop full-stack web apps by using tools like NodeJS, Redux, and MongoDB in conjunction with React, Udemy has a comprehensive selection of courses to choose from.


Node.js is an open-source, cross-platform, back-end, JavaScript runtime environment that executes JavaScript code outside a web browser.


NodeJS and Express is a growing web server technology. By learning Node with Express, you can improve your skills, get a new job and you can build a powerful, robust backend.


Learning Node.js is a great way to get into backend web development, or expand your fullstack development practice. With Udemy’s hands-on Node.js courses, you can learn the concepts and applications of this wildly useful JavaScript runtime.


Node.js is essential to developing real-time applications in JavaScript and has been instrumental in the development of websites like eBay and PayPal. Node is designed around an event loop, which allows for easy management of asynchronous functions. This makes it a popular environment for modern developers working on chat and gaming apps.


MongoDB is a cross-platform document-oriented NoSQL database program. By using MongoDB, you can build a modern application database for your projects.


MongoDB is a document-oriented data store that is easy to get started with but also scales well as your application grows. It’s schemaless nature allows greater flexibility or changing application requirements. It’s one of the best databases in terms of developer productivity.


MongoDB is an document-oriented database designed to allow developers to scale their applications to meet scalability demands. MongoDB features a flexible document model that allows for accelerated development productivity so that you can release better applications, faster.


No Previous Knowledge is needed!


You don’t need to have previous knowledge about React. This course will take you from a beginner to a more advanced level with hands-on examples.


You will be confident in using React JS, and if you ever get stuck, we will be there to help.


Learn by doing!


So we have made this course as simple as possible in order to take you through step by step so you can feel confident and get a truly good understanding of how to utilize ReactJS. In this course, we will be teaching React by creating various projects.


What you'll learn


  • This is the full React JS course. You will learn React JS, Redux, Hooks and Context, NodeJs, ExpressJs and Mongo DB
  • You will learn React JS with hands-on examples
  • Learn how to create Single Page Web Application with React JS
  • Create reusable React Components
  • User Inputs, Forms and Events in React
  • Learn to create multi-page web app with react-router-dom
  • We will learn how to perform asynchronous operations with Redux thunk
  • Learn to consume context with Context Consumer
  • How to manipulate context data in class-based components
  • Learn how powerfull when we use Context and Hooks together
  • Learn components, props, states and component life cycle methods in React JS
  • Learn sending request to an API and fetch data
  • Routing with React Router
  • Manage the data of our application with the Redux library
  • Create context with class-based component
  • Learn how to consume context with static contextType
  • Learn the most important hook functions like, useState, useEffect, useReducer and useContext
  • Learn how to easily build the largest and most advanced React applications
  • Dive into Nodejs, learn rapidy growing web server technology, Nodejs & understand how NodeJS works with Node course!
  • By learning growing web server technology, Nodejs, you can improve your skills, get a new job and you can build powerful, robust web applications.
  • Learn the key concepts of the NodeJS
  • Learn to create servers, and understand how it works
  • Understand and use the Event Emitter
  • Understand Buffers, Streams, and Pipes
  • Learn routing with NodeJS
  • Learn the most used, open-source document database, and NoSQL database aka MongoDB
  • Logic behind the MongoDB data storage
  • The most popular Object Data Modeling Library for MongoDB, Mongoose JS
  • Learn to execute CRUD - write queries to create, read, update and delete operations
  • Understand terminal commands for managing the database
  • Advanced Features of MongooseJS
  • The best testing framework for NodeJS, Mocha
  • Learn how easy to use MongoD
  • Learn the key concepts of the Express JS
  • Express Route parameters
  • Middleware & Static files
  • Query Strings
  • There are many reasons why React is popular. One reason is that Facebook developed it.
  • React, or React JS, is a front-end Javascript library for building UI components for the web. If you are interested in web development, React is the perfect lib
  • React is worth learning. There are a couple of reasons. The first one is that React is in high demand in the software development job market
  • Frameworks provide an opinionated approach to building an entire application.
  • React encourages engineers to write code using a Functional Programming approach. Engineers create components, which are normal Javascript functions
  • React is an open-source JavaScript frontend library. Some developers consider it a frontend framework because it does more than standard libraries usually do.
  • React is an essential Javascript framework for web development. It is the most popular framework for developing web, mobile, and desktop app user interfaces.
  • A runtime system is a platform where a software program runs. It’s essentially an environment housing the collection of software and hardware that allows an app

Preview this Course

Posted by free courses at November 16, 2022

The Complete Guide To Mastering Modern Day Python In 2022

Monday, November 14, 2022

The Complete Guide To Mastering Modern Day Python In 2022

The Complete Guide To Mastering Modern Day Python In 2022 - 
A modern & essential guide to mastering Python programming in 2022.


New, Created by Federico Azzurro


Are you ready to become a true Python programmer and learn some of the most demanded skills on the market in programming for 2022?




Who is this course for?


