Here is a list of Python free courses that i maintain
An Introduction to Interactive Programming in Python (Part 1)
By: Rice University - Coursera
This two-part course is designed to help students with very little or no computing background learn the basics of building simple interactive applications. Our language of choice, Python, is an easy-to learn, high-level computer language that is used in many of the computational courses offered on Coursera. To make learning Python easy, we have developed a new browser-based programming environment that makes developing interactive applications in Python simple. These applications will involve windows whose contents are graphical and respond to buttons, the keyboard and the mouse.
Introduction to Computer Science and Programming Using Python
By: MITx - Edx
This course is the first of a two-course sequence: Introduction to Computer Science and Programming Using Python, and Introduction to Computational Thinking and Data Science. Together, they are designed to help people with no prior exposure to computer science or programming learn to think computationally and write programs to tackle useful problems. Some of the people taking the two courses will use them as a stepping stone to more advanced computer science courses, but for many it will be their first and last computer science courses. This run features lecture videos, lecture exercises, and problem sets using Python 3.5. Even if you previously took the course with Python 2.7, you will be able to easily transition to Python 3.5 in future courses, or enroll now to refresh your learning.
Since these courses may be the only formal computer science courses many of the students take, we have chosen to focus on breadth rather than depth. The goal is to provide students with a brief introduction to many topics so they will have an idea of what is possible when they need to think about how to use computation to accomplish some goal later in their career. That said, they are not “computation appreciation” courses. They are challenging and rigorous courses in which the students spend a lot of time and effort learning to bend the computer to their will.
Learn to Program: The Fundamentals
By: University of Toronto - Coursera
Behind every mouse click and touch-screen tap, there is a computer program that makes things happen. This course introduces the fundamental building blocks of programming and teaches you how to write fun and useful programs using the Python language.
CS50’s Introduction to Computer Science
By: Harvard University - Edx
This is CS50x, Harvard University’s introduction to the intellectual enterprises of computer science and the art of programming for majors and non-majors alike, with or without prior programming experience. An entry-level course taught by David J. Malan, CS50x teaches students how to think algorithmically and solve problems efficiently. Topics include abstraction, algorithms, data structures, encapsulation, resource management, security, software engineering, and web development. Languages include C, Python, SQL, and JavaScript plus CSS and HTML. Problem sets inspired by real-world domains of biology, cryptography, finance, forensics, and gaming. The on-campus version of CS50x, CS50, is Harvard’s largest course.
CS50’s Web Programming with Python and JavaScript
By: Harvard University - Edx
This course picks up where CS50 leaves off, diving more deeply into the design and implementation of web apps with Python, JavaScript, and SQL using frameworks like Flask, Django, and Bootstrap.
Learn to Program: Crafting Quality Code
By: University of Toronto - Coursera
Not all programs are created equal. In this course, we’ll focus on writing quality code that runs correctly and efficiently. We’ll design, code and validate our programs and learn how to compare programs that are addressing the same task.
CS For All: Introduction to Computer Science and Python Programming
By: Harvey Mudd College - Edx
Looking to get started with computer science while learning to program in Python?
This computer science course provides an introduction to computer science that’s both challenging and fun. It takes a broad look at the field of computer science through a variety of demonstrations and projects. We’ll cover both low- and high-level concepts, from how the circuits inside a computer represent data to how to design algorithms, as well as how all of this information affects the technology we use today. Additionally, we’ll teach the basics of Python programming, giving us a a way to put our new CS knowledge into practice.
No need to know any programming before starting the course; we’ll teach everything you need to know along the way. All you need to start is a good grasp of algebra, and you can fall in love with both the concepts and the practice of computer science.
Python Programming Essentials
By: Rice University - Coursera
This course will introduce you to the wonderful world of Python programming! We’ll learn about the essential elements of programming and how to construct basic Python programs. We will cover expressions, variables, functions, logic, and conditionals, which are foundational concepts in computer programming. We will also teach you how to use Python modules, which enable you to benefit from the vast array of functionality that is already a part of the Python language. These concepts and skills will help you to begin to think like a computer programmer and to understand how to go about writing Python programs. By the end of the course, you will be able to write short Python programs that are able to accomplish real, practical tasks. This course is the foundation for building expertise in Python programming. As the first course in a specialization, it provides the necessary building blocks for you to succeed at learning to write more complex Python programs. This course uses Python 3. While many Python programs continue to use Python 2, Python 3 is the future of the Python programming language. This first course will use a Python 3 version of the CodeSkulptor development environment, which is specifically designed to help beginning programmers learn quickly. CodeSkulptor runs within any modern web browser and does not require you to install any software, allowing you to start writing and running small programs immediately. In the later courses in this specialization, we will help you to move to more sophisticated desktop development environments.
Using Databases with Python
By: University of Michigan - Coursera
This course will introduce students to the basics of the Structured Query Language (SQL) as well as basic database design for storing data as part of a multi-step data gathering, analysis, and processing effort. The course will use SQLite3 as its database. We will also build web crawlers and multi-step data gathering and visualization processes. We will use the D3.js library to do basic data visualization. This course will cover Chapters 14-15 of the book “Python for Everybody”. To succeed in this course, you should be familiar with the material covered in Chapters 1-13 of the textbook and the first three courses in this specialization. This course covers Python 3.
