Complete Python Web Course: Build 8 Python Web Apps
Thursday, March 31, 2022
Free Coupon Discount - Complete Python Web Course: Build 8 Python Web Apps, Build Python Web Applications from Beginner to Expert using Python and Flask
Created by Jose Salvatierra Teclado by Jose Salvatierra
English [Auto], Portuguese [Auto], 1 more
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Description
The Complete Python Web Developer Course will lead you down a path of understanding and skill that may well, with work and patience, result in an income boost or a career change.
It is a one-stop-shop covering everything you need to start having ideas and creating Python web applications that engage visitors and provide them with value. In addition, I’ll always be available to help you further your learning and explore more avenues for success.
What do you have to do?
You’ll have immediate access to 8 carefully designed sections, each teaching and guiding you into creating a web application using Python: your challenge. I’ve created thorough, extensive, but easy to follow content which you’ll easily understand and absorb.
I recommend taking your time, as software development doesn’t happen overnight. Each section should take approximately one week, including developing the weekly challenge, reading around the subject, and practising further.
The course starts with the basics, including Python fundamentals, programming, and user interaction.
Then we will move onto how the internet works, making web requests and parsing webpages to get data from them using Python.
Now that you’ll have all the knowledge required, we’ll introduce our database of choice, MongoDB, and then proceed into creating our first Python web application: a blog where users can register and publish posts.
Then we will create a fantastic Python web application to notify you when prices of items in online stores go down; a really useful web app!
During all this, we’ll be learning about deploying our Python web applications, making it performing so it can scale to thousands of users, and usability and security issues.
Over the entire course you will learn:
Python
HTML
CSS
Responsive Design with Bootstrap
JavaScript
jQuery
MongoDB
Linux (UNIX)
APIs (both creating them and interacting with them)
Deployments to Heroku and DigitalOcean
What else will you get?
A friendly community to support you at all times
Personal contact with me: I’m always available to answer questions and help out
Lifetime access to course materials, even as more are released (and they are, very often!)
Hands-on learning to ensure you’re absorbing everything
A true understanding of the concepts of software development, design, and operations
By the time you’re done with the course you’ll have a fantastic set of fundamentals and extensive knowledge of Python and web development, which will allow you to easily continue learning and developing more and more advanced and engaging web applications.
It doesn’t matter how old you are or what you do professionally. I guarantee that anyone can benefit from learning web development and Python, but especially web application development.
So what are you waiting for? Sign up now, and I’ll see you on the inside!
100% Off Udemy Coupon . Free Udemy Courses . Online Classes
Computer Vision: Face Recognition Quick Starter in Python
Wednesday, March 30, 2022
Computer Vision: Face Recognition Quick Starter in Python - Quickly Build Python Deep Learning based Face Detection, Recognition, Emotion , Gender and Age Classification Systems
What you'll learn
- Face Detection from Images
- Face Detection from Realtime Videos
- Emotion Detection, Age-Gender Prediction
- Face Recognition from Images, Realtime Videos
Description
Hi There!
welcome to my new course 'Face Recognition with Deep Learning using Python'. This is the second course from my Computer Vision series.
Face Detection and Face Recognition is the most used applications of Computer Vision. Using these techniques, the computer will be able to extract one or more faces in an image or video and then compare it with the existing data to identify the people in that image.
Face Detection and Face Recognition is widely used by governments and organizations for surveillance and policing. We are also making use of it daily in many applications like face unlocking of cell phones etc.
This course will be a quick starter for people who wants to dive deep into face recognition using Python without having to deal with all the complexities and mathematics associated with typical Deep Learning process.
We will be using a python library called face-recognition which uses simple classes and methods to get the face recognition implemented with ease. We are also using OpenCV, Dlib and Pillow for python as supporting libraries.
Let's now see the list of interesting topics that are included in this course.
At first we will have an introductory theory session about Face Detection and Face Recognition technology.
After that, we are ready to proceed with preparing our computer for python coding by downloading and installing the anaconda package. Then we will install the rest of dependencies and libraries that we require including the dlib, face-recognition, opencv etc and will try a small program to see if everything is installed fine.
Most of you may not be coming from a python based programming background. The next few sessions and examples will help you get the basic python programming skill to proceed with the sessions included in this course. The topics include Python assignment, flow-control, functions and data structures.
Then we will have an introduction to the basics and working of face detectors which will detect human faces from a given media. We will try the python code to detect the faces from a given image and will extract the faces as separate images.
Then we will go ahead with face detection from a video. We will be streaming the real-time live video from the computer's webcam and will try to detect faces from it. We will draw rectangle around each face detected in the live video.
In the next session, we will customize the face detection program to blur the detected faces dynamically from the webcam video stream.
After that we will try facial expression recognition using pre-trained deep learning model and will identify the facial emotions from the real-time webcam video as well as static images
And then we will try Age and Gender Prediction using pre-trained deep learning model and will identify the Age and Gender from the real-time webcam video as well as static images
After face detection, we will have an introduction to the basics and working of face recognition which will identify the faces already detected.
In the next session, We will try the python code to identify the names of people and their the faces from a given image and will draw a rectangle around the face with their names on it.
Then, like as we did in face detection we will go ahead with face recognition from a video. We will be streaming the real-time live video from the computer's webcam and will try to identify and name the faces in it. We will draw rectangle around each face detected and beneath that their names in the live video.
Most times during coding, along with the face matching decision, we may need to know how much matching the face is. For that we will get a parameter called face distance which is the magnitude of matching of two faces. We will later convert this face distance value to face matching percentage using simple mathematics.
In the coming two sessions, we will learn how to tweak the face landmark points used for face detection. We will draw line joining these face land mark points so that we can visualize the points in the face which the computer is used for evaluation.
Taking the landmark points customization to the next level, we will use the landmark points to create a custom face make-up for the face image.
That's all about the topics which are currently included in this quick course. The code, images and libraries used in this course has been uploaded and shared in a folder. I will include the link to download them in the last session or the resource section of this course. You are free to use the code in your projects with no questions asked.
Also after completing this course, you will be provided with a course completion certificate which will add value to your portfolio.
So that's all for now, see you soon in the class room. Happy learning and have a great time.
Who this course is for:
- Beginners or who wants to start with Python based Face Recognition
Learn Streamlit Python
Learn Streamlit Python
Create Beautiful Data Apps and Machine Learning Web Apps In Python Faster with Streamlit
- Bestseller
Preview this Course
What you'll learn
- Learn the basics of Streamlit Framework
- Use Streamlit to create Machine Learning Web Apps and Data Apps
- Deploying Streamlit Python Web Applications
Requirements
- Basic understanding of Python programming language
- Understand Machine Learning Concepts in Python
- Determination and Desire to Learn New Things
Description
Are you having difficulties trying to build web applications for your data science projects? Do you spend more time trying to create a simple MVP app with your data to show your clients and others? Then let me introduce you to Streamlit - a python framework for building web apps.
Welcome to the coolest online resource for learning how to create Data Science Apps and Machine Learning Web Apps using the
awesome Streamlit Framework and Python.
This course will teach you Streamlit - the python framework that saves you from spending days and weeks in creating
data science and machine learning web applications.