This course is for anyone who wants to gain a very profound understanding of the Python language, so that you can take advantage of one of the most important tools of the century. Whether your are a beginner, or have experience with code, I will start from the very basics, and build up to the most important and advanced aspects of the Python programming language. You will also have the option to ask questions at any point during the course to profound your understanding of the Python programming language.




Why should you pick this course and not the others?


There are thousands of Python courses on the internet, so why should you pick this one? Well, to put it simply, I believe that I teach programming concepts in a far more effective way than a majority of the courses on the Internet. I make sure to only teach what's essential and needed, so that you don't waste time with code that you will never see or use in your entire career. I'm a self-taught professional and will teach you how you can be the same!




30 Day Money-Back Guarantee


At any point of this course you can opt in to get your money back. Whether you feel that this course is not right for you, or changed your mind about learning Python, you can easily request a refund which will be immediately refunded to your account with no questions asked through Udemy!


What you'll learn


  • Master the most important concepts in Python, and start using them from day 1.
  • You will be able to program in Python professionally.
  • Expand your thinking in ways you never thought possible.
  • Create powerful scripts that can be used for automation in everyday life.
  • Start building your own career with a new advanced skillset.
  • Gain confidence in creating your own projects.
  • Create and host your very own API.


Preview this Course

Posted by free courses at November 14, 2022

Graphic Design MasterClass- Photoshop, Illustrator, Indesign

 


Graphic Design MasterClass- Photoshop, Illustrator, Indesign - 
Graphic design for beginners including Graphic Design Theories, Photoshop, Illustrator, Indesign, CC & Creative Thinking

Created by Khalil Ibrahim

Graphic design & Advertising Masterclass:


Do you want to learn graphic design & advertising from scratch? well, I’ve tried to squeeze my academic studying of advertising in the faculty of applied arts & experience in the design field for more than 15 years in one single graphic design course that would help an absolute beginner or even an advanced graphic designer to become a professional graphic designer.


What you'll learn


  • Graphic design Theories
  • color theory
  • Composition & layout rules
  • Grid systems
  • creative thinking
  • How to use Photoshop in a creative way to create stunning Ads
  • advertising rules in action while designing
  • the science behind achieving great visuals
  • photoshop tools panels & essentials
  • about typography anatomy & how to use type & fonts properly
  • How to use Illustrator in a creative way to create brand identity
  • How to use illustrator to design Logos, business cards, letterheads, envelope..etc
  • how to present your graphic design work in a great way to easily get your designs approved by clients
  • saving & exporting different formats
  • Illustrator tools panels & essentials
  • How to use Indesign in a creative way to create amazing editorial design
  • how to create brochures, catalogs & flyers
  • how to create multipage editorial design up to 16 pages & more
  • build your own portfolio through the entire course
  • Get a portfolio template to customize it according to your own style
  • about printing & screen designs technicals
  • how to export your designs in different formats
  • how to create printing PDF & Impositions for your editorial design
  • you will get tons of design files & assets to assist you in your designs
  • you will find many exercises to sharped your design skills
  • you will learn to select & mask images with different techniques including tough areas like Hair, Smoke..etc.
  • you will learn how to design precisely in Photoshop using design grids & guides.
  • Work like a professional in Photoshop mastering all Non- destructive techniques.
  • Create graphics from scratch.
  • Use the Pen & drawing tools like a professional in Photoshop, Illustrator & Indesign
  • Master the Different drawing & illustrations tools & techniques
  • to speed up your workflow & speed up Illustrator performance as well
  • Know the details of Glyphs, Stylistic Sets, Variable fonts

Preview this Course

Posted by free courses at November 14, 2022

The Complete Certified in Cybersecurity (CC) course ISC2 '23

Sunday, November 13, 2022

The Complete Certified in Cybersecurity (CC) course ISC2 '23

 The Complete Certified in Cybersecurity (CC) course ISC2 '23 - 
Start your Cyber security career today! Take the Complete Certified in Cybersecurity (CC) beginners course ISC2 - 2023

Bestseller Created by Thor Pedersen | 358,000+ Enrollments Worldwide, ThorTeaches.com LLC

Welcome, I am Thor Pedersen, and I am here to help you get that critical entry level Cyber security knowledge, so you can get your first job in Cyber security and/or pass your Certified in Cybersecurity (CC) certification by (ISC)².


My courses on Udemy have over 350,000 enrollments from 190+ countries, and my courses Certified in Cybersecurity (CC), CISSP, PMP, and CISM are the “Best Selling” and “Highest Rated”.


What you'll learn


  • Gain the knowledge for entry level IT, Cyber Security, roles and job interviews.
  • Prepare for in-demand Cyber Security entry level certifications like Certified in Cybersecurity (CC), CSX-P, or ITCA.
  • Get started on or take your Cyber Security career to the next level! Get the tools to start or grow your Cyber Security career.
  • Understand and be able to explain: Security Principle, Business Continuity Planning (BCP), Disaster Recovery Planning (DRP) and Incident Response Concepts.
  • Understand and be able to explain: Access Controls Concepts, Network Security, Security Operations, and much more.
  • Understand and be able to explain: The CIA triad, IAAA, Risk Management, Organizational/IT/Cyber Security Governance.
  • Understand and be able to explain: Physical/logical access control, Disaster planning/recovery, Cryptography, Network Security, Malware, and much more.
  • Understand and be able to explain: Cyber Security, Information, and IT Security.


