Python OOP – Object Oriented Programming for Beginners

What you’ll learn
  • The principles of Object Oriented Programming (OOP) using Python.
  • How the elements of Object Oriented Programming work behind the scenes.
  • Understand how classes work and how you can create instances from classes.
  • Implement and call methods. Understand their purpose within classes.
  • Define instance attributes and class attributes. Learn their differences.
  • Use ‘self’ to refer to instances. Learn how it works behind the scenes.
  • Work with inheritance to reuse code, improve design, and avoid repetition.
  • Practice key aspects of OOP such as Docstrings and Special Methods.
  • Basic Python and programming knowledge (data types, variables, functions, conditionals, and loops).
  • Basic knowledge of lists, tuples, and dictionaries is required to complete the mini projects.
  • Python 3 and Python Shell or a Python IDE installed on your device.
  • Coding exercises can be submitted directly on the browser.

Learn Object Oriented Programming (OOP) in Python with mini projects, hands-on practice, and carefully designed visual explanations. Understand how the elements and abstract concepts of OOP work behind the scenes. Apply your knowledge to new scenarios.

Object Oriented Programming is Your Next Step Into the In-demand and Powerful World of Python

  • Create and work with classes and instances to write Python programs.
  • Understand how they work behind the scenes.
  • Learn when to use instance attributes vs. class attributes.
  • Implement and call methods. Understand their purpose.
  • Take advantage of the power of inheritance to avoid code repetition.
  • Use key concepts like Docstrings and Special methods in your programs.


Student Reviews:

“Even total noob will understand the OOP here :)” — Łukasz Bryzek

“I love how the instructor explains things in simple language and relates it to everyday life. I actually understand! I’m just at the beginning of the course and am excited about continuing.” — Donny Lobree

“I would strongly recommend this course if you are new to programming and want to master the basics of OOP.” — Demayne Collins

“The learning sticks. The coverage of Python classes here, for me, has been better than how this topic has been covered in a couple of other Python classes I have from more well-established Python instructors. Hats of to the instructor for putting this together.” — Thomas P

“Perfect for persons without any clue in oop!” — Patrick Onegin


Add New Python Skills To Your Resume

Python is currently one of the most popular programming languages and its popularity continues rising every year. It is used for real-world applications in diverse areas such as Data Science, Game Development, Web Development, Machine Learning, Artificial Intelligence, and many more. Learning Object Oriented Programming in Python is your next step into the powerful world of computer science.

Object Oriented Programming is key if you wish to expand your computer science skills and create maintainable and scalable programs. You will need to learn these concepts to implement data structures like trees, graphs, and linked lists. It’s also key for game development, GUI programming, artificial intelligence, and many other areas. The concepts and techniques that you will learn in this course are easily transferable to other programming languages like Java, JavaScript, and many more.

Content & Overview

With high-quality video lectures that include graphics and animations, you will learn and work with these concepts:

  • Classes
  • Instances
  • Instance Attributes
  • Class Attributes
  • Methods
  • The ‘self’ parameter
  • Inheritance
  • Docstrings
  • Special Methods
  • …. and more!

You will create a mid-term course project, a text-based version of the Blackjack game following the principles of Object Oriented Programming.

Learning Material & Resources

Throughout the course, you will find these resources:

  • Video lectures: carefully designed graphics, animations, and explanations.
  • Mid-term Course Project: you will create a text-based version of the Blackjack game using the principles of Object Oriented Programming.
  • Mini Projects: apply your knowledge at the end of each section with these mini projects.
  • Solutions to the Mini Projects: each mini project has its corresponding solution.
  • PDF Handouts: unique study guides with a graphical summary of the key aspects of each section.
  • Coding Exercises: practice key concepts with the coding exercises.
  • Solutions to the Coding Exercises: each coding exercise has its corresponding solution.
  • Quizzes: check your knowledge interactively after each lecture with short quizzes that have unlimited attempts.
  • PDF Slides: download the slides used in each section as a pdf file. Take your learning with you anywhere you go.
  • Python Files: download the code used for each section as a compressed (.zip) file with individual python (.py) files.
  • Articles: read complementary articles to expand your knowledge.
  • Capstone Project: apply your knowledge in a final capstone project.
  • Discussion Forums: ask questions on the discussion forums and discuss interesting topics with your peers.

