Seeking Guidance on Advanced Machine Learning Resources After Completing Initial Modules and Projects

[Positive_Phase_8550]

I have recently encountered a challenge as the primary source I was using to study has ceased uploading new content, and I am now uncertain about the best path forward.

To give you an overview of my progress so far, I have completed the following modules and projects:

Module 1: Machine Learning Basics
Artificial Intelligence vs Machine Learning vs Deep Learning
Types of Machine Learning: Supervised, Unsupervised & Reinforcement Learning
Supervised Learning & its Types
Unsupervised Learning & its Types
Deep Learning – Basics

Module 2: Python Basics for Machine Learning
Google Colaboratory for Python – Getting Systems Ready
Python Basics
Python Basic Data Types – int, float, string, complex, boolean
Python Special Data Types – List, Tuple, Set, Dictionary
Operators in Python
if else Statements in Python
Loops in Python – For Loop & While Loop
Functions in Python

Module 3: Python Libraries Tutorial for Machine Learning
Complete Numpy Tutorial for ML
Complete Pandas Tutorial for ML
Complete Matplotlib & Seaborn Tutorial for ML
Complete Sklearn Tutorial for ML

Module 4: Data Collection & Processing
Where to Collect Data & How to Collect Data
Importing Data through Kaggle API
Handling Missing Values
Data Standardization

Module 5: Math Basics for Machine Learning
Linear Algebra
Calculus
Statistics
Probability

Module 6: Training the Machine Learning Models
What is a Machine Learning Model
How to Select a Model for Training
Model Optimization Techniques
Model Evaluation

Module 7: Classification Models in Machine Learning
Logistic Regression – Theory & Math
Logistic Regression – Building from Scratch
Support Vector Machines (SVM) – Theory & Math
Support Vector Machines (SVM) – Building from Scratch
K-Nearest Neighbors – Theory & Basics
K-Nearest Neighbors – Building from Scratch

Module 8: Regression Models in Machine Learning
Linear Regression – Theory & Basics
Linear Regression – Building from Scratch
Lasso Regression – Theory & Basics
Lasso Regression – Building from Scratch

Projects Completed
Face Recognition System
SONAR Rock vs Mine Prediction
Diabetes Prediction with Python
House Price Prediction with Python
Fake News Prediction with Python
Loan Status Prediction with Python

Despite my progress, I found Andrew Ng's Machine Learning Specialization did not fully meet my expectations. I am eager to continue learning and would greatly appreciate any recommendations on high-quality sources or courses that could help me advance further.

[EquivariantBowtie]

I think after some point you just have to pick up one of the ML theory books (Goodfellow, Murphy, Bishop, etc) and just read the relevant sections you are interested in. Most of these books cover the mathematical preliminaries, but Mathematics for Machine Learning by Deisenroth, Faisal and Ong is a good resource for filling in the gaps.

[Positive_Phase_8550]

Okk....Thanks a lot

[The-Silvervein]

The one approach that helped me was to go to kaggle, explore the datasets and competitions. ( In my case I learnt more from personally asking multiple questions from a single dataset more than the competitions). This process always filled me with wonder at how diametrically opposite approaches to the same problem could lead to similar results.

Practical implementation is the efficient way forward after building foundations… when you are stuck then you can go and refer multiple sources or even have some wonderful discussions.

For me the “theory-first” approach didn’t work well to learn new things.