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Complete Web Application Hacking & Penetration Testing

 

Complete Web Application Hacking & Penetration Testing

Complete Web Application Hacking & Penetration Testing - 
Hacking web applications, hacking websites, bug bounty & penetration testing in my ethical hacking course to be Hacker


Created by Muharrem AYDIN


Welcome to my Complete Web Application Hacking & Penetration Testing course.


Hacking web applications, hacking websites, bug bounty & penetration testing in my ethical hacking course to be Hacker




Web Applications run the world. From social media to business applications almost every organization has a web application and does business online. So, we see a wide range of applications being delivered every day.

Whether you want to get your first job in IT security, become a white hat hacker, or prepare to check the security of your own home network, Udemy offers practical and accessible ethical hacking courses to help keep your networks safe from cybercriminals.


Penetration testing skills make you a more marketable IT tech. Understanding how to exploit servers, networks, and applications means that you will also be able to better prevent malicious exploitation. From website and network hacking, to pen testing in Python and Metasploit, Udemy has a course for you.

Our Student says that: This is the best tech-related course I've taken and I have taken quite a few. Having limited networking experience and absolutely no experience with hacking or ethical hacking, I've learned, practiced, and understood how to perform hacks in just a few days.


I was an absolute novice when it came to anything related to penetration testing and cybersecurity. After taking this course for over a month, I'm much more familiar and comfortable with the terms and techniques and plan to use them soon in bug bounties.


What you'll learn


  • Ethical hacking involves a hacker agreeing with an organization or individual who authorizes the hacker to levy cyber attacks on a system.
  • Becoming an ethical hacker involves learning at least one programming language and having a working knowledge of other common languages like Python, SQL, C++
  • Many hackers use the Linux operating system (OS) because Linux is a free and open-source OS, meaning that anyone can modify it. It’s easy to access.
  • Ethical hacking is legal because the hacker has full, expressed permission to test the vulnerabilities of a system
  • The Certified Ethical Hacker (CEH) certification exam supports and tests the knowledge of auditors, security officers, site administrators, security.
  • Passing the Certified Information Security Manager (CISM) exam indicates that the credentialed individual is an expert in the governance of information security
  • The different types of hackers include white hat hackers who are ethical hackers and are authorized to hack systems, black hat hackers who are cybercriminals.
  • Penetration testing, or pen testing, is the process of attacking an enterprise's network to find any vulnerabilities that could be present to be patched.
  • There are many types of penetration testing. Internal penetration testing tests an enterprise's internal network.
  • Penetration tests have five different stages. Security experts will also gather intelligence on the company's system to better understand the target
  • Advanced Web Application Penetration Testing
  • Terms, standards, services, protocols and technologies
  • Setting up Virtual Lab Environment
  • Software and Hardware Requirements
  • Modern Web Applications
  • Web Application Architectures
  • Web Application Hosting
  • Web Application Attack Surfaces
  • Web Application Defenses
  • Core technologies
  • Web Application Proxies
  • Whois Lookup
  • DNS Information
  • Subdomains
  • Discovering Web applications on the Same Server
  • Web Crawling and Spidering - Directory Structure
  • Authentication Testing
  • Brute Force and Dictionary Attacks
  • Cracking Passwords
  • CAPTCHA
  • Identifying Hosts or Subdomains Using DNS
  • Authorization Testing
  • Session Management Testing
  • Input Validation Testing
  • Testing for Weak Cryptography
  • Client Side Testing
  • Browser Security Headers
  • Using Known Vulnerable Components
  • Bypassing Cross Origin Resource Sharing
  • XML External Entity Attack
  • Attacking Unrestricted File Upload Mechanisms
  • Server-Side Request Forgery
  • Creating a Password List: Crunch
  • Attacking Insecure Login Mechanisms
  • Attacking Improper Password Recovery Mechanisms
  • Attacking Insecure CAPTCHA Implementations
  • Inband SQL Injection over a Search Form
  • Inband SQL Injection over a Select Form
  • Time Based Blind SQL Injection
  • ethical hacking
  • cyber security
  • android hacking
  • hacking
  • Ethical Intelligence
  • Ethical Hacker

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AWS Certified Data Analytics Specialty 2022 - Hands On!

Friday, November 11, 2022

AWS Certified Data Analytics Specialty 2022 - Hands On!

Free Coupon Discount - AWS Certified Data Analytics Specialty 2022 - Hands On!, Practice exam included! AWS DAS-C01 certification prep course with exercises. Kinesis, EMR, DynamoDB, Redshift and more!

Created by , Sundog Education by Frank Kane, Stephane Maarek | AWS Certified Solutions Architect & Developer Associate, Frank Kane

Students also bought

  • Ultimate AWS Certified SysOps Administrator Associate 2020
  • 2020 AWS SageMaker, AI and Machine Learning Specialty Exam
  • AWS Lambda and the Serverless Framework - Hands On Learning!
  • AWS CloudFormation Master Class
  • AWS Certified Machine Learning Specialty 2020 - Hands On!
  • AWS Certified DevOps Engineer Professional 2020 - Hands On!