Why is this course unique?

This course is unique because of its emphasis on providing visual and detailed explanations of how the elements of Object Oriented Programming (OOP) work behind the scenes, so you will not only learn how to use them in Python, you will actually understand what each line of code does behind the scenes.

During the course, you will apply your knowledge by completing mini projects that simulate simplified real-world scenarios such as fixing classes in a bakery system, representing bacteria for educational software using instance attributes, implementing inheritance for a videogame, completing the system of a vending machine, and many more. Each mini project includes its corresponding solution.

Unique study materials complement the course experience. You will find PDF handouts specifically designed for the course with a graphical summary of the key aspects of each section.

You will solve coding exercises directly on the browser and you will receive instant feedback for your submission.

You will check your knowledge with short Quizzes after each main lecture. The Quizzes provide instant feedback, so you can see the correct answer immediately. The quiz questions were designed to make you think more deeply about the topics presented.

You will receive a certificate of completion that you can add to your social media profiles to showcase your new skills.

You will also have lifetime access to the course and to all the new additions.


You are very welcome to watch the preview lectures and check out the full course curriculum.

If you are looking for an engaging, visual, and practical course, you’ve found it.

Add “Object Oriented Programming in Python” to your resume and showcase your new skills!

Who this course is for:
  • New developers who know the basics of Python and would like to expand their knowledge.
  • Developers and Students who want to learn how to work with Object Oriented Programming.
  • Self-taught developers who wish to dive into the world of Object Oriented Programming from the basics.
  • Programmers who need to refresh their knowledge on this topic.

Take Course

Django Tutorials Python Python Books

Machine Learning with Python Cookbook Practical Solutions from Preprocessing to Deep Learning

What you’ll learn

Over the last few years machine learning has become embedded in a wide variety of day-to-day business, nonprofit, and government operations. As the popularity of machine learning increased, a cottage industry of high-quality literature that taught applied machine learning to practitioners developed. This literature has been highly successful in training an entire generation of data scientists and machine learning engineers. This literature also approached the topic of machine learning from the perspective of providing a learning resource to teach an individual what machine learning is and how it works. However, while fruitful, this approach left out a different perspective on the topic: the nuts and bolts of doing machine learning day to day. That is the motivation of this book—not as a tome of machine learning knowledge for the student but as a wrench for the professional, to sit with dog-eared pages on desks ready to solve the practical day-to-day problems of a machine learning practitioner

More specifically, the book takes a task-based approach to machine learning, with almost 200 self-contained solutions (you can copy and paste the code and it’ll run) for the most common tasks a data scientist or machine learning engineer building a model will run into.

The ultimate goal is for the book to be a reference for people building real machine learning systems. For example, imagine a reader has a JSON file containing 1,000 categorical and numerical features with missing data and categorical target vectors with imbalanced classes, and wants an interpretable model. The motivation for this book is to provide recipes to help the reader learn processes such as:

  • 2.5 Loading a JSON file
  • 4.2 Standardizing a Feature
  • 5.3 Encoding Dictionaries of Features
  • 5.4 Imputing Missing Class Values
  • 9.1 Reducing Features Using Principal Components
  • 12.2 Selecting Best Models Using Randomized Search
  • 14.4 Training a Random Forest Classifier
  • 14.7 Selecting Random Features in Random Forests

The goal is for the reader to be able to:

1. Copy/paste the code and gain confidence that it actually works with the included toy datasets.

2. Read the discussion to gain an understanding of the theory behind the technique the code is executing and learn which parameters are important to consider.

