For almost all machine learning projects, the main steps of the ideal solution remain same. Briefly, we all go over the steps below each and every time:

Understand the data

Clean up, fix the missing values, extract new features, select the best ones

Build the model, compare it with the other ones, tune hyper parameters, find out what is the right metric to evaluate your model

Iterate this process over and over again until you believe you have the best solution :)

During each step, I had to do some research on the web depending on my business objective and jotted down the best resources I ran across. The resources include Online Courses, Kernels from Kaggle, Cheat Sheets and Blog Posts. Below I’ve listed them and categorised by each step (all of the resources are free except the ones that have ‘paid’ in the end):

Exploratory Data Analysis

1- Kaggle Tutorial EDA & Machine Learning (Datacamp-Blog Post)

2- EDA Kernel for Zillow Competition (Kaggle — Kernel)

3- Compherensive EDA Kernel for Kaggle House Prices: Advanced Regression Techniques Competition (Kaggle — Kernel)

4- EDA Kernel for Kaggle Santander Customer Satisfaction Competition (Kaggle — Kernel)

5- Exploratory Data Analysis in Advanced ML Specialisation (Coursera — Online Course)

6- Statistical Thinking in Python Part 1 & Part 2 (Datacamp — Paid — Online Course)

Feature Engineering

7- Machine Learning with Kaggle: Feature Engineering (Datacamp — Blog Post)

8- Feature Engineering for Continuous Numeric Data (Towards Data Science — Blog Post)

9- Feature Engineering for Categorical Data (Towards Data Science — Blog Post)

10- Feature Engineering for Text Data — Traditional Methods (Towards Data Science — Blog Post)

11- Feature Engineering for Text Data — Deep Learning Methods (Towards Data Science — Blog Post)

12- Prepare Text Data for Machine Learning (Machine Learning Mastery — Blog Post)

13- Feature Selection (Machine Learning Mastery — Blog Post)

14- An example kernel of Text Data Processing for Kaggle Mercari Competition (Kaggle — Kernel)

15- An example kernel of Feature Engineering for House Prices: Advanced Regression Techniques Competition (Kaggle — Kernel)

16- Advanced Feature Engineering Part 1 and Part 2 in Advanced ML Specialisation (Coursera — Online Course)

Machine Learning Models & Model Selection & Hyperparameter Tuning

17- Complete Machine Learning Project Walkthrough Part 2 (Towards Data Science — Blog Post)

18- Exploratory Study on ML algorithms (Kaggle — Kernel)

19- Comparing ML algorithms (Machine Learning Mastery — Blog Post)

20- Comparing various ML models (Kaggle — Kernel)

21- Supervised Learning Course (Datacamp — Paid — Online Course)

22- How to tune ML algorithm parameters (Machine Learning Mastery — Blog Post)

Finding Right Metric for Evaluation

23- Choosing the right metric for ML Models Part 1 and Part 2

24- Metrics To Evaluate Machine Learning Algorithms (Machine Learning Mastery — Blog Post)