Preview this Udemy Course GET COUPON CODE


Description
[v2020: The course has been fully updated for the new AWS Certified Data Analytics -Specialty DAS-C01 exam, and will be kept up-to-date all of 2020. Optional content for the previous AWS Certified Big Data - Speciality BDS-C01 exam remains as well as an appendix. Happy learning! ]
The AWS Certified Data Analytics Specialty Exam is one of the most challenging certification exams you can take from Amazon. Passing it tells employers in no uncertain terms that your knowledge of big data systems is wide and deep. But, even experienced technologists need to prepare heavily for this exam. This course sets you up for success, by covering all of the big data technologies on the exam and how they fit together.
Best-selling Udemy instructors Frank Kane and Stéphane Maarek have teamed up to deliver the most comprehensive and hands-on prep course we've seen. Together, they've taught over 300,000 people around the world. This course combines Stéphane's depth on AWS with Frank's experience in Big Data, gleaned during his 9-year career at Amazon itself. Both Frank and Stéphane have taken and passed the exam themselves on the first try.
The world of data analytics on AWS includes a dizzying array of technologies and services. Just a sampling of the topics we cover in-depth are:
Streaming massive data with AWS Kinesis
Queuing messages with Simple Queue Service (SQS)
Wrangling the explosion data from the Internet of Things (IOT)
Transitioning from small to big data with the AWS Database Migration Service (DMS)
Storing massive data lakes with the Simple Storage Service (S3)
Optimizing transactional queries with DynamoDB
Tying your big data systems together with AWS Lambda
Making unstructured data query-able with AWS Glue
Processing data at unlimited scale with Elastic MapReduce, including Apache Spark, Hive, HBase, Presto, Zeppelin, Splunk, and Flume
Applying neural networks at massive scale with Deep Learning, MXNet, and Tensorflow
Applying advanced machine learning algorithms at scale with Amazon SageMaker
Analyzing streaming data in real-time with Kinesis Analytics
Searching and analyzing petabyte-scale data with Amazon Elasticsearch Service
Querying S3 data lakes with Amazon Athena
Hosting massive-scale data warehouses with Redshift and Redshift Spectrum
Integrating smaller data with your big data, using the Relational Database Service (RDS) and Aurora
Visualizing your data interactively with Quicksight
Keeping your data secure with encryption, KMS, HSM, IAM, Cognito, STS, and more
Throughout the course, you'll have lots of opportunities to reinforce your learning with hands-on exercises and quizzes. And when you're done, this course includes a practice exam that's very similar to the real exam in difficulty, length, and style - so you'll know if you're ready before you invest in taking it. We'll also arm you with some valuable test-taking tips and strategies along the way.
Data analytics is an advanced certification, and it's best tackled by students who have already obtained associate-level certification in AWS and have some real-world industry experience. This exam is not intended for AWS beginners.
You want to go into the AWS Certified Data Analtyics Specialty Exam with confidence, and that's what this course delivers. Hit the enroll button, and we're excited to see you in the course... and ultimately to see you get your certification!

100% Off Udemy Coupon . Free Udemy Courses . Online Classes

Ultimate AWS Certified SysOps Administrator Associate 2022

Thursday, November 10, 2022

Ultimate AWS Certified SysOps Administrator Associate 2022

 Ultimate AWS Certified SysOps Administrator Associate 2022 - 
Full Practice Test with Explanations included! PASS the AWS Certified SysOps Administrator Associate SOA-C02 Exam.

Bestseller | Created by Stephane Maarek | AWS Certified Cloud Practitioner,Solutions Architect,Developer

Welcome! I'm here to help you prepare and PASS the newest SOA-C02 AWS Certified SysOps Administrator Associate exam.


[April 2022 Update]: Over 30 videos have been refreshed/added to keep up with the AWS UI changes and exam changes


[October 2021 Update]: Over 50 videos have been refreshed/added to keep up with the AWS UI changes and exam changes


[August 2021 Update]: 200+ videos have been refreshed and added to update the course to exam version SOA-C02


[May 2020 Update]: 50+ videos have been updated to keep up with AWS UI changes.


[July 2019 Update]: Few lectures refreshed, including EC2 placement groups.


-----------------------------------


The AWS Certified SysOps Administrator Associate certification is one of the most challenging exams. It's great at assessing how well you understand not just AWS, but how to administer it and troubleshoot issues, which makes this certification incredibly valuable to have and pass. Rest assured, I've passed it myself with a score of 980 out of 1000. Yes, you read that right, I only made one mistake! Next, I want to help YOU pass the AWS Certified SysOps Administrator Associate certification with flying colors.


Important: It is highly advised that you first pass the Developer or Solution Architect certification first.


This is going to be a long journey, but passing the AWS Certified SysOps Administrator Associate exam will be worth it!