In this course we will cover everything you need to know concerning streamlit such as
Fundamentals and the Basics of Streamlit ;
- Working with Text
- Working with Widgets (Buttons,Sliders,
- Displaying Data
- Displaying Charts and Plots
- Working with Media Files (Audio,Images,Video)
- Streamlit Layouts
- File Uploads
- Streamlit Static Components
Creating cool data visualization apps
How to Build A Full Web Application with Streamlit
By the end of this exciting course you will be able to
Build data science apps in hours not days
Productionized your machine learning models into web apps using streamlit
Build some cools and fun data apps
Deploy your streamlit apps using Docker,Heroku,Streamlit Share and more
Join us as we explore the world of building Data and ML Apps.
See you in the Course,Stay blessed.
Tips for getting through the course
Please write or code along with us do not just watch,this will enhance your understanding.
You can regulate the speed and audio of the video as you wish,preferably at -0.75x if the speed is too fast for you.
Suggested Prerequisites is understanding of Python
This course is about Streamlit an ML Framework to create data apps in hours not weeks. We will try our best to cover some concepts for the beginner and the pro .
Who this course is for:
- Beginner Python Developers curious about Streamlit
- Data Scientist and ML Engineers who want to productionized their Models faster
Python From Scratch & Selenium WebDriver QA Automation 2022
Tuesday, March 29, 2022
Python From Scratch & Selenium WebDriver QA Automation 2022
2 courses in 1. Python and Selenium WebDriver from scratch for Automation Testing, SQL Crash Course, Framework Design
Preview this Course
What you'll learn
- You will learn how to write Python programming language
- You will learn how to build test Framework for Front-end and Back-end automation
- You will learn how to write Selenium WebDriver scripts using the Python programming language
- Hands on training on Python Scripting will enable you to develop, understand and analyze scripts in Python
- You will learn SQL (Database Language) to read and write to database
- You will have good understanding of Selenium Web Automation Framework
- You will learn to build an E-Commerce site locally to practice testing
- You will generate html test reports with screen shots for failed tests
- You will have all the required skills and you will be confident to Automate any Web Application Tests using Selenium WebDriver and Python Scripting
- You will be confident during software Test Automation job interviews
- You will practice writing real tests on real E-Commerce site
Requirements
- Required basic understanding of computer and how to install and run software on your computer
- Good to have basic knowledge about HTML and Web Applications
- The desire to learn is all you need
Description
Attention all struggling Software Testers, Automation Testers, and Students who are aspired to take their careers to next level in Software Web Application Test Automation.
Have you been trying endlessly to learn Selenium WebDriver Test Automation Framework to automate tests for your Web Applications, but haven't had any luck?
Do you want to learn Python Scripting and struggle to start?
Do you want to take your software testing skills to the next level?
If you have answered YES to any of those questions, then you are at the right place…!!!
Here is one of the Best-selling courses on Udemy to learn Python scripting from scratch and to learn Web Application Test Automation using Selenium WebDriver and Python.
Unlike other courses, this course covers Python Scripting from scratch so even if you don’t know anything about Python scripting you can take this course. Hands-on training on Python Scripting and Selenium WebDriver will enable you to become a master in Web Application Test Automation.
This course is designed for Software Testers, Automation Testers, and even for Students who are aspired to take their career to next level by learning Web Application Test Automation using Selenium WebDriver and Python. This course includes the step-by-step guide to learn starting from installation of Python, IDE (PyCharm), and Selenium WebDriver.
Why I should take this course?
With over 28+ hours of videos and around 108 modules, you will get a great understanding of how to automate web applications tests using Selenium WebDriver and Python Scripting
Our aim is to make you understand Selenium WebDriver Framework and Python Scripting as quickly as possible
Unlike other courses, this course covers Python Scripting from scratch so even if you don’t know Python scripting you can take this course
You will have all the required skills and you will be confident to Automate any Web Application Tests using Selenium WebDriver and Python Scripting
Hands-on training on Python Scripting will enable you to develop, understand and analyze scripts in Python
After taking this course you will be confident to appear for job interviews for Software Test Automation profiles
You will be able to put your Python and/or Selenium code on GitHub and use it in your resume
You have lifetime access to this course and a 30-day satisfaction guaranteed with this course
Overview of the Course Contents –
Pythons Scripting - In the first half of this course you will have hands-on learning on Python Scripting, from the scratch. We will start with the installation and configuration of Python, PIP, and PyCharm and introduction to Python scripting. Then we will learn about variables in Python, different data types, control flow, conditional statements, exception handling, and functions in Python. We will understand all these points with examples. At the end of this section, you will be able to develop, understand, and analyze any Python script code.
Selenium WebDriver - In the second part of this course we will talk about Selenium WebDriver. This section will also start with an introduction and step-by-step installation of Selenium WebDriver. Which is an additional tool in Software Testing. Then we will cover how to run Web Automation test scripts on different browsers such as Chrome and Firefox. Next, we will talk about locating elements, basic actions, dealing with common elements, windows, and frames in detail. We will also learn how to deal with URLs, how to open ULR or links in a new window, and how to take screenshots. We will write working functions and run them against some well-known websites and watch WebDriver do its magic.
This is the course that could change your life.
After taking this course, you will become proficient in Web Application Test Automation using Selenium WebDriver with Python scripting. An investment in your software testing career is an investment in yourself. Don’t procrastinate. There is no time like the present to take charge of your software testing career. Take your Software Testing and Test Automation skills to the next level by taking this course!
You have 30 days money-back guarantee…!!!
And remember that once you purchase the course you will have lifetime access to the course and you have 30 days money-back guarantee if you do not like the course because of any reason. So, what are you waiting for? Go ahead enroll now.
See you inside the course…!!!
Who this course is for:
- This course is designed for Software Testers, Automation Testers and even for Students who want to learn Software Web Application Test Automation using Selenium WebDriver
- Unlike other courses this course covers Python Scripting from scratch, so even a beginner or novice can take this course
- This course is ideal for anyone who want to build and/or enhance their career in Test Automation
- Those who are looking to prepare Test Automation interviews can also take this course
Data Science Mega-Course: #Build {120-Projects In 120-Days}
Data Science Mega-Course: #Build {120-Projects In 120-Days}
Build & Deploy Data Science, Machine Learning, Deep Learning (Python, Flask, Django, AWS, Azure, GCP, Heruko Cloud)
- New
Preview this Course
What you'll learn
- Make powerful analysis, Make robust Machine Learning models
- Master Machine Learning on Python
- Know which Machine Learning model to choose for each type of problem
- Implement Machine Learning Algorithms
- Explore how to deploy your machine learning models.
- Understand the full product workflow for the machine learning lifecycle.
- Present Data Science projects to management
- Real life case studies and projects to understand how things are done in the real world
- Build a portfolio of work to have on your resume
Description
In This Course, Solve Business Problems Using Data Science Practically. Learn To Build & Deploy Machine Learning, Data Science, Artificial Intelligence, Auto Ml, Deep Learning, Natural Language Processing (Nlp) Web Applications Projects With Python (Flask, Django, Heroku, AWS, Azure, GCP, IBM Watson, Streamlit Cloud).
