Machine Learning is one of the most in-demand careers out there and the current trend shows that this particular field won’t be becoming obsolete anytime soon.
It might be a booming career path, at least for a few upcoming decades. Economists are predicting the arrival of the recession, and mass layoff is in the full swing. Considering these events, if you are looking for a career change, or maybe you are a complete beginner and want to get into Machine Learning.
We have compiled the best five GitHub repositories that you can utilize to learn Machine Learning for Free.
1. Homemade Machine Learning – [20.7k stars]
They call it “Homemade Machine Learning”. The reason? All the ML algorithms they have implemented are from scratch without using any third-party libraries. So that you understand the logic and mathematics involved behind each of these algorithms in great detail.
You can fire up the Jupyter Notebook which comes with the individual algorithm, directly into your browser to play with the training datasets and see the results promptly. They have covered Supervised and Unsupervised ML models thoroughly. Furthermore, they have focused on the inner working of Neural Networks.
After learning each of these models, you will have a basic understanding of how these ML models are used in various real-world applications.
For example in stock price forecast, sales analysis, spam filters that are used in your emails, language detection, and handwritten letter recognition that is used by most modern translation apps today, examples include the Yandex translator and the Google translator.
I can name a few more use cases, the list is endless. Overall, the “Homemade Machine Learning” repository is a must if you want to level up your machine-learning game.
2. Made with ML – [31.9k stars]
Made With ML is another great repository for machine learning. I would recommend every absolute beginner to see their foundation lessons on ML and Deep Learning which will introduce you to various significant concepts like Linear Regression, Logistic Regression, Convolutional Neural Networks, Recurrent Neural Networks, and the importance of data quality in ML, etc.
The course is provisioned especially for software engineers, data scientists, college grads, and product managers. After completing the “Made with ML” course, you will develop a strong foundation in ML and be able to responsibly deliver value with ML.
Apart from that, you will learn the practical skills that are required in the industry. The course is a perfect blend of theory and pragmatic skills.
3. 500+ AI Machine Learning with Code – [11.2k stars]
500+ AI Machine Learning with Code repository lists various other GitHub repositories and links to beneficial resources to get you started in ML.
There are tons of practical projects here which you can try and even dive deep into its source code. Python is predominantly used in most of its projects. Not all projects are directly related to ML here. Some tasks are related to web scraping, GUI in python or Django web framework. Feel free to explore it, if you may find something that interests you.
The Andrew-NG-Notes” is one of the repositories inside this list and is immensely interesting, it contains essential ML learning content that I would strongly recommend you visit.
Here you can find notes on ML courses taught by Andrew Ng. He is a computer scientist and technology entrepreneur. He has co-founded Google Brain, it’s a deep learning artificial intelligence research team under the umbrella of Google AI. Apart from that, he co-founded Coursera. He is also currently an adjunct professor at Stanford University.
There is a free ML series taught by him available on YouTube [112 free videos] and Coursera [127,825 ratings]. You may find this “Artificial Intelligence” repository quite overwhelming, due to its vast resources but in the end, it’s worth it to explore.
4. Complete Machine Learning Package – [3.7k stars]
Undoubtedly, Python is leading the Machine Learning and data science industry. The reason is its libraries that offer a total environment and tools which data scientists and ML engineers require to perform their daily tasks.
The Complete Machine Learning Package repository will walk you through the python ML and Data Science libraries. Here you will learn about ML libraries such as NumPy, Pandas, Matplotlib, Seaborn, Scikit-Learn, Tensor Flow, and Keras.
It also contains a list of awesome machine-learning books that you will find compelling. It’s a good starting place for newcomers to quickly begin their ML journey.
5. Mathematics for Machine Learning – [3.3k stars]
Mathematics for Machine Learning is another substantial repository containing a list of books and resources mainly focused on Mathematics that directly relates to Machine Learning. It also has some fascinating research papers on ML and Deep Learning.
Apart from that, you can also find links to video lectures on mathematical concepts like Linear Algebra, Calculus, and some good video links covering ML. If you want to refresh some mathematical topics like Statistics, Probability Theory, Information Theory, etc., for ML, they have a good compilation of content for you.
To conclude, we have mentioned the best free GitHub repositories to learn about Machine Learning. Every repository mentioned here has something to offer which aligns directly with your goal of becoming a Machine Learning Engineer or Data Scientist.
If we’ve missed any useful Machine Learning repository in the list that you want us to include, do share it in the comments.
Credits – Thank you @DanKornas for curating such an amazing list.