What you'll learn


  • Pass the AWS Certified SysOps Administrator Associate Certification (SOA-C02)
  • Full Practice Exam with Explanations included!
  • All 400+ slides available as downloadable PDF
  • Apply the right AWS services for your future real-world AWS projects
  • Master topics you know from a SysOps perspective: EC2, ELB, ASG, RDS & more
  • Use Systems Manager to perform automations and patching
  • Troubleshoot Elastic Beanstalk and CloudFormation
  • Store data properly with maximum performance using EBS and EFS
  • Master S3 and its ecosystem: Glacier, Snowball, Storage Gateway, CloudFront
  • Implement Monitoring, Security, Compliance, and AWS Account Management
  • Master networking in AWS: Route 53 and VPC in depth
  • Show less

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Machine Learning & Deep Learning in Python & R

Tuesday, November 8, 2022

data_science_a_to_z

Machine Learning & Deep Learning in Python & R - 
Covers Regression, Decision Trees, SVM, Neural Networks, CNN, Time Series Forecasting and more using both Python & R
  • Created by Start-Tech Academy
  • English [Auto]
Preview this Udemy Course GET COUPON CODE

What you'll learn

  • Learn how to solve real life problem using the Machine learning techniques
  • Machine Learning models such as Linear Regression, Logistic Regression, KNN etc.
  • Advanced Machine Learning models such as Decision trees, XGBoost, Random Forest, SVM etc.
  • Understanding of basics of statistics and concepts of Machine Learning
  • How to do basic statistical operations and run ML models in Python
  • Indepth knowledge of data collection and data preprocessing for Machine Learning problem
  • How to convert business problem into a Machine learning problem