According to Glassdoor, the average salary for a Data Scientist is $117,345/yr. This is above the national average of $44,564. Therefore, a Data Scientist makes 163% more than the national average salary.
This makes Data Science a highly lucrative career choice. It is mainly due to the dearth of Data Scientists resulting in a huge income bubble.
Since Data Science requires a person to be proficient and knowledgeable in several fields like Statistics, Mathematics, and Computer Science, the learning curve is quite steep. Therefore, the value of a Data Scientist is very high in the market.
A Data Scientist enjoys a position of prestige in the company. The company relies on its expertise to make data-driven decisions and enable them to navigate in the right direction.
Furthermore, the role of a Data Scientist depends on the specialization of his employer company. For example – A commercial industry will require a data scientist to analyze their sales.
A healthcare company will require data scientists to help them analyze genomic sequences. The salary of a Data Scientist depends on his role and type of work he has to perform. It also depends on the size of the company which is based on the amount of data they utilize.
Still, the pay scale of Data scientists is way above other IT and management sectors. However, the salary observed by Data Scientists is proportional to the amount of work that they must put in. Data Science needs hard work and requires a person to be thorough with his/her skills.
Due to several lucrative perks, Data Science is an attractive field. This, combined with the number of vacancies in Data Science makes it an untouched gold mine. Therefore, you should learn Data Science in order to enjoy a fruitful career.
In This Course, We Are Going To Work On 120 Real World Projects Listed Below:
Project-1: Pan Card Tempering Detector App -Deploy On Heroku
Project-2: Dog breed prediction Flask App
Project-3: Image Watermarking App -Deploy On Heroku
Project-4: Traffic sign classification
Project-5: Text Extraction From Images Application
Project-6: Plant Disease Prediction Streamlit App
Project-7: Vehicle Detection And Counting Flask App
Project-8: Create A Face Swapping Flask App
Project-9: Bird Species Prediction Flask App
Project-10: Intel Image Classification Flask App
Project-11: Language Translator App Using IBM Cloud Service -Deploy On Heroku
Project-12: Predict Views On Advertisement Using IBM Watson -Deploy On Heroku
Project-13: Laptop Price Predictor -Deploy On Heroku
Project-14: WhatsApp Text Analyzer -Deploy On Heroku
Project-15: Course Recommendation System -Deploy On Heroku
Project-16: IPL Match Win Predictor -Deploy On Heroku
Project-17: Body Fat Estimator App -Deploy On Microsoft Azure
Project-18: Campus Placement Predictor App -Deploy On Microsoft Azure
Project-19: Car Acceptability Predictor -Deploy On Google Cloud
Project-20: Book Genre Classification App -Deploy On Amazon Web Services
Project 21 : DNA classification for finding E.Coli - Deploy On AWS
Project 22 : Predict the next word in a sentence. - AWS - Deploy On AWS
Project 23 : Predict Next Sequence of numbers using LSTM - Deploy On AWS
Project 24 : Keyword Extraction from text using NLP - Deploy On Azure
Project 25 : Correcting wrong spellings - Deploy On Azure
Project 26 : Music popularity classification - Deploy On Google App Engine
Project 27 : Advertisement Classification - Deploy On Google App Engine
Project 28 : Image Digit Classification - Deploy On AWS
Project 29 : Emotion Recognition using Neural Network - Deploy On AWS
Project 30 : Breast cancer Classification - Deploy On AWS
Project-31: Sentiment Analysis Django App -Deploy On Heroku
Project-32: Attrition Rate Django Application
Project-33: Find Legendary Pokemon Django App -Deploy On Heroku
Project-34: Face Detection Streamlit App
Project-35: Cats Vs Dogs Classification Flask App
Project-36: Customer Revenue Prediction App -Deploy On Heroku
Project-37: Gender From Voice Prediction App -Deploy On Heroku
Project-38: Restaurant Recommendation System
Project-39: Happiness Ranking Django App -Deploy On Heroku
Project-40: Forest Fire Prediction Django App -Deploy On Heroku
Project-41: Build Car Prices Prediction App -Deploy On Heroku
Project-42: Build Affair Count Django App -Deploy On Heroku
Project-43: Build Shrooming Predictions App -Deploy On Heroku
Project-44: Google Play App Rating prediction With Deployment On Heroku
Project-45: Build Bank Customers Predictions Django App -Deploy On Heroku
Project-46: Build Artist Sculpture Cost Prediction Django App -Deploy On Heroku
Project-47: Build Medical Cost Predictions Django App -Deploy On Heroku
Project-48: Phishing Webpages Classification Django App -Deploy On Heroku
Project-49: Clothing Fit-Size predictions Django App -Deploy On Heroku
Project-50: Build Similarity In-Text Django App -Deploy On Heroku
Project-51: Black Friday Sale Project
Project-52: Sentiment Analysis Project
Project-53: Parkinson’s Disease Prediction Project
Project-54: Fake News Classifier Project
Project-55: Toxic Comment Classifier Project
Project-56: IMDB Movie Ratings Prediction
Project-57: Indian Air Quality Prediction
Project-58: Covid-19 Case Analysis
Project-59: Customer Churning Prediction
Project-60: Create A ChatBot
Project-61: Video Game sales Analysis
Project-62: Zomato Restaurant Analysis
Project-63: Walmart Sales Forecasting
Project-64 : Sonic wave velocity prediction using Signal Processing Techniques
Project-65 : Estimation of Pore Pressure using Machine Learning
Project-66 : Audio processing using ML
Project-67 : Text characterisation using Speech recognition
Project-68 : Audio classification using Neural networks
Project-69 : Developing a voice assistant
Project-70 : Customer segmentation
Project-71 : FIFA 2019 Analysis
Project-72 : Sentiment analysis of web scrapped data
Project-73 : Determining Red Vine Quality
Project-74 : Customer Personality Analysis
Project-75 : Literacy Analysis in India
Project-76: Heart Attack Risk Prediction Using Eval ML (Auto ML)
Project-77: Credit Card Fraud Detection Using Pycaret (Auto ML)
Project-78: Flight Fare Prediction Using Auto SK Learn (Auto ML)
Project-79: Petrol Price Forecasting Using Auto Keras
Project-80: Bank Customer Churn Prediction Using H2O Auto ML
Project-81: Air Quality Index Predictor Using TPOT With End-To-End Deployment (Auto ML)
Project-82: Rain Prediction Using ML models & PyCaret With Deployment (Auto ML)
Project-83: Pizza Price Prediction Using ML And EVALML(Auto ML)
Project-84: IPL Cricket Score Prediction Using TPOT (Auto ML)
Project-85: Predicting Bike Rentals Count Using ML And H2O Auto ML
Project-86: Concrete Compressive Strength Prediction Using Auto Keras (Auto ML)
Project-87: Bangalore House Price Prediction Using Auto SK Learn (Auto ML)
Project-88: Hospital Mortality Prediction Using PyCaret (Auto ML)
Project-89: Employee Evaluation For Promotion Using ML And Eval Auto ML
Project-90: Drinking Water Potability Prediction Using ML And H2O Auto ML
Project-91: Image Editor Application With OpenCV And Tkinter
Project-92: Brand Identification Game With Tkinter And Sqlite3
Project-93: Transaction Application With Tkinter And Sqlite3
Project-94: Learning Management System With Django
Project-95: Create A News Portal With Django
Project-96: Create A Student Portal With Django
Project-97: Productivity Tracker With Django And Plotly
Project-98: Create A Study Group With Django
Project-99: Building Crop Guide Application with PyQt5, SQLite
Project-100: Building Password Manager Application With PyQt5, SQLite
Project-101: Create A News Application With Python
Project-102: Create A Guide Application With Python
Project-103: Building The Chef Web Application with Django, Python
Project-104: Syllogism-Rules of Inference Solver Web Application
Project-105: Building Vision Web Application with Django, Python
Project-106: Building Budget Planner Application With Python
Project-107: Build Tic Tac Toe Game
Project-108: Random Password Generator Website using Django
Project-109: Building Personal Portfolio Website Using Django
Project-110: Todo List Website For Multiple Users
Project-111: Crypto Coin Planner GUI Application
Project-112: Your Own Twitter Bot -python, request, API, deployment, tweepy
Project-113: Create A Python Dictionary Using python, Tkinter, JSON
Project-114: Egg-Catcher Game using python
Project-115: Personal Routine Tracker Application using python
Project-116: Building Screen -Pet using Tkinter & Canvas
Project-117: Building Caterpillar Game Using Turtle and Python
Project-118: Building Hangman Game Using Python
Project-119: Developing our own Smart Calculator Using Python and Tkinter
Project-120: Image-based steganography Using Python and pillows
Tip: Create A 60 Days Study Plan Or 120 Day Study Plan, Spend 1-3hrs Per Day, Build 120 Projects In 60 Days Or 120 Projects In 120 Days.