3. Insert/combine/adapt the code from the recipes to construct the actual application.

Who This Book Is For

This book is not an introduction to machine learning. If you are not comfortable with the basic concepts of machine learning or have never spent time learning machine learning, do not buy this book. Instead, this book is for the machine learning practitioner who, while comfortable with the theory and concepts of machine learning, would benefit from a quick reference containing code to solve challenges he runs into working on machine learning on an everyday basis.

This book assumes the reader is comfortable with the Python programming language and package management.

  Who This Book Is Not For

As stated previously, this book is not an introduction to machine learning. This book should not be your first. If you are unfamiliar with concepts like cross-validation, random forest, and gradient descent, you will likely not benefit from this book as much as one of the many high-quality texts specifically designed to introduce you to the topic. I recommend reading one of those books and then coming back to this book to learn working, practical solutions for machine learning.

Terminology Used in This Book

Machine learning draws upon techniques from a wide range of fields, including computer science, statistics, and mathematics. For this reason, there is significant variation in the terminology used in the discussions of machine learning:

MATLAB Tutorials Python Udemy Courses

Differential Equations Tutorial – Runge-Kutta Method in Python and MATLAB

What you’ll learn

  • Implementation of Runge-Kutta in Python
  • Implementation of Runge-Kutta in MATLAB
  • Solving System of Nonlinear Differential Equations
  • Simulation of a Lotka-Volterra (Predator-Prey) System


  • Python and/or MATLAB Programming
  • Differential Equations


In this video tutorial, the theory of Runge-Kutta Method (RK4) for numerical solution of ordinary differential equations (ODEs), is discussed and then implemented using MATLAB and Python from scratch. As an example, the well-know Lotka-Volterra model (aka. the Predator-Prey model) is numerically simulated and solved using Runge-Kutta 4th order (RK4), in both languages, Python and MATLAB.

MATLAB Tutorials Python Python Courses Udemy Courses

Signal processing problems, solved in MATLAB and in Python

Applications-oriented instruction on signal processing and digital signal processing (DSP) using MATLAB and Python codes

What you’ll learn

  • Understand commonly used signal processing tools
  • Design, evaluate, and apply digital filters
  • Clean and denoise data
  • Know what to look for when something isn’t right with the data or the code
  • Improve MATLAB or Python programming skills
  • Know how to generate test signals for signal processing methods
  • *Fully manually corrected English captions!


  • Basic programming experience in MATLAB or Python
  • High-school mathn


Why you need to learn digital signal processing.

Nature is mysterious, beautiful, and complex. Trying to understand nature is deeply rewarding, but also deeply challenging. One of the big challenges in studying nature is data analysis. Nature likes to mix many sources of signals and many sources of noise into the same recordings, and this makes your job difficult.

Therefore, one of the most important goals of time series analysis and signal processing is to denoise: to separate the signals and noises that are mixed into the same data channels.

The big idea of DSP (digital signal processing) is to discover the mysteries that are hidden inside time series data, and this course will teach you the most commonly used discovery strategies.

What’s special about this course?

The main focus of this course is on implementing signal processing techniques in MATLAB and in Python. Some theory and equations are shown, but I’m guessing you are reading this because you want to implement DSP techniques on real signals, not just brush up on abstract theory.

The course comes with over 10,000 lines of MATLAB and Python code, plus sample data sets, which you can use to learn from and to adapt to your own coursework or applications.

In this course, you will also learn how to simulate signals in order to test and learn more about your signal processing and analysis methods.

Are there prerequisites?

You need some programming experience. I go through the videos in MATLAB, and you can also follow along using Octave (a free, cross-platform program that emulates MATLAB). I provide corresponding Python code if you prefer Python. You can use any other language, but you would need to do the translation yourself.

I recommend taking my Fourier Transform course before or alongside this course. However, this is not a requirement, and you can succeed in this course without taking the Fourier transform course.

What should you do now?

Watch the sample videos, and check out the reviews of my other courses — many of them are “best-seller” or “top-rated” and have lots of positive reviews. If you are unsure whether this course is right for you, then feel free to send me a message. I hope you to see you in class!