Description

You're looking for a complete Machine Learning and Deep Learning course that can help you launch a flourishing career in the field of Data Science & Machine Learning, right?
You've found the right Machine Learning course!
After completing this course you will be able to:
· Confidently build predictive Machine Learning and Deep Learning models to solve business problems and create business strategy
· Answer Machine Learning related interview questions
· Participate and perform in online Data Analytics competitions such as Kaggle competitions
Check out the table of contents below to see what all Machine Learning and Deep Learning models you are going to learn.
How this course will help you?
A Verifiable Certificate of Completion is presented to all students who undertake this Machine learning basics course.
If you are a business manager or an executive, or a student who wants to learn and apply machine learning in Real world problems of business, this course will give you a solid base for that by teaching you the most popular techniques of machine learning.
Why should you choose this course?
This course covers all the steps that one should take while solving a business problem through linear regression.
Most courses only focus on teaching how to run the analysis but we believe that what happens before and after running analysis is even more important i.e. before running analysis it is very important that you have the right data and do some pre-processing on it. And after running analysis, you should be able to judge how good your model is and interpret the results to actually be able to help your business.
What makes us qualified to teach you?
The course is taught by Abhishek and Pukhraj. As managers in Global Analytics Consulting firm, we have helped businesses solve their business problem using machine learning techniques and we have used our experience to include the practical aspects of data analysis in this course
We are also the creators of some of the most popular online courses - with over 600,000 enrollments and thousands of 5-star reviews like these ones:
This is very good, i love the fact the all explanation given can be understood by a layman - Joshua
Thank you Author for this wonderful course. You are the best and this course is worth any price. - Daisy
Our Promise
Teaching our students is our job and we are committed to it. If you have any questions about the course content, practice sheet or anything related to any topic, you can always post a question in the course or send us a direct message.
Download Practice files, take Quizzes, and complete Assignments
With each lecture, there are class notes attached for you to follow along. You can also take quizzes to check your understanding of concepts. Each section contains a practice assignment for you to practically implement your learning.
Table of Contents
Section 1 - Python basic
This section gets you started with Python.
This section will help you set up the python and Jupyter environment on your system and it'll teach you how to perform some basic operations in Python. We will understand the importance of different libraries such as Numpy, Pandas & Seaborn.
Section 2 - R basic
This section will help you set up the R and R studio on your system and it'll teach you how to perform some basic operations in R.
Section 3 - Basics of Statistics
This section is divided into five different lectures starting from types of data then types of statistics then graphical representations to describe the data and then a lecture on measures of center like mean median and mode and lastly measures of dispersion like range and standard deviation
Section 4 - Introduction to Machine Learning
In this section we will learn - What does Machine Learning mean. What are the meanings or different terms associated with machine learning? You will see some examples so that you understand what machine learning actually is. It also contains steps involved in building a machine learning model, not just linear models, any machine learning model.
Section 5 - Data Preprocessing
In this section you will learn what actions you need to take step by step to get the data and then prepare it for the analysis these steps are very important. We start with understanding the importance of business knowledge then we will see how to do data exploration. We learn how to do uni-variate analysis and bivariate analysis then we cover topics like outlier treatment, missing value imputation, variable transformation and correlation.
Section 6 - Regression Model
This section starts with simple linear regression and then covers multiple linear regression.
We have covered the basic theory behind each concept without getting too mathematical about it so that you understand where the concept is coming from and how it is important. But even if you don't understand it,  it will be okay as long as you learn how to run and interpret the result as taught in the practical lectures.
We also look at how to quantify models accuracy, what is the meaning of F statistic, how categorical variables in the independent variables dataset are interpreted in the results, what are other variations to the ordinary least squared method and how do we finally interpret the result to find out the answer to a business problem.
Section 7 - Classification Models
This section starts with Logistic regression and then covers Linear Discriminant Analysis and K-Nearest Neighbors.
We have covered the basic theory behind each concept without getting too mathematical about it so that you
understand where the concept is coming from and how it is important. But even if you don't understand
it,  it will be okay as long as you learn how to run and interpret the result as taught in the practical lectures.
We also look at how to quantify models performance using confusion matrix, how categorical variables in the independent variables dataset are interpreted in the results, test-train split and how do we finally interpret the result to find out the answer to a business problem.
Section 8 - Decision trees
In this section, we will start with the basic theory of decision tree then we will create and plot a simple Regression decision tree. Then we will expand our knowledge of regression Decision tree to classification trees, we will also learn how to create a classification tree in Python and R
Section 9 - Ensemble technique
In this section, we will start our discussion about advanced ensemble techniques for Decision trees. Ensembles techniques are used to improve the stability and accuracy of machine learning algorithms. We will discuss Random Forest, Bagging, Gradient Boosting, AdaBoost and XGBoost.
Section 10 - Support Vector Machines
SVM's are unique models and stand out in terms of their concept. In this section, we will discussion about support vector classifiers and support vector machines.
Section 11 - ANN Theoretical Concepts
This part will give you a solid understanding of concepts involved in Neural Networks.
In this section you will learn about the single cells or Perceptrons and how Perceptrons are stacked to create a network architecture. Once architecture is set, we understand the Gradient descent algorithm to find the minima of a function and learn how this is used to optimize our network model.
Section 12 - Creating ANN model in Python and R
In this part you will learn how to create ANN models in Python and R.
We will start this section by creating an ANN model using Sequential API to solve a classification problem. We learn how to define network architecture, configure the model and train the model. Then we evaluate the performance of our trained model and use it to predict on new data. Lastly we learn how to save and restore models.
We also understand the importance of libraries such as Keras and TensorFlow in this part.
Section 13 - CNN Theoretical Concepts
In this part you will learn about convolutional and pooling layers which are the building blocks of CNN models.
In this section, we will start with the basic theory of convolutional layer, stride, filters and feature maps. We also explain how gray-scale images are different from colored images. Lastly we discuss pooling layer which bring computational efficiency in our model.
Section 14 - Creating CNN model in Python and R
In this part you will learn how to create CNN models in Python and R.
We will take the same problem of recognizing fashion objects and apply CNN model to it. We will compare the performance of our CNN model with our ANN model and notice that the accuracy increases by 9-10% when we use CNN. However, this is not the end of it. We can further improve accuracy by using certain techniques which we explore in the next part.
Section 15 - End-to-End Image Recognition project in Python and R
In this section we build a complete image recognition project on colored images.
We take a Kaggle image recognition competition and build CNN model to solve it. With a simple model we achieve nearly 70% accuracy on test set. Then we learn concepts like Data Augmentation and Transfer Learning which help us improve accuracy level from 70% to nearly 97% (as good as the winners of that competition).
Section 16 - Pre-processing Time Series Data
In this section, you will learn how to visualize time series, perform feature engineering, do re-sampling of data, and various other tools to analyze and prepare the data for models
Section 17 - Time Series Forecasting
In this section, you will learn common time series models such as Auto-regression (AR), Moving Average (MA), ARMA, ARIMA, SARIMA and SARIMAX.
By the end of this course, your confidence in creating a Machine Learning or Deep Learning model in Python and R will soar. You'll have a thorough understanding of how to use ML/ DL models to create predictive models and solve real world business problems.