The Only Course You Need To Become A Data Scientist, Get Hired And Start A New Career
Note (Read This): This Course Is Worth Of Your Time And Money, Enroll Now Before Offer Expires.
Who this course is for:
- Beginners in data science
Ethereum Trading in 2022 + 99 Robots Every Month
Ethereum Trading in 2022 + 99 Robots Every Month
Choose the Top Performers from the 99 EAs/Robots in the Ethereum Trading Course: Cryptocurrency Algorithmic Trading
Preview this Course
What you'll learn
- Manage trading account with 99 Ethereum robots and select only the Top Expert Advisors
- Test strategies for 1 month on a demo account within few clicks using professional software
- Trade with 99 Ethereum trading robots in one trading account simultaneously to diversify the risk
- Manage a professional system for Ethereum trading using CFD trading and Expert Advisors
- You will know how to use the Meta Trader platform - free and trusted algorithmic platform
- You will be able to create your own strategies, as the process is shown in the course
- At the end of the course, you will learn how to maintain the top EAs in a separate/live account
- Achieve huge choice of trading strategies, as you will receive new 99 Robots every month
Requirements
- You should be able to use a PC at a beginner level and have a good Internet connection
- No previous trading experience is needed - just follow the steps in the course
- Have any PC, notebook, laptop, tablet or phone with Meta Trader Installed
- The desire to be a profitable trader with much of a knowledge
Description
Are you looking for profitable Ethereum trading strategies? With this course, you can choose among 99 strategies every month.
Petko Aleksandrov is a professional trader and Mentor at EA Forex Academy. He teaches algorithmic trading in his courses and shares his trading strategies. You will learn how he creates 100s of Expert Advisors – Robots for trading. More, you will be able to trade with the 99 Robots for Ethereum trading that he uses every month.
You will see how Petko tested the strategies for one month period with just a few clicks. Also, he will show you the whole process - from creating the strategies, up to trading with them.
You will learn to use the Meta Trader platform, which Petko considers "the only trusted platform for algorithmic trading."
Also, the Mentor will teach you how actually to place the 99 Expert Advisors over the chart and how to follow their performance and select only the Robots that profit from the current market conditions.
What will you learn in the course?
What does CFD stand for, and how Petko does algorithmic Ethereum trading
How to open a demonstrative/virtual account with a trading broker
How to recognize the Scam brokers and the steps to avoid those
How to export History data from the chosen broker, which is very important
You will learn how to manage the 99 Expert Advisors and choose the best ones
You will receive huge diversification and choice by adding new 99 EAs every month
"We need to be very flexible when trading - the Bitcoin costs are huge currently, and there are so many other assets to be traded, just like the Ethereum," says Petko in the course.
The Idea in the course
Petko teaches that the most common reason that makes the traders lose is the feelings - fear and greed. The fear of losing profit and trades are being closed too early. The greed to trade with bigger size after profitable trades.
Algorithmic trading solves this problem. The trader does not need to put any emotions because he does not decide when to buy or sell.
In the Ethereum trading course, you will see the results of 99 strategies trading together in one account. Petko is just following their performance and selecting the most profitable EAs. This way, no emotions are involved.
In the Ethereum trading course, you will receive the script Petko uses to export the History data from his broker. This is very important if you want to create your strategies or optimize the ones you will receive in the course.
Mr. Aleksandrov will introduce to you an informative lecture with the professional strategy builder EA Studio, which he uses to generate robust strategies and test them for one month period within a few clicks. The software provides him with the ability to create 100s of strategies and select only the top ones(if you decide to sign up, you can use a free trial version and practice)
Anyway, he will provide these strategies for you, and you will not need to go over this process.
Who is the Mentor?
After University, Petko Aleksandrov graduates the London Academy for trading in the United Kingdom. There he started developing his trading strategies with surprising profit. He was invited to stay as a trader and Mentor. Still, he decided to continue on his own with the algorithmic trading and especially the Forex and Cryptocurrency algorithmic trading, because he already knew that this was not the future but the present.
He created Trading Academy, where he teaches Algorithmic trading to avoid these feelings in his students and to teach them how to trade relaxed and calm. All he depends on is coding, formulas, statistics, and the hard work to create 100s of Expert Advisors and to select the ones that are currently profiting.
Petko has done that all, and he will share it with you-you will receive the 99 Robots included in the Ethereum trading course.
More, once enrolled in the course, you will get access to 99 Ethereum EAs he uses monthly. You can place the Expert Advisors on different demo accounts each month and have a massive choice of strategies. This way, you will be able to choose the most profitable EAs from few months.
Best of all, you will receive full support personally from Petko
If the course does not meet your expectations for some reason, you can use the 30-days money-back guarantee.
Enroll now, and move your algorithmic trading to the next level!
Enjoy the course!
Who this course is for:
- Traders that wish to spend less time in front of the trading screen
- Traders that are interested in Ethereum trading and wish to do algo trading
- Open-minded traders that wish to take the things in their hands
- Traders that want to manage a portfolio of 99 Robots and trade only the Top ones
Real World Auto Machine Learning Bootcamp: Build 14 Projects
Monday, March 28, 2022
Real World Auto Machine Learning Bootcamp: Build 14 Projects
Solve Data Science Problems Using Automated -ML, Learn To Use Eval ML, Pycaret, Auto Keras, Auto SK Learn, H20 Auto ML
- New
Preview this Course
What you'll learn
- Understand the full product workflow for the machine learning lifecycle.
