I haven’t ever explored machine learning and data science. Also I don’t know the further mathematics this science requiring. I only know about the basics of Python. What books would you recommend for learning that entire science? According to others, there is a good way to start with “Introducion to Linear Algebra”.
1 Like
There are some good courses e.g. on udemy and also on youtube.
Also something neatly structured like this might be a good start:
3 Likes
OpenAI has a GitHub site. There is a Cookbook repository.
On the readme page is
Related resources from around the web
It has more than just books. 
Note: The information is still valid even though the list has not been updated since 2023.
2 Likes
You could check out a more M/L slanted book that covers Linear Algebra, Stats, and Calculus and other useful concepts specifically for Machine Learning.
One good one that fits this category is by Deisenroth, and is FREE.
Direct PDF link:
4 Likes
I’m biased, but you really should try The Hundred-Page Machine Learning Book in combination with asking questions to ChatGPT.
2 Likes
- Mathematics for Machine Learning
- Hands on ML with Scikit learn, Keras and tensorflow
- Python Machine Learning by examples
If you’re still looking to learn machine learning from scratch, I’d recommend “Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow” by Aurélien Géron, as someone mentioned.
1 Like
As you have just started learning machine learning and data science with only basic Python knowledge, the best way is to build your skills step by step-starting with the math and Python skills needed, and then moving to machine learning and deep learning.
And for this you can follow below simple steps:
Step 1: Learn the Math Behind Machine Learning
Step 2: Improve Your Python for Data Work
Step 3: Learn Machine Learning
Step 4: Learn Deep Learning
Step 5: Now practice What You Learn
One more thing that you need to understand that books may be different but you need to keep your focus on basics.
Thanks
Far better than reading a book is writing one.
Ask ChatGPT for an outline an fact check everything and build experiments and fact check them too.
Watch out for real world problems. Ask companies if they would be willing to give you the opportunity to solve something for your book.
This has multiple positive aspects when done correctly.
Most important: don’t be lazy! Do not take a chapter just to fill the book.
And at last: throw it all away and then write it again.
The third iteration should take about one year and give you a solid understanding and hey you made a book and you fully understand it.
That’s way more valuable than reading 20 books.
2 Likes
“读万卷书，行万里路” (“Read ten thousand books, travel ten thousand miles”)
1 Like
Hello, As you want to explore machine learning and data science, there are lots of good books that will help you. Yes, “Introduction to Linear Algebra” is one of the best ways to start. Here are some of the good book suggestions that may help you in learning.
For Mathematics,
- Introduction to Linear Algebra by Gilbert Strang
- Linear Algebra Done Right by Sheldon Axler
- Practical Statistics for Data Scientists by Peter Bruce & Andrew Bruce
- Introduction to Probability by Dimitri P. Bertsekas & John Tsitsiklis
- Calculus: Early Transcendentals by James Stewart
For Python and data science, you can read these books.- Python Data Science Handbook by Jake VanderPlas.
- Data Science from Scratch by Joel Grus
For machine learning, you can read these books. - Hands-On Machine Learning with Scikit-Learn, Keras and TensorFlow by Aurelien Geron.
- Deep Learning by Ian Goodfellow, Yoshua Bengio and Aaron Courville.
- Machine Learning Yearning by Andrew Ng.
Hope it will help you.
Mamta Bankoti