Who this course is for:

  • Students in a signal processing or digital signal processing (DSP) course
  • Scientific or industry researchers who analyze data
  • Developers who work with time series data
  • Someone who wants to refresh their knowledge about filtering
  • Engineers who learned the math of DSP and want to learn about implementations in software

Created by Mike X Cohen
Last updated 10/2019

Size: 5.70 GB

IT & Software Python Python Courses Udemy Courses

Python for beginners – Learn all the basics of python 2020

Learn how to program in python- python functions-python basic apps – python tips and tricks – Other Python features

What you’ll learn

  • Learn how to use Python 3 the right way
  • Understand complex functions in python
  • Be able to use python on a daily basis
  • Create your own basic programs with python


  • Having a computer
  • Wanting to learn programming in python
  • no experience required


Have you always wanted to learn programming but didn’t know where to start ?  Well now you are at the right place ! I created this python course to help everyone learn all the basics of this programming language. This course is really straight to the point and will give you all the notion about python. Also, the course is not that long so and the way the material is presented is very easy to assimilate. So if python is something that you are interested about, then you will definitely like this course.

Who this course is for:

  • People interested to learn how to program in python
  • people curious about programming

Size: 1.47 GB

Node.JS Courses Python Web Development

Learn Web Scraping with NodeJs in 2020 – The Crash Course

Learn and be great at Web Scraping with NodeJs and tools like Puppeteer by Google, Request, Cheerio, NightmareJs.

What you’ll learn

Learn Web Scraping with NodeJs in 2019 – The Crash Course

  • Create Data Scrapers from Scratch to Finish with NodeJs
  • Choosing the right tools for Scraping different websites
  • How to use the Top Scraping tools for NodeJs to your Advantage
  • How to Automate User Interactions with NodeJs
  • Build Scrapers with Puppeteer by Google
  • Build Scrapers with the native Request & Cheerio
  • Learn how to scrape with NightmareJs


  • JavaScript Knowledge with ES6 Syntax
  • Be familiar with CSS / jQuery selectors


Get into the world of Web Scraping and Data Mining with NodeJs. Learn modern methods of scraping with NodeJs – Puppeteer and with direct NodeJs Requests.

Introduce yourself and improve your knowledge on Scraping

  • Learn Scraping with PuppeteerNightmareJs or Manual Requests
  • Build scraper modules for various websites ( IMDB, twitter, Instagram..etc )
  • Learn multiple ways of scraping and when to choose them
  • Get familiar with the ethics, do’s and dont’s of Scraping

Enjoy coding and learning Web Scraping with real-world examples and real-world problem solving while building scrapers with NodeJs.

Web Scraping is a very gray area and not many talks about it or even teach about this. You are going to find valuable scraping information and techniques that you can directly put to practice for yourself.

I’ve been working with Data Mining with NodeJs for more than 2 years on dozens of websites and I’ve learned many ways of creating a scraper and the best practices. All of which you are going to find out and learn in just a few hours in this course.


I designed this Web Scraping Crash Course to be easily understood by absolute beginners and people who already have some knowledge about the subject.

Complete crash course with all files and code samples, you’ll be able to work alongside with me as we work through each concept and scraper module.

This is not some random tutorial that you usually find on the internet with extremely simple examples. I am showing you everything that you need to think about when starting to build a scraper with NodeJs while building, problem-solving techniques and all you need to know that by the end of the course to be confident and create one for yourself.