- Write clean, maintainable and performant code
- Have a great intuition of many Auto Machine Learning models
- Master Machine Learning and use it on the job
- Learn to perform Classification and Regression modelling
Description
Automated machine learning (AutoML) represents a fundamental shift in the way organizations of all sizes approach machine learning and data science. Applying traditional machine learning methods to real-world business problems is time-consuming, resource-intensive, and challenging. It requires experts in several disciplines, including data scientists – some of the most sought-after professionals in the job market right now.
Automated machine learning changes that, making it easier to build and use machine learning models in the real world by running systematic processes on raw data and selecting models that pull the most relevant information from the data – what is often referred to as “the signal in the noise.” Automated machine learning incorporates machine learning best practices from top-ranked data scientists to make data science more accessible across the organization.
“Data science is the transformation of data using mathematics and statistics into valuable insights, decisions, and products”
As data science evolves and gains new “instruments” over time, the core business goal remains focused on finding useful patterns and yielding valuable insights from data. Today, data science is employed across a broad range of industries and aids in various analytical problems. For example, in marketing, exploring customer age, gender, location, and behavior allows for making highly targeted campaigns, evaluating how much customers are prone to make a purchase or leave. In banking, finding outlying client actions aids in detecting fraud. In healthcare, analyzing patients’ medical records can show the probability of having diseases, etc.
The data science landscape encompasses multiple interconnected fields that leverage different techniques and tools.
There’s a difference between data mining and very popular machine learning. Still, machine learning is about creating algorithms to extract valuable insights, it’s heavily focused on continuous use in dynamically changing environments and emphasizes adjustments, retraining, and updating of algorithms based on previous experiences. The goal of machine learning is to constantly adapt to new data and discover new patterns or rules in it. Sometimes it can be realized without human guidance and explicit reprogramming.
Machine learning is the most dynamically developing field of data science today due to a number of recent theoretical and technological breakthroughs. They led to natural language processing, image recognition, or even the generation of new images, music, and texts by machines. Machine learning remains the main “instrument” of building artificial intelligence.
Machine Learning Workflow
Generally, the workflow follows these simple steps:
Collect data. Use your digital infrastructure and other sources to gather as many useful records as possible and unite them into a dataset.
Prepare data. Prepare your data to be processed in the best possible way. Data preprocessing and cleaning procedures can be quite sophisticated, but usually, they aim at filling the missing values and correcting other flaws in data, like different representations of the same values in a column (e.g. December 14, 2016 and 12.14.2016 won’t be treated the same by the algorithm).
Split data. Separate subsets of data to train a model and further evaluate how it performs against new data.
Train a model. Use a subset of historic data to let the algorithm recognize the patterns in it.
Test and validate a model. Evaluate the performance of a model using testing and validation subsets of historic data and understand how accurate the prediction is.
Deploy a model. Embed the tested model into your decision-making framework as a part of an analytics solution or let users leverage its capabilities (e.g. better target your product recommendations).
Iterate. Collect new data after using the model to incrementally improve it.
Who this course is for:
- Beginners in machine learning
Modern Computer Vision™ PyTorch, Tensorflow2 Keras & OpenCV4
Modern Computer Vision™ PyTorch, Tensorflow2 Keras & OpenCV4
Using Python Learn OpenCV4, CNNs, Detectron2, YOLOv5, GANs, Tracking, Segmentation, Face Recognition & Siamese Networks
- Highest rated
Preview this Course
What you'll learn
- All major Computer Vision theory and concepts!
- Learn to use PyTorch, TensorFlow 2.0 and Keras for Computer Vision Deep Learning tasks
- OpenCV4 in detail, covering all major concepts with lots of example code
- All Course Code works in accompanying Google Colab Python Notebooks
- Learn all major Object Detection Frameworks from YOLOv5, to R-CNNs, Detectron2, SSDs, EfficientDetect and more!
- Deep Segmentation with U-Net, SegNet and DeepLabV3
- Understand what CNNs 'see' by Visualizing Different Activations and applying GradCAM
- Generative Adverserial Networks (GANs) & Autoencoders - Generate Digits, Anime Characters, Transform Styles and implement Super Resolution
- Training, fine tuning and analyzing your very own Classifiers
- Facial Recognition along with Gender, Age, Emotion and Ethnicity Detection
- Neural Style Transfer and Google Deep Dream
- Transfer Learning, Fine Tuning and Advanced CNN Techniques
- Important Modern CNNs designs like ResNets, InceptionV3, DenseNet, MobileNet, EffiicentNet and much more!
- Tracking with DeepSORT
- Siamese Networks, Facial Recognition and Analysis (Age, Gender, Emotion and Ethnicity)
- Image Captioning, Depth Estimination and Vision Transformers
- Point Cloud (3D data) Classification and Segmentation
- Making a Computer Vision API and Web App using Flask
Requirements
- No programming experience (some Python would be beneficial)
- Basic highschool mathematics
- A broadband internet connection
Description
Welcome to Modern Computer Vision™ Tensorflow, Keras & PyTorch!
AI and Deep Learning are transforming industries and one of the most intriguing parts of this AI revolution is in Computer Vision!
But what exactly is Computer Vision and why is it so exciting? Well, what if Computers could understand what they’re seeing through cameras or in images? The applications for such technology are endless from medical imaging, military, self-driving cars, security monitoring, analysis, safety, farming, industry, and manufacturing! The list is endless.
Job demand for Computer Vision workers are skyrocketing and it’s common that experts in the field are making $200,000+ USD salaries. However, getting started in this field isn’t easy. There’s an overload of information, many of which is outdated, and a plethora of tutorials that neglect to teach the foundations. Beginners thus have no idea where to start.
======================================================
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 replacing actors in films
======================================================
This course aims to solve all of that!
Taught using Google Colab Notebooks (no messy installs, all code works straight away)
27+ Hours of up-to-date and relevant Computer Vision theory with example code
Taught using both PyTorch and Tensorflow Keras!
