Let's explore a step-by-step process for approaching ML problems in Real life:

• Understanding the Business Requirements and the Nature of the Available Data.
• Classify the problem as Supervised/Unsupervised as well as Regression/Classification (in advance).
• Download, Clean & Explore data and Create New Features (if required) that may improve models.
• Create Training/Validation/Test sets of data and prepare the data for training ML models.
• Create a quick & easy baseline model to evaluate and benchmark future models.
• Select a modeling strategy, train a model, and tune hyperparameters to achieve optimal fit.
• Experiment and combine results from multiple strategies to get a better result.
• Interpret models, study individual predictions, and present your findings to the stakeholders.

[Check out the full scoop here]: ML Cheat Codes 2024

#	Topics	Links
1	Data-Gathering
2	Understanding-Data
3	Feature-Engineering
	Outlier Handlings
	PCA (Principal Component Analysis)
	UMAP (Uniform Manifold Approximation and Projection)
4	Tensors
5	Linear-Regression
6	Logistic-Regression
7	DT-and-RF
8	Voting-Ensemble
9	XGBoost
10	Support-Vector-Machine
11	Unsupervised-Clustering

This is the Ultimate Cheat Code for Machine Learning is what you will need in 2024. Even if you are beignner in Data Science or you're a seasoned expert. These codes will be your lifesaver for quick syntax reference. 🏄‍♂️🐍