Below is a list of popular FAQs of students who want to start their Machine learning journey-
What is Machine Learning?
Machine Learning is a field of computer science which gives the computer the ability to learn without being explicitly programmed. It is a branch of artificial intelligence based on the idea that systems can learn from data, identify patterns and make decisions with minimal human intervention.
Why use Python for Machine Learning?
Understanding Python is one of the valuable skills needed for a career in Machine Learning.
Though it hasn’t always been, Python is the programming language of choice for data science. Here’s a brief history:
    In 2016, it overtook R on Kaggle, the premier platform for data science competitions.
    In 2017, it overtook R on KDNuggets’s annual poll of data scientists’ most used tools.
    In 2018, 66% of data scientists reported using Python daily, making it the number one tool for analytics professionals.
Machine Learning experts expect this trend to continue with increasing development in the Python ecosystem. And while your journey to learn Python programming may be just beginning, it’s nice to know that employment opportunities are abundant (and growing) as well.
Why use R for Machine Learning?
Understanding R is one of the valuable skills needed for a career in Machine Learning. Below are some reasons why you should learn Machine learning in R
1. It’s a popular language for Machine Learning at top tech firms. Almost all of them hire data scientists who use R. Facebook, for example, uses R to do behavioral analysis with user post data. Google uses R to assess ad effectiveness and make economic forecasts. And by the way, it’s not just tech firms: R is in use at analysis and consulting firms, banks and other financial institutions, academic institutions and research labs, and pretty much everywhere else data needs analyzing and visualizing.
2. Learning the data science basics is arguably easier in R. R has a big advantage: it was designed specifically with data manipulation and analysis in mind.
3. Amazing packages that make your life easier. Because R was designed with statistical analysis in mind, it has a fantastic ecosystem of packages and other resources that are great for data science.
4. Robust, growing community of data scientists and statisticians. As the field of data science has exploded, R has exploded with it, becoming one of the fastest-growing languages in the world (as measured by StackOverflow). That means it’s easy to find answers to questions and community guidance as you work your way through projects in R.
5. Put another tool in your toolkit. No one language is going to be the right tool for every job. Adding R to your repertoire will make some projects easier – and of course, it’ll also make you a more flexible and marketable employee when you’re looking for jobs in data science.
What is the difference between Data Mining, Machine Learning, and Deep Learning?
Put simply, machine learning and data mining use the same algorithms and techniques as data mining, except the kinds of predictions vary. While data mining discovers previously unknown patterns and knowledge, machine learning reproduces known patterns and knowledge—and further automatically applies that information to data, decision-making, and actions.
Deep learning, on the other hand, uses advanced computing power and special types of neural networks and applies them to large amounts of data to learn, understand, and identify complicated patterns. Automatic language translation and medical diagnoses are examples of deep learning.
Who this course is for:

People pursuing a career in data science
Working Professionals beginning their Data journey
Statisticians needing more practical experience

Posted by free courses at November 08, 2022

Linear Regression and Logistic Regression in Python

Linear Regression and Logistic Regression in Python

Linear Regression and Logistic Regression in Python,Build predictive ML models with no coding or maths background. Linear Regression and Logistic Regression for beginners

Preview this Course

What you'll learn
  • Learn how to solve real life problem using the Linear and Logistic Regression technique
  • Preliminary analysis of data using Univariate and Bivariate analysis before running regression analysis
  • Understand how to interpret the result of Linear and Logistic Regression model and translate them into actionable insight
  • Indepth knowledge of data collection and data preprocessing for Linear and Logistic Regression problem
  • Basic statistics using Numpy library in Python
  • Data representation using Seaborn library in Python
  • Linear Regression technique of Machine Learning using Scikit Learn and Statsmodel libraries of Python

Requirements
  • This course starts from basics and you do not even need coding background to build these models in Python
  • Students will need to install Python and Anaconda software but we have a separate lecture to help you install the same
Description
You're looking for a complete Linear Regression and Logistic Regression course that teaches you everything you need to create a Linear or Logistic Regression model in Python, right?

You've found the right Linear Regression course!

After completing this course you will be able to:

Identify the business problem which can be solved using linear and logistic regression technique of Machine Learning.

Create a linear regression and logistic regression model in Python and analyze its result.

Confidently model and solve regression and classification problems

A Verifiable Certificate of Completion is presented to all students who undertake this Machine learning basics course.

What is covered in this course?

This course teaches you all the steps of creating a Linear Regression model, which is the most popular Machine Learning model, to solve business problems.

Below are the course contents of this course on Linear Regression:

Section 1 - Basics of Statistics

This section is divided into five different lectures starting from types of data then types of statistics

then graphical representations to describe the data and then a lecture on measures of center like mean

median and mode and lastly measures of dispersion like range and standard deviation

Section 2 - Python basic

This section gets you started with Python.

This section will help you set up the python and Jupyter environment on your system and it'll teach

you how to perform some basic operations in Python. We will understand the importance of different libraries such as Numpy, Pandas & Seaborn.

Section 3 - Introduction to Machine Learning

In this section we will learn - What does Machine Learning mean. What are the meanings or different terms associated with machine learning? You will see some examples so that you understand what machine learning actually is. It also contains steps involved in building a machine learning model, not just linear models, any machine learning model.

Section 4 - Data Preprocessing

In this section you will learn what actions you need to take a step by step to get the data and then

prepare it for the analysis these steps are very important.

We start with understanding the importance of business knowledge then we will see how to do data exploration. We learn how to do uni-variate analysis and bi-variate analysis then we cover topics like outlier treatment, missing value imputation, variable transformation and correlation.

Section 5 - Regression Model

This section starts with simple linear regression and then covers multiple linear regression.

We have covered the basic theory behind each concept without getting too mathematical about it so that you

understand where the concept is coming from and how it is important. But even if you don't understand

it,  it will be okay as long as you learn how to run and interpret the result as taught in the practical lectures.

We also look at how to quantify models accuracy, what is the meaning of F statistic, how categorical variables in the independent variables dataset are interpreted in the results, what are other variations to the ordinary least squared method and how do we finally interpret the result to find out the answer to a business problem.

By the end of this course, your confidence in creating a regression model in Python will soar. You'll have a thorough understanding of how to use regression modelling to create predictive models and solve business problems.