In this course, you will learn the essential very foundations of Computer Vision, Classical Computer Vision (using OpenCV) I then move on to Deep Learning where we build our foundational knowledge of CNNs and learn all about the following topics:
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Detailed OpenCV Guide covering:
Image Operations and Manipulations
Contours and Segmentation
Simple Object Detection and Tracking
Facial Landmarks, Recognition and Face Swaps
OpenCV implementations of Neural Style Transfer, YOLOv3, SSDs and a black and white image colorizer
Working with Video and Video Streams
Our Comprehensive Deep Learning Syllabus includes:
Classification with CNNs
Detailed overview of CNN Analysis, Visualizing performance, Advanced CNNs techniques
Transfer Learning and Fine Tuning
Generative Adversarial Networks - CycleGAN, ArcaneGAN, SuperResolution, StyleGAN
Autoencoders
Neural Style Transfer and Google DeepDream
Modern CNN Architectures including Vision Transformers (ResNets, DenseNets, MobileNET, VGG19, InceptionV3, EfficientNET and ViTs)
Siamese Networks for image similarity
Facial Recognition (Age, Gender, Emotion, Ethnicity)
PyTorch Lightning
Object Detection with YOLOv5 and v4, EfficientDetect, SSDs, Faster R-CNNs,
Deep Segmentation - MaskCNN, U-NET, SegNET, and DeepLabV3
Tracking with DeepSORT
Deep Fake Generation
Video Classification
Optical Character Recognition (OCR)
Image Captioning
3D Computer Vision using Point Cloud Data
Medical Imaging - X-Ray analysis and CT-Scans
Depth Estimation
Making a Computer Vision API with Flask
And so much more
This is a comprehensive course, is broken up into two (2) main sections. This first is a detailed OpenCV (Classical Computer Vision tutorial) and the second is a detailed Deep Learning
======================================================
This course is filled with fun and cool projects including these Classical Computer Vision Projects:
Sorting contours by size, location, using them for shape matching
Finding Waldo
Perspective Transforms (CamScanner)
Image Similarity
K-Means clustering for image colors
Motion tracking with MeanShift and CAMShift
Optical Flow
Facial Landmark Detection with Dlib
Face Swaps
QR Code and Barcode Reaching
Background removal
Text Detection
OCR with PyTesseract and EasyOCR
Colourize Black and White Photos
Computational Photography with inpainting and Noise Removal
Create a Sketch of yourself using Edge Detection
RTSP and IP Streams
Capturing Screenshots as video
Import Youtube videos directly
======================================================
Deep Learning Computer Vision Projects:
PyTorch & Keras CNN Tutorial MNIST
PyTorch & Keras Misclassifications and Model Performance Analysis
PyTorch & Keras Fashion-MNIST with and without Regularisation
CNN Visualisation - Filter and Filter Activation Visualisation
CNN Visualisation Filter and Class Maximisation
CNN Visualisation GradCAM GradCAMplusplus and FasterScoreCAM
Replicating LeNet and AlexNet in Tensorflow2.0 using Keras
PyTorch & Keras Pretrained Models - 1 - VGG16, ResNet, Inceptionv3, MobileNetv2, SqueezeNet, WideResNet, DenseNet201, MobileMNASNet, EfficientNet and MNASNet
Rank-1 and Rank-5 Accuracy
PyTorch and Keras Cats vs Dogs PyTorch - Train with your own data
PyTorch Lightning Tutorial - Batch and LR Selection, Tensorboards, Callbacks, mGPU, TPU and more
PyTorch Lightning - Transfer Learning
PyTorch and Keras Transfer Learning and Fine Tuning
PyTorch & Keras Using CNN's as a Feature Extractor
PyTorch & Keras - Google Deep Dream
PyTorch Keras - Neural Style Transfer + TF-HUB Models
PyTorch & Keras Autoencoders using the Fashion-MNIST Dataset
PyTorch & Keras - Generative Adversarial Networks - DCGAN - MNIST
Keras - Super Resolution SRGAN
Project - Generate_Anime_with_StyleGAN
CycleGAN - Turn Horses into Zebras
ArcaneGAN inference
PyTorch & Keras Siamese Networks
Facial Recognition with VGGFace in Keras
PyTorch Facial Similarity with FaceNet
DeepFace - Age, Gender, Expression, Headpose and Recognition
Object Detection - Gun, Pistol Detector - Scaled-YOLOv4
Object Detection - Mask Detection - TensorFlow Object Detection - MobileNetV2 SSD
Object Detection - Sign Language Detection - TFODAPI - EfficientDetD0-D7
Object Detection - Pot Hole Detection with TinyYOLOv4
Object Detection - Mushroom Type Object Detection - Detectron 2
Object Detection - Website Screenshot Region Detection - YOLOv4-Darknet
Object Detection - Drone Maritime Detector - Tensorflow Object Detection Faster R-CNN
Object Detection - Chess Pieces Detection - YOLOv3 PyTorch
Object Detection - Hardhat Detection for Construction sites - EfficientDet-v2
Object DetectionBlood Cell Object Detection - YOLOv5
Object DetectionPlant Doctor Object Detection - YOLOv5
Image Segmentation - Keras, U-Net and SegNet
DeepLabV3 - PyTorch_Vision_Deeplabv3
Mask R-CNN Demo
Detectron2 - Mask R-CNN
Train a Mask R-CNN - Shapes
Yolov5 DeepSort Pytorch tutorial
DeepFakes - first-order-model-demo
Vision Transformer Tutorial PyTorch
Vision Transformer Classifier in Keras
Image Classification using BigTransfer (BiT)
Depth Estimation with Keras
Image Similarity Search using Metric Learning with Keras
Image Captioning with Keras
Video Classification with a CNN-RNN Architecture with Keras
Video Classification with Transformers with Keras
Point Cloud Classification - PointNet
Point Cloud Segmentation with PointNet
3D Image Classification CT-Scan
X-ray Pneumonia Classification using TPUs
Low Light Image Enhancement using MIRNet
Captcha OCR Cracker
Flask Rest API - Server and Flask Web App
Detectron2 - BodyPose
Who this course is for:
- College/University Students of all levels Undergrads to PhDs (very helpful for those doing projects)
- Software Developers and Engineers looking to transition into Computer Vision
- Start up founders lookng to learn how to implement thier big idea
- Hobbyist and even high schoolers looking to get started in Computer Vision
Reinforcement Learning & Deep RL Python(Theory & Projects)
Reinforcement Learning & Deep RL Python(Theory & Projects)
Reinforcement Learning: Deep Q-Learning, SARSA, Deep RL, with Car Racing and Trading Project and Project and Interview
- New
Preview this Course
What you'll learn
● The introduction and importance of Reinforcement & Deep Reinforcement Learning
● Practical explanation and live coding with Python
● Deep Reinforcement Learning applications
● Q-Learning using Python
● SARSA using Python
● Random Solutions using Python
● Hyper-parameters of Deep RL
● MDP
● Mini Project (Frozen Lake) using Python
● Open AI GYM
● Intro to Deep Learning
● Deep Learning Fundamentals
● Mini Project (CIFAR) using Pytorch
● Fundamentals of DQN
● Cart-Pole from Scratch Project using Python
● Stable Baseline 3
● Cart-Pole from Scratch Project using Stable Baseline 3
● Car Racing Game Project using Stable Baseline 3
● Trading Bot Project using Stable Baseline 3
● Interview Preparations
Requirements
● Prior knowledge of Python.
● An elementary understanding of programming.
● A willingness to learn and practice.
Description
Comprehensive Course Description:
Reinforcement Learning (RL) is a subset of machine learning. In the RL training method, desired actions are rewarded, and undesired actions are punished. In general, an RL agent can understand and interpret its environment, take actions, and also learn through trial and error.
Deep Reinforcement Learning (Deep RL) is also a subfield of machine learning. In Deep RL, intelligent machines and software are trained to learn from their actions in the same way that humans learn from experience. That is, Deep RL blends RL techniques with Deep Learning (DL) strategies.
Deep RL has the capability to solve complex problems that were unmanageable by machines in the past. Therefore, the potential applications of Deep RL in various sectors such as robotics, medicine, finance, gaming, smart grids, and more are enormous.
The phenomenal ability of Artificial Neural Networks (ANNs) to process unstructured information fast and learn like a human brain is starting to be exploited only now. We are only in the initial stages of seeing the full impact of the technology that combines the power of RL and ANNs. This latest technology has the potential to revolutionize every sphere of commerce and science.
How Is This Course Different?
