10 Best Free Courses to learn NumPy, Pandas, Keras, PyTorch & MatPlotLib in 2024 (original) (raw)

Free online courses to learn NumPy, Pandas, Keras, PyTorch, and MatPlotLib in, and other popular Python machine learning libraries in 2024

Hello guys, when I started learning Data Science and Machine learning, I had a hard time figuring out how everything worked. Which programming language to use for Data Science? Python or R? Which library was the best for building a deep learning model and plotting charts? Which algorithms worked best for which data set? How could I know my model was accurate? etc

I spent a lot of time on tutorials, books, courses, and reading to try and finding out answers to these questions. In the end, I felt like the process I took to learn deep learning was too inefficient.

That is why I am writing this article to share different Machine learning libraries, their purpose, and a free course to learn them. If one of your goals is to learn Machine learning and Deep learning in 2024, then these resources can help you a lot.

In this article, I am going to share some of the best free classes to learn Machine learning and Deep learning online. Btw, If you are thinking to learn Data Science, Machine learning, or Deep learning then you are not alone, more and more people are starting with these advanced skills around the world.

AI Jobs

Though, I have seen a lot of interest from Indian engineers in Data Science, Machine learning and the Artificial intelligence space. They are totally caught up with the craze of developing programs that can recognize numbers, alphabets, vehicles, and several other image scanning stuff.

The craze is very similar to what the 1980’s programmer has about video games, where moving a character on screen gives the joy you get when your program correctly identifies the number or letter you make from hand.

From college graduates to junior programmers and from experienced programmers to software architects, all are showing interest in Machine learning and Artificial intelligence to become part of the next technical revolution, we may be witnessing.

Btw, if you are wondering about what is Machine learning and Deep Learning, then let me give you a brief overview.

Machine learning programs use algorithms to parse data, learn from that data, and make informed decisions based on what it has learned. One example of that was selecting the best Cucumber from a lot, which was done by a Japanese programmer, you can read the full story here.

On the other, Deep learning structures algorithms in layers to create an “artificial neural network” that can learn and make intelligent decisions on its own. It’s more complicated than machine learning.

These free courses are great to learn both basic Machine learning concepts as well as useful Python libraries which make things possible. Btw, if you don’t mind paying few bucks for learning a valuable skill like Data Science and Machine Learning then I also recommend you check out Machine Learning, Data Science, and Deep Learning with Python by

on Udemy.

It's a complete hands-on course to learn Machine learning, DataScience, Tensorflow, artificial intelligence, and neural networks

10+ Free Machine Learning and Deep Learning Courses for Beginners and Intermediate Data Science Developers

Before I share the list of courses, I’d like to clarify that, even though these courses are free, they are not of inferior quality, and they are just made free by their instructor for promotional and education purposes.

In fact, sometimes these free courses are covered into paid courses once the instructor reaches their promotional targets, so please be careful and check the price of the course before you join.

Anyway, Here is my list of some of the best free courses to learn Machine Learning and Deep Learning online by yourself.

1. Python for Machine Learning — FREE

This course is a prerequisite to Learn Machine Learning and I strongly suggest you learn and master Python before you deep dive into machine learning libraries and algorithms.

This is a beginner-level course and designed for programmers, who want to learn Python Programming to get into Data Science or Machine Learning. You will learn all the basic Python programming concepts in this course for FREE.

The course covers things like:

Introduction to Dictionary
Dictionary & Its Methods
Nested Dictionary
Sets
Tuples

Here is the link to join this course — Python for Machine Learning — FREE

2. Numpy Basics For Machine Learning

This is another excellent free course to learn Deep Learning on Udemy. This covers four major Python libraries, like the Numpy, Scipy, Pandas, and Matplotlib stack, which are crucial to Deep learning, Machine learning, and Artificial intelligence.

If you don’t know, Numpy provides essential building blocks, like vectors, matrices, and operations on them, while Scipy uses those general building blocks to do specific things.

Panda’s strength lies in loading data, particularly from the database, while Matplotlib helps in looking at that data using some standard plots like namely the line chart, scatter plot, and histogram.

Here is the link to join this course — Numpy Basics For Machine Learning

In this 1.5 hours long course, you will learn all these libraries and learn how to supervise machine learning (classification and regression) with real-world examples using Scikit-Learn.

You will also learn how to use Numpy, Scipy, Matplotlib, and Pandas to implement numerical algorithms, and most importantly, you will learn the pros and cons of various machine learning models, including Deep Learning decisions Trees, Random Forest, Linear Regression, Boosting, etc.

In short, an excellent free course to learn Deep Learning using Numpy, Scipy, Pandas, and Matplotlib stack.

Photo by h heyerlein on Unsplash

3. Finding Actionable Insights using Keras Autoencoders

This is an excellent course to learn another powerful Python machine learning library called Keras. If you don’t know, Keras is a both powerful and easy-to-use Python library for developing and evaluating deep learning models.

It wraps the efficient numerical computation libraries like Theano and TensorFlow and allows you to define and train neural network models in a few short lines of code, which is just awesome.

In this course, you will learn how to build a Keras Autoencoder using Python and extract actionable insights from data using unsupervised and semi-supervised modeling. You will also learn to find anomalies in data.

This is a great course for anyone who wan to analyze data, perform anomaly detection, and be interested in Autoencoders and machine learning with Keras

Here is the link to join this course — Finding Actionable Insights using Keras Autoencoders

4. Deep Learning with PyTorch — Zero to GANs

PyTorch is another open-source neural networks library developed by Facebook which is often used for building deep learning models. It’s like TensorFlow but much simpler and easy to use.