Who this course is for:

  • Complete beginners interested in learning Data Scraping with NodeJs
  • People who already have some Basic Knowledge but want to Take it to the Next Level
Node.JS Courses Python Web Development

Node with SocketIO: Build A Full Web Chat App From Scratch

Build A Complete Chat App With Private and Group Chat Functionalities Using NodeJS, SocketIO, MongoDB, Express

What you’ll learn

Node with SocketIO: Build A Full Web Chat App From Scratch – Course Site

  • Understand RESTful API Design
  • Use Social Authentication in Apps
  • Dependency Injection Module
  • SocketIO Events
  • Store and Retrieve Data with MongoDB and Mongoose
  • AWS S3 Buckets
  • App Deployment to Production
  • Express Servers and APIs
  • Group Chat Functionality
  • Private Chat Functionality


  • A computer on which you can install software
  • A basic understanding of HTML and CSS
  • A basic understanding of AJAX method


Have you tried to build your web application with real-time functionalities using Node? Maybe you have heard about the popular socket IO real-time application framework but have never used it because you don’t know where and how to start. Perhaps, you have tried to build an application with some socket IO functionalities and you need to do more with it, then this course is for you.

This course is created for you

The complete socket IO course will guide you through building your real-time web chat application from start to finish. The course uses tools like Express, MongoDB, Mongoose.

The best way to learn Node

Reading about Node is not just sufficient in learning but also by building real-world apps. That is why this course is strictly project-based from start to finish. In the end, you’ll gain hands-on experience in learning Node and socket IO.

You’ll be building a chat application using:

  • Node
  • Socket IO
  • NPM
  • Express
  • MongoDB
  • Mongoose
  • Amazon Web Service
  • RESTful API Design
  • Asynchronous  programming
  • ES6 features like classes
  • Version control with Git
  • Github
  • App deployment with Heroku

What is an app if it is not online for people to use? That is why I’ll show you how to deploy your app to Heroku and also point your domain to Heroku app URL or domain.

During the course you’ll learn:

  1. Dependency injection with modules
  2. Users local authentication with passport
  3. Users social authentication with Facebook and Google
  4. Amazon Web Service
  5. Uploading files to AWS S3 buckets from your Node.js app
  6. MongoDB aggregate method
  7. Socket IO emitting and listening for events
  8. Group chat functionality
  9. Private chat functionality
  10. Functionality to send and receive friend requests
  11. Real-time friend request and message notifications
  12. Using third party API
  13. App deployment to Heroku (You’ll see how to point your domain to Heroku app)
  14. And more…

Who this course is for:

  • Anyone looking to launch their chat application for other people to use
  • Who anyone with a passionate and enthusiastic mindset to learn
  • Anyone wanting to train in back-end development
Python React Courses Udemy Courses

Advanced React And Redux: 2020 Edition

Detailed walkthroughs on advanced React and Redux concepts – Authentication, Testing, Middlewares, HOC’s, and Deployment

What you’ll learn

  • Build a scaleable API with authentication using Express, Mongo, and Passport
  • Learn the differences between cookie-based and token-based authentication
  • Figure out what a Higher Order Component and how to use it to write dramatically less code
  • Write Redux middleware from scratch to uncover what is happening behind the scenes with Redux
  • Set up your own testing environment with Jest and Enzyme
  • Realize the power of building composable components


  • Solid understanding of React
  • Intermediate understanding of Redux; you should have knowledge of reducers, actions, and action creators


Knowledge of React + Redux is 100% required! If you are familiar with reducers and action creators you will be fine.

This is the tutorial you’ve been looking for to take your React and Redux skills to the next level.

Authentication with Express/Mongo?  Yes!  Middleware/Higher Order Components? We got it.  Testing with Mocha/Chai?  It’s here!

This course wastes no time diving right into interesting topics, and teaches you the core knowledge you need to deeply understand and build React components and structure applications with Redux.

Mastering React and Redux can get you a position in web development or help you build that personal project you’ve been dreaming of. It’s a skill that will put you more in demand in the modern web development industry, especially with the release of Redux and ReactNative.

There are dozens of great tutorials online for React and Redux, but none of them teach the challenging, core features of these two fantastic libraries.  I created this course to push you beyond “just getting started.”