In this detailed Learning by Doing course, each new theoretical explanation is followed by practical implementation. This course offers you the right balance between theory and practice. Six projects have been included in the course curriculum to simplify your learning. The focus is to teach RL and Deep RL to a beginner. Hence, we have tried our best to simplify things.
The course ‘A Complete Guide to Reinforcement & Deep Reinforcement Learning’ reflects the most in-demand workplace skills. The explanations of all the theoretical concepts are clear and concise. The instructors lay special emphasis on complex theoretical concepts, making it easier for you to understand them. The pace of the video presentation is neither fast nor slow. It’s perfect for learning. You will understand all the essential RL and Deep RL concepts and methodologies. The course is:
• Simple and easy to learn.
• Self-explanatory.
• Highly detailed.
• Practical with live coding.
• Up-to-date covering the latest knowledge of this field.
As this course is an exhaustive compilation of all the fundamental concepts, you will be motivated to learn RL and Deep RL. Your learning progress will be quick. You are certain to experience much more than what you learn. At the end of each new concept, a revision task such as Homework/activity/quiz is assigned. The solutions for these tasks are also provided. This is to assess and promote your learning. The whole process is closely linked to the concepts and methods you have already learned. A majority of these activities are coding-based, as the goal is to prepare you for real-world implementations.
In addition to high-quality video content, you will also get access to easy-to-understand course material, assessment questions, in-depth subtopic notes, and informative handouts in this course. You are welcome to contact our friendly team in case of any queries related to the course, and we assure you of a prompt response.
The course tutorials are subdivided into 145+ short HD videos. In every video, you’ll learn something new and fascinating. In addition, you’ll learn the key concepts and methodologies of RL and Deep RL, along with several practical implementations. The total runtime of the course videos is 14+ hours.
Why Should You Learn RL & Deep RL?
RL and Deep RL are the hottest research topics in the Artificial Intelligence universe.
Reinforcement learning (RL) is a subset of machine learning concerned with the actions that intelligent agents need to take in an environment in order to maximize the reward. RL is one of three essential machine learning paradigms, besides supervised learning and unsupervised learning.
Let’s look at the next hot research topic.
Deep Reinforcement Learning (Deep RL) is a subset of machine learning that blends Reinforcement Learning (RL) and Deep Learning (DL). Deep RL integrates deep learning into the solution, permitting agents to make decisions from unstructured input data without human intervention. Deep RL algorithms can take in large inputs (e.g., every pixel rendered to the user’s screen in a video game) and determine the best actions to perform to optimize an objective (e.g., attain the maximum game score).
Deep RL has been used for an assortment of applications, including but not limited to video games, oil & gas, natural language processing, computer vision, retail, education, transportation, and healthcare.
Course Content:
The comprehensive course consists of the following topics:
1. Introduction
a. Motivation
i. What is Reinforcement Learning?
ii. How is it different from other Machine Learning Frameworks?
iii. History of Reinforcement Learning
iv. Why Reinforcement Learning?
v. Real-world examples
vi. Scope of Reinforcement Learning
vii. Limitations of Reinforcement Learning
viii. Exercises and Thoughts
b. Terminologies of RL with Case Studies and Real-World Examples
i. Agent
ii. Environment
iii. Action
iv. State
v. Transition
vi. Reward
vii. Quiz/Solution
viii. Policy
ix. Planning
x. Exercises and Thoughts
2. Hands-on to Basic Concepts
a. Naïve/Random Solution
i. Intro to game
ii. Rules of the game
iii. Setups
iv. Implementation using Python
b. RL-based Solution
i. Intro to Q Table
ii. Dry Run of states
iii. How RL works
iv. Implementing RL-based solution using Python
v. Comparison of solutions
vi. Conclusion
3. Different types of RL Solutions
a. Hyper Parameters and Concepts
I. Intro to Epsilon
II. How to update epsilon
III. Quiz/Solution
IV. Gamma, Discount Factor
V. Quiz/Solution
VI. Alpha, Learning Rate
VII. Quiz/Solution
VIII. Do’s and Don’ts of Alpha
IX. Q Learning Equation
X. Optimal Value for number of Episodes
XI. When to Stop Training
b. Markov Decision Process
i. Agent-environment interaction
ii. Goals
iii. Returns
iv. Episodes
v. Value functions
vi. Optimization of policy
vii. Optimization of the value function
viii. Approximations
ix. Exercises and Thoughts
c. Q-Learning
i. Intro to QL
ii. Equation Explanation
iii. Implementation using Python
iv. Off-Policy Learning
d. SARSA
i. Intro to SARSA
ii. State, Action, Reward, State, Action
iii. Equation Explanation
iv. Implementation using Python
v. On-Policy Learning
e. Q-Learning vs. SARSA
i. Difference in Equation
ii. Difference in Implementation
iii. Pros and Cons
iv. When to use SARSA
v. When to use Q Learning
vi. Quiz/Solution
4. Mini Project Using the Above Concepts (Frozen Lake)
a. Intro to GYM
b. Gym Environment
c. Intro to Frozen Lake Game
d. Rules
e. Implementation using Python
f. Agent Evaluation
g. Conclusion
5. Deep Learning/Neural Networks
a. Deep Learning Framework
i. Intro to Pytorch
ii. Why Pytorch?
iii. Installation
iv. Tensors
v. Auto Differentiation
vi. Pytorch Practice
b. Architecture of DNN
i. Why DNN?
ii. Intro to DNN
iii. Perceptron
iv. Architecture
v. Feed Forward
vi. Quiz/Solution
vii. Activation Function
viii. Loss Function
ix. Gradient Descent
x. Weight Initialization
xi. Quiz/Solution
xii. Learning Rate
xiii. Batch Normalization
xiv. Optimizations
xv. Dropout
xvi. Early Stopping
c. Implementing DNN for CIFAR Using Python
6. Deep RL / Deep Q Network (DQN)
a. Getting to DQN
i. Intro to Deep Q Network
ii. Need of DQN
iii. Basic Concepts
iv. How DQN is related to DNN
v. Replay Memory
vi. Epsilon Greedy Strategy
vii. Quiz/Solution
viii. Policy Network
ix. Target Network
x. Weights Sharing/Target update
xi. Hyper-parameters
b. Implementing DQN
i. DQN Project – Cart and Pole using Pytorch
ii. Moving Averages
iii. Visualizing the agent
iv. Performance Evaluation
7. Car Racing Project
a. Intro to game
b. Implementation using DQN
8. Trading Project
a. Stable Baseline
b. Trading Bot using DQN
9. Interview Preparation
Successful completion of this course will enable you to:
● Relate the concepts and practical applications of Reinforcement and Deep Reinforcement Learning with real-world problems.
● Apply for the jobs related to Reinforcement and Deep Reinforcement Learning.
● Work as a freelancer for jobs related to Reinforcement and Deep Reinforcement Learning.
● Implement any project that requires Reinforcement and Deep Reinforcement Learning knowledge from scratch.
● Extend or improve the implementation of any other project for performance improvement.
● Know the theory and practical aspects of Reinforcement and Deep Reinforcement Learning.
Who this course is for:
● Beginners who know absolutely nothing about Reinforcement and Deep Reinforcement Learning.
● People who want to develop intelligent solutions.
