Machine Learning

உரையாடல்
1

As I mentioned in the Machine Learning session I am sharing the details of how one can approach Machine Learning from scratch.

2 Likes
2

One cannot do machine learning without understanding the underlying mathematics. So what topics one must refresh before entering ML is :

  • Linear Algebra: Vectors, matrices, matrix multiplication, linear transformations.
  • Calculus: Derivatives, partial derivatives, gradient descent.
  • Probability & Statistics: Basic probability, distributions, mean, variance, standard deviation.
1 Like
3

After solidifying your understanding of mathematics, it’s essential to become comfortable with Python, the most popular language for machine learning. Python’s popularity stems from its simplicity and powerful libraries that streamline data processing, visualization, and model building.

Focus on understanding and using the following key libraries:

  • NumPy: Essential for numerical operations and working with arrays and matrices.
  • Pandas: Crucial for data manipulation, handling data structures, and performing operations on datasets.
  • Matplotlib & Seaborn: These libraries are vital for creating informative data visualizations to understand data patterns and trends.
1 Like
4

Now all you need is good resource for learning machine learning.

From my experience Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow by Aurélien Géron is the best reference material for any beginner.

And for Neural Networks refer Neural Networks and Deep Learning by Michael Nielsen.

3 Likes
5

Hello Brother, thanks for sharing useful information.
I am new to machine learning, do we have any specific study material or videos for above topic? or can i directly start reading the book you specified

6

@syed the class recordings,
https://www.youtube.com/watch?v=vxB_ASanNXo&t=2108s.

You can also start from the above books.

1 Like
7

These lectures provide a solid foundation. After watching a lecture, reinforce your understanding by diving deeper into the topic using additional resources. Most importantly, ensure you are applying what you learn through hands-on exercises and practical work.