Using Python for Research
By: Harvard University via Edx
This course bridges the gap between introductory and advanced courses in Python. While there are many excellent introductory Python courses available, most typically do not go deep enough for you to apply your Python skills to research projects. In this course, after first reviewing the basics of Python 3, we learn about tools commonly used in research settings. This version of the course includes a new module on statistical learning.
Using a combination of a guided introduction and more independent in-depth exploration, you will get to practice your new Python skills with various case studies chosen for their scientific breadth and their coverage of different Python features.
Problem Solving, Python Programming, and Video Games
By: University of Alberta - Coursera
This course is an introduction to computer science and programming in Python. Upon successful completion of this course, you will be able to: 1. Take a new computational problem and develop a plan to solve it through problem understanding and decomposition. 2. Follow a design creation process that includes specifications, algorithms, and testing. 3. Code, test, and debug a program in Python, based on your design. Important computer science concepts such as problem solving (computational thinking), problem decomposition, algorithms, abstraction, and software quality are emphasized throughout. The Python programming language and video games are used to demonstrate computer science concepts in a concrete and fun manner. However, a learner can take the knowledge and skills from this course and apply them to non-game problems, other programming languages, and other computer science courses. You do not need any previous programming, Python, or video game experience. However, some computer skills (e.g., mouse, keyboard, document editing), knowledge of algebra, attention to detail (as with many technical subjects), and a “just give it a try” spirit will be keys to your success. Despite the use of video games for all the programming examples, PVG is not about computer games. PVG will still provide valuable knowledge and skills for non-game computational problems.
Advanced Algorithmics and Graph Theory with Python
By: Institut Mines Telecom - Edx
Algorithmics and programming are fundamental skills for engineering students, data scientists and analysts, computer hobbyists or developers.
Learning how to program algorithms can be tedious if you aren’t given an opportunity to immediately practice what you learn. In this course, you won’t justfocus on theoryor study a simple catalog of methods, procedures, and concepts. Instead, you’ll be given a challenge wherein you’ll be asked to beat an algorithm we’ve written for you by coming up with your own clever solution.
To be specific, you’ll have to work out a route faster than your opponent through a maze while picking up objects.
Each week, you will learn new material to improve your artificial intelligence in order to beat your opponent. This structure means that as a learner, you’ll confront each abstract notion with a real-world problem.
We’ll go over data-structures, basic and advanced algorithms for graph theory, complexity/accuracy trade-offs, and even combinatorial game theory. This course has received financial support from the Patrick and Lina Drahi Foundation.
Applied Machine Learning in Python
By: University of Michigan - Coursera
This course will introduce the learner to applied machine learning, focusing more on the techniques and methods than on the statistics behind these methods. The course will start with a discussion of how machine learning is different than descriptive statistics, and introduce the scikit learn toolkit through a tutorial. The issue of dimensionality of data will be discussed, and the task of clustering data, as well as evaluating those clusters, will be tackled. Supervised approaches for creating predictive models will be described, and learners will be able to apply the scikit learn predictive modelling methods while understanding process issues related to data generalizability (e.g. cross validation, overfitting). The course will end with a look at more advanced techniques, such as building ensembles, and practical limitations of predictive models. By the end of this course, students will be able to identify the difference between a supervised (classification) and unsupervised (clustering) technique, identify which technique they need to apply for a particular dataset and need, engineer features to meet that need, and write python code to carry out an analysis. This course should be taken after Introduction to Data Science in Python and Applied Plotting, Charting & Data Representation in Python and before Applied Text Mining in Python and Applied Social Analysis in Python.
Deep Neural Networks with PyTorch
By: IBM - Coursera
The course will teach you how to develop deep learning models using Pytorch. The course will start with Pytorch’s tensors and Automatic differentiation package. Then each section will cover different models starting off with fundamentals such as Linear Regression, and logistic/softmax regression. Followed by Feedforward deep neural networks, the role of different activation functions, normalization and dropout layers. Then Convolutional Neural Networks and Transfer learning will be covered. Finally, several other Deep learning methods will be covered. Learning Outcomes: After completing this course, learners will be able to:
• explain and apply their knowledge of Deep Neural Networks and related machine learning methods
• know how to use Python libraries such as PyTorch for Deep Learning applications
• build Deep Neural Networks using PyTorch
Deep Learning with Python and PyTorch
By: IBM - Edx
The course will teach you how to develop Deep Learning models using Pytorch while providing the necessary deep-learning background.
We’ll start off with PyTorch’s tensors and its Automatic Differentiation package. Then we’ll cover different Deep Learning models in each section, beginning with fundamentals such as Linear Regression and logistic/softmax regression.
We’ll then move on to Feedforward deep neural networks, the role of different activation functions, normalization and dropout layers.
In the final part of the course, we’ll focus on Convolutional Neural Networks and Transfer Learning (pre-trained models). Several other Deep Learning methods will also be covered.