Here are the concepts you will learn in this course:

PyTorch Basics: Tensors & Gradients
Linear Regression & Gradient Descent
Classification using Logistic Regression
Feedforward Neural Networks & Training on GPUs (this post)

Here is the link to join this course for FREE — Deep Learning with PyTorch — Zero to GANs

5. Meeshkan: Machine Learning the GitHub API

The Meeshkan is an easy and inexpensive platform where people can explore ideas in AI, Machine Learning, and Deep Learning. In this course, Meeshkan C.E.O. Mike Solomon will teach you how to do Machine Learning on Meeshkan.

This course starts with a simple AI question — Can a machine predict if a GitHub project will be successful by analyzing only the first few commits of that project?

First, you will learn how to run the Machine Learning project on Meeshkan. After that you will go through each step of the process in detail, covering data collection, data egress, infrastructure deployment, model design, model executing, and result in analysis.

6. Learn Machine Learning algorithms, software, deep learning

This is another excellent free course to learn Python Deep learning libraries and essential algorithms. This course covers Deep Learning, Neural Networks, KDD, AI, BI, ANN, Decision tree, Bayesian networks, TensorFlow and Knime

You will learn about the process of building supervised predictive models and make several of them using Python, the most widely used programming language for machine learning.

As part of the course, you will also learn how to do Machine Learning on AWS covers more details about concepts of TensorFlow, Amazon SageMaker, and other AWS ML topics.

Here is the link to join this course —Learn Machine Learning algorithms, software, deep learning

7. How to Think About Machine Learning Algorithms

If you don’t know the question, you probably won’t get the answer right, and this course is all about asking the right machine learning questions.

Machine learning is behind one of the coolest technological innovations today, but contrary to popular perception, you don’t need to be a math genius to successfully apply machine learning.

At first, you need to identify whether machine learning can provide an appropriate solution, and in this course, you’ll learn how to identify those situations.

The topics covered in this course include Classifying Data, Predicting relationships using regression, Recommending a product, and Clustering large data sets into meaningful groups.

Here is the link to join this course — How to Think About Machine Learning Algorithms

You need a Pluralsight membershipto access this course, which costs around $29 per month. On the other note, Pluralsight is a great resource, and its membership is definitely worth every penny spent. I have bought the annual membership, which comes with a discount.

Anyway, even if you don’t have Pluralsight membership, you can still access this course for free by signing up for a 10-day free trial without any commitment, which provides 200 minutes of watch time.

Overall an excellent course to get a high-level overview of what is machine learning and how to use it to solve real-world problems. This is one of the basic Machine learning courses, but I have put that to the end because it’s not entirely free.

8. Deep Learning Prerequisites: The Numpy Stack in Python V2

There is a huge gap for many programmers between machine learning “theory” and writing actual code and without basic knowledge of data manipulation, vectors, and matrices, you can put your great ideas into working solutions, on a computer.

Created by Lazyprogrammer Me this course closes that gap by teaching you all the basic operations you need for implementing machine learning and deep learning algorithms.

In this course, you will learn about machine learning algorithms, and implement those algorithms in code using the tools and techniques you learned in this course.

Here is the link to join this course for FREE — Deep Learning Prerequisites: The Numpy Stack in Python V2

10. Pandas with Python

Pandas is another powerful and essential Python library for Data Scientists. It allows you to do anything and everything with colossal data sets like cleaning, analyzing, organizing, sorting, filtering, pivoting, aggregating, munging, cleaning, calculating, and more!

With over 27 lectures and 6 and half hours of Pandas training material, you will learn everything a Data Scientist should know, from installation to visualization! Thanks to Srinivas Reddy for creating this awesome course.

You will learn hundreds of different methods, attributes, features, and functionalities packed away inside this awesome library. We’ll dive into tons of different datasets, short and long, broken and pristine, to demonstrate the incredible versatility and efficiency of this package.

If you’ve spent time in spreadsheet software like Microsoft Excel, Apple Numbers, or Google Sheets and are eager to take your data analysis skills to the next level, this course is for you!

Here is the link to join this course for FREE —Pandas with Python

11. MatPlotLib with Python

More and more programmers are realizing the vast benefits and uses of analyzing big data. However, the majority of people lack the skills and the time needed to understand this data in its original form.

That’s where data visualization comes into the picture, it helps you to create easy-to-read, simple-to-understand graphs, charts, and other visual representations of data.

And, when it comes to Data Visualization, Python 3 and Matplotlib are the most easily accessible tools for Data Visualization and this course will teach you everything you want to know about the MatPlotLit library.

With over 58 lectures and 6 hours of content, this course covers almost every major chart that Matplotlib is capable of providing. You will learn how to create line graphs, scatter plots, stack plots, pie charts, bar charts, 3D lines, 3D wireframes, 3D bar charts, 3D scatter plots, geographic maps, live-updating graphs, and virtually anything else you can think of!

Here is the link to join this course for FREE — MatPlotLib with Python

That’s all about some of the best free courses to learn Machine Learning, Deep Learning, and Artificial intelligence in 2024. As I have said, these are new technologies which will rule the world in the coming years, hence learning them now will provide you with valuable experience and you will be well ahead of others.

At the moment, a Machine learning specialist is also drawing a very handsome salary and solving some interesting problems of the world, so it’s not only financially rewarding but also work is really great.

Other Free Programming Courses you may like

Thanks for reading this article so far. If you like these free Machine Learning courses, then please share them with your friends and colleagues. If you have any questions or feedback, then please drop a note.

P. S. — If you are looking for the best Machine Learning course and don’t mind paying some money, thenMachine Learning A-Z: Hands-On Python & Ris the perfect course to start with. This would be the right choice to learn Machine learning in 2024.