How this course will help you?

If you are a business manager or an executive, or a student who wants to learn and apply machine learning in Real world problems of business, this course will give you a solid base for that by teaching you the most popular techniques of machine learning, which is Linear Regression and Logistic Regregression

Why should you choose this course?

This course covers all the steps that one should take while solving a business problem through linear and logistic regression.

Most courses only focus on teaching how to run the analysis but we believe that what happens before and after running analysis is even more important i.e. before running analysis it is very important that you have the right data and do some pre-processing on it. And after running analysis, you should be able to judge how good your model is and interpret the results to actually be able to help your business.

What makes us qualified to teach you?

The course is taught by Abhishek and Pukhraj. As managers in Global Analytics Consulting firm, we have helped businesses solve their business problem using machine learning techniques and we have used our experience to include the practical aspects of data analysis in this course

We are also the creators of some of the most popular online courses - with over 150,000 enrollments and thousands of 5-star reviews like these ones:

This is very good, i love the fact the all explanation given can be understood by a layman - Joshua

Thank you Author for this wonderful course. You are the best and this course is worth any price. - Daisy

Our Promise

Teaching our students is our job and we are committed to it. If you have any questions about the course content, practice sheet or anything related to any topic, you can always post a question in the course or send us a direct message.

Download Practice files, take Quizzes, and complete Assignments

With each lecture, there are class notes attached for you to follow along. You can also take quizzes to check your understanding of concepts. Each section contains a practice assignment for you to practically implement your learning.



Go ahead and click the enroll button, and I'll see you in lesson 1!



Cheers

Start-Tech Academy



------------

Below is a list of popular FAQs of students who want to start their Machine learning journey-

What is Machine Learning?

Machine Learning is a field of computer science which gives the computer the ability to learn without being explicitly programmed. It is a branch of artificial intelligence based on the idea that systems can learn from data, identify patterns and make decisions with minimal human intervention.

What is the Linear regression technique of Machine learning?

Linear Regression is a simple machine learning model for regression problems, i.e., when the target variable is a real value.

Linear regression is a linear model, e.g. a model that assumes a linear relationship between the input variables (x) and the single output variable (y). More specifically, that y can be calculated from a linear combination of the input variables (x).

When there is a single input variable (x), the method is referred to as simple linear regression.

When there are multiple input variables, the method is known as multiple linear regression.

Why learn Linear regression technique of Machine learning?

There are four reasons to learn Linear regression technique of Machine learning:

1. Linear Regression is the most popular machine learning technique

2. Linear Regression has fairly good prediction accuracy

3. Linear Regression is simple to implement and easy to interpret

4. It gives you a firm base to start learning other advanced techniques of Machine Learning

How much time does it take to learn Linear regression technique of machine learning?

Linear Regression is easy but no one can determine the learning time it takes. It totally depends on you. The method we adopted to help you learn Linear regression starts from the basics and takes you to advanced level within hours. You can follow the same, but remember you can learn nothing without practicing it. Practice is the only way to remember whatever you have learnt. Therefore, we have also provided you with another data set to work on as a separate project of Linear regression.

What are the steps I should follow to be able to build a Machine Learning model?

You can divide your learning process into 4 parts:

Statistics and Probability - Implementing Machine learning techniques require basic knowledge of Statistics and probability concepts. Second section of the course covers this part.

Understanding of Machine learning - Fourth section helps you understand the terms and concepts associated with Machine learning and gives you the steps to be followed to build a machine learning model

Programming Experience - A significant part of machine learning is programming. Python and R clearly stand out to be the leaders in the recent days. Third section will help you set up the Python environment and teach you some basic operations. In later sections there is a video on how to implement each concept taught in theory lecture in Python

Understanding of Linear and Logistic Regression modelling - Having a good knowledge of Linear and Logistic Regression gives you a solid understanding of how machine learning works. Even though Linear regression is the simplest technique of Machine learning, it is still the most popular one with fairly good prediction ability. Fifth and sixth section cover Linear regression topic end-to-end and with each theory lecture comes a corresponding practical lecture where we actually run each query with you.

Why use Python for data Machine Learning?

Understanding Python is one of the valuable skills needed for a career in Machine Learning.

Though it hasn’t always been, Python is the programming language of choice for data science. Here’s a brief history:

    In 2016, it overtook R on Kaggle, the premier platform for data science competitions.

    In 2017, it overtook R on KDNuggets’s annual poll of data scientists’ most used tools.

    In 2018, 66% of data scientists reported using Python daily, making it the number one tool for analytics professionals.

Machine Learning experts expect this trend to continue with increasing development in the Python ecosystem. And while your journey to learn Python programming may be just beginning, it’s nice to know that employment opportunities are abundant (and growing) as well.



Who this course is for:
  • People pursuing a career in data science
  • Working Professionals beginning their Data journey
  • Statisticians needing more practical experience
  • Anyone curious to master Linear and Logistic Regression from beginner to advanced level in a short span of time

Posted by free courses at November 08, 2022
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