Machine Learning From Scratch

Before proceeding with Machine Learning Course, you should have some basic understanding of below mentioned concepts:

• What is Data?
• How we can collect Data?
• How we can analyze the collected Data?
• How we can deal with irregularities in Data?

We have provided two courses (Statistics and Data Handling) which will help you in covering the mentioned topics.

Once you are comfortable with the mentioned concepts, then you can easily start with this course.

Welcome to “Machine Learning from Scratch” – an immersive journey into the heart of machine learning.

In this course, we strip away the layers of abstraction and dive headfirst into the fundamentals. No black boxes, no shortcuts – just pure, unadulterated exploration of how machines learn from data.

Throughout this course, you’ll embark on a comprehensive exploration of key concepts, algorithms, and techniques that form the backbone of modern machine learning. From understanding the foundational principles of supervised and unsupervised learning to mastering complex algorithms like neural networks and support vector machines, every step of the way is meticulously crafted to empower you with a deep understanding.

But this isn’t just about theory. We firmly believe in learning by doing. That’s why each concept you encounter will be accompanied by hands-on exercises and projects designed to reinforce your understanding and sharpen your skills. You’ll tackle real-world datasets, experiment with cutting-edge tools and libraries, and gain the practical experience needed to tackle machine learning challenges with confidence.

Whether you’re a seasoned programmer looking to expand your skill set or a newcomer eager to explore the frontier of artificial intelligence, “Machine Learning from Scratch” offers something for everyone. Our comprehensive curriculum is structured to accommodate learners of all levels, guiding you from the basics to advanced topics with clarity and precision.

Let’s embark on this journey together, where curