Learn Machine Learning in 21 Days

Learn to create Machine Learning Algorithms in Python Data Science enthusiasts. Code templates included.

What you will learn

Master Machine Learning on Python

Make accurate predictions

Make robust Machine Learning models

Use Machine Learning for personal purpose

Have a great intuition of many Machine Learning models

Know which Machine Learning model to choose for each type of problem

Use SciKit-Learn for Machine Learning Tasks

Make predictions using linear regression, polynomial regression, and multiple regression

Classify data using K-Means clustering, Support Vector Machines (SVM), KNN, Decision Trees, Naive Bayes, etc.


Interested in the field of Machine Learning? Then this course is for you!

This course has been designed by Code Warriors the ML Enthusiasts so that we can share our knowledge and help you learn complex theories, algorithms, and coding libraries in a simple way.

We will walk you step-by-step into the World of Machine Learning. With every tutorial, you will develop new skills and improve your understanding of this challenging yet lucrative sub-field of Data Science.

This course is fun and exciting, but at the same time, we dive deep into Machine Learning. It is structured the following way:

You can do a lot in 21 Days. Actually, it’s the perfect number of days required to adopt a new habit!

What you’ll learn:-

1.Machine Learning Overview

2.Regression Algorithms on the real-time dataset

3.Regression Miniproject

4.Classification Algorithms on the real-time dataset

5.Classification Miniproject

6.Model Fine-Tuning

7.Deployment of the ML model





What is ML? Application & Types of ML

Data Preprocessing Techniques

What is NumPy?

Data Manipulation with Pandas


Simple Linear Regression

Multiple Linear Regression

Polynomial Regression

Support Vector Regression(SVR)

Decision Tree Regression

Random Forest Regression

Regression Mini Project


Logistic Regression

K-Nearest Neighbour

Support Vector Machine (SVM)

Kernel SVM

Naive Bayes Classification

Decision Tree Classification

Random Forest Classification

Classification Mini Project

Problems With ML

Underfitting and Overfitting

Model Selection

Cross Validation And Grid Search

Model Deployment

ML Model With Deployment

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