  • Learn how to thoroughly test React and Redux code, including tests for action creators and reducers
  • Get familiar with Higher Order Components.  Don’t know what they are?  No problem, you have been using them without even knowing it!
  • Rewrite a popular Redux Middleware from scratch to handle asynchronous actions
  • Become a master of the trickiest topic in Javascript: authentication.  You will write a server with enterprise-grade authentication from scratch that can scale to hundreds of thousands of users.  No shortcuts, no dummy data.

I have built the course that I would have wanted to take when I was learning React and Redux.  A course that explains the concepts and how they’re implemented in the best order for you to learn and deeply understand them.

Who this course is for:

  • Programmers with experience on React and Redux
  • NOT for programmers with no previous React/Redux experience!

Size: 8.81 GB

Python Python Books


So, you want to learn programming. Welcome to one of the great adventures of the twenty-first century. Programming requires little in the way of specialized equipment; the software tools can all be downloaded for free off the Internet, and it can be practiced in the safety and comfort of your own home, without having to ask anyone’s permission.

This will ease you in gently by introducing you to the software you will need to create your programs: a command-line interface, which allows you to use Python in interactive mode, and a text editor for writing scripts—nothing more complicated than that.

I will also show you where to go to find help and documentation, so you can decode the sometimes-impenetrable jargon that seems to surround this, the geekiest of all technical disciplines.

To begin with, you will need to make sure that you have a decently recent version of Python installed on your machine or follow the steps later in this chapter to install it (see “Choosing the Right Python Version” for a definition of decently recent).

This chapter explains how to make sure that you have everything set up correctly and that you have suitable references at hand before you start your journey.

Size: 5MB

Data Science Machine Learning Python Python Courses Udemy Courses

Beginning with Machine Learning & Data Science in Python

Fundamentals of Data Science : Exploratory Data Analysis (EDA), Regression (Linear & logistic), Visualization, Basic ML

What you’ll learn

  • You will be able to apply data science algorithms for solving industry problems
  • You will have a clear understanding of industry standards and best practices for predictive model building
  • Will be able to derive key insights from data using exploratory data analysis techniques
  • You will be able to efficiently handle data in a structured way using Pandas
  • Have a strong foundation of linear regression, multiple regression and logistic regression
  • You will be able to use python scikit-learn for building different types of regression models
  • Will be able to use cross validation techniques for comparing models, select parameters
  • You will know about common pitfalls in modeling like over-fitting, bias-variance trade off etc..
  • You will be able to regularize models for reliable predictions


  • Basic programming in any language
  • Basic Mathematics
  • Some exposure to Python (but not mandatory)


This course will help you create a solid foundation of the essential topics of data science. With a solid foundation, you will be able to go a long way, understand any method easily, and create your own predictive analytics models.

At the end of this course, you will be able to:

  • Get your hands dirty by building machine learning models
  • Master logistic and linear regression, the workhorse of data science
  • Build your foundation for data science
  • Fast-paced course with all the basic & intermediate level concepts
  • Learn to manage data using standard tools like Pandas

This course is designed to get students on board with data science and make them ready to solve industry problems. This course is a perfect blend of foundations of data science, industry standards, broader understanding of machine learning and practical applications.

Special emphasis is given to regression analysis. Linear and logistic regression is still the workhorse of data science. These two topics are the most basic machine learning techniques that everyone should understand very well. Concepts of over fitting, regularization etc. are discussed in details.

These fundamental understandings are crucial as these can be applied to almost every machine learning methods.

This course also provide an understanding of the industry standards, best practices for formulating, applying and maintaining data driven solutions. It starts off with basic explanation of Machine Learning concepts and how to setup your environment. Learning the industry standard best practices and evaluating the models for sustained development comes next.

Final learning are around some of the core challenges and how to tackle them in an industry setup. This course supplies in-depth content that put the theory into practice.

Who this course is for:

  • Anyone willing to take the first step towards data science
  • Anyone willing to develop a solid foundation for data science
  • Planning to build the first regression / machine learning models
  • Anyone willing to learn exploratory data analysis

Size: 542.72 MB