● People who love to learn the theoretical concepts first before implementing them using Python.
● People who want to learn PySpark along with its implementation in realistic projects.
● Machine Learning or Deep Learning Lovers.
● Anyone interested in Artificial Intelligence.
Who this course is for:
● Beginners who know absolutely nothing about Reinforcement and Deep Reinforcement Learning.
● People who want to develop intelligent solutions.
● People who love to learn the theoretical concepts first before implementing them using Python.
50 Days React Bootcamp: Build 50 Real World React Projects
Sunday, March 27, 2022
50 Days React Bootcamp: Build 50 Real World React Projects
Learn React Programming: Develop Web Applications Using Socket, REST APIs, Firebase, React Hooks, Bootstrap, React.js
- Hot & new
Preview this Course
What you'll learn
- Build powerful, fast, user-friendly and reactive web app
- Provide amazing user experiences by leveraging the power of JavaScript with ease
- Learn all about React Hooks and React Components
- Assemble incredibly reusable React components
- Learn to use React Hooks for building functional components
- Create portfolio of real- world projects on one of the most in-demand web development technologies
- See the step-by-step process of designing and assembling an advanced project
Description
React uses declarative instead of imperative syntax. It’s a simpler way of developing apps, and you can learn why here.
Basically, React is faster to develop with because you don’t need to tell the app how to represent the state — you just need to say what you’d like to happen. It’s quick, it’s easy, and there’s less room for human error.
You may have heard of the phrase “Write once, run anywhere” before. React Native brings that kind of philosophy to React with “Learn once, write anywhere”.
Once you understand the basic architecture and thinking behind React, you’ll be able to develop fully functioning apps for both Android and iOS. You won’t have to learn two different ways to represent your app. So after you learn React, you can bring your new product to users not just as quickly as possible, but as widely as possible.
React JS is an open-source, component-based Javascript library, which is used to build UI specifically for single-page applications. It’s one of the most popular Javascript libraries used to build apps front-end right now. Facebook developers created ReactJS in 2011 and used first in the Facebook app, and until today it has a huge community supporting it and lots of resources to learn it.
In This Course Learn React Programming Practically, Develop Real World Web Applications Using Socket, REST APIs, Firebase, React Hooks, Bootstrap, React.js, Webpack , HTML5 , CSS3 , React- Router.
React js developer: salaries per region
New York -$142,350
Georgia -$135,000
New Jersey -$131,625
California -$130,000
Illinois -$126,750
Arizona -$121,875
Texas -$117,000
In This Course, We Are Going To Work On 50 Real World React Development Projects Listed Below:
Project-1 : E-Commerce (Amazon Clone) - React, React-Context-Api, Firebase
Project-2 : Chat Application - React, React Chat engine, Socket, Rest Apis
Project-3 : Movies Application (Ott) - React, Omdb Api, React Hooks, Bootstrap
Project-4 : Video Sharing Website - React, Youtube Api, Material-Ui, Axios
Project-5 : Todolist Website - React, Material-Ui, React Hooks, State Management
Project-6 : Blog Website - React, Material-Ui, React Hooks, State Management, Gnews Api
Project-7 : Social Networking Website - Material-Ui, React Hooks, State Management, Google Oauth, Security And Authentication
Project-8 : Resume Website (Portfolio) - React, Material-Ui, React Hooks, State Management--
Project-9 : An Emoji Search Application Made With React - React, Reactdom, Html5, Css3
Project-10 : A Breaking Bad Character Application Made With React - React, Reactdom, Html5, Scss, React Context
Project-11 : A Random Quote Generator Application Made With React And Deployed On Heroku
Project-12 : A Password Generator Application Made With React - React, Reactdom, Html5, Css
Project-13 : A Quiz App Made With React - React, Reactdom, Html5, Scss
Project-14 : UnSplash Image Gallery Application made with React -ReactDOM, React Hooks
Project-15 : A React Router Demo Application made with ReactJs -React Js , NPM , CSS , React Router Dom , Context , React Hooks
Project-16 : A Book Shelf Application made with React- React Js , NPM , CSS , State Management
Project-17 : A Note Taking Application Made with React and Redux-React Js , NPM , CSS , State Management , Redux , React-Redux , CRA
Project-18 : A React Contact Register Application-React Js , NPM , CSS , State Management , React Context , Hooks
Project-19 : A Spend Money App made with React-React Js , NPM , CSS , State Management , React Context , Hooks
Project-20 : A Resort Booking Application made with React-React Js , NPM , CSS , State Management , Hooks , CRA
Project-21 : A Body Mass Index Calculator made with React-React Js , NPM , CSS , State Management , React Context , Hooks , Context
Project-22 : A Furniture Store made with React and Redux-React Js , NPM , CSS , State Management , React Context , Hooks
Project-23 : A Scoreboard Application made with ReactJS-React Js , NPM , CSS , State Management , React Context , Hooks
Project-24 : Meta Tag Generator Application made with React-React Js , NPM , CSS , State Management , React Context , Hooks
Project-25 : Food Ordering Web Application - ReactJs, Material-Ui, React Router, Css
Project-26 : Weather Web Application Using Api - ReactJs, Open Weather Api, Css
Project-27 : Food Recipe Application - ReactJs, Edamam Api, Firebase For Hosting, Css
Project-28 : My Cart Application - ReactJs, React-Hooks, External Api, Css
Project-29 : Cafe Menu Application - ReactJs, React-Hooks , Firebase, Css
Project-30 : Cocktail Hub Web Application - ReactJs, React-Hooks, External Api, Context Api, Css
Project-31 : Review Posting Application - ReactJs, Material-Ui, Css, Nanoid
Project-32 : Pomodoro Application - ReactJs, React Countdown Timer Npm Package, Css
Project-33 : Google Search Application Using Api - ReactJs, Tailwind Css, Google Search Api, React Router Dom, React Player--
Project-34 : Tic-Tac-Toe game with React js
Project-35 : Word and letter counter apllication using react js, including useState hooks and pure javascript.
Project-36 : Currency converter using React js
Project-37 : Speech Recogitio, Voice assistant app using react js
Project-38 : How to build a Calculator using react js, with hooks
Project-39 : Build a budget app using react js, hooks, custom hooks, react-contex concept
Project-40 : Music Player using react js (Spotify clone) using react js with proper user interface using official spotify api
Project-41 : Calender Application with scheduling events functionality
Project-42 : Dictionary App using react js
Project-43 : Youtube Clone using react js
Project-44 : Canndy Crush game usinng react js
Project-45 : Astronomy stuff of the day Using ReactJs CSS , react hooks, APOD API of NASA
Project-46 : Rock paper Scissors using Reactjs , CSS, React hooks
Project-47 : Realtime Notification app Using ReactJs , CSS, React hooks , Socket
Project-48 : Covid-19 tracker Application Using ReactJs , CSS , React hooks
Project-49 : Random gif generator app using ReactJs , React Hooks , CSS , API , asynchronous javascript
Project-50 : Wildfire tracker App USing Reactjs, CSS, asynchronous js, NASA open API
Note (Read This): This Course Is Worth Of Your Time And Money, Enroll Now Before Offer Expires.
Who this course is for:
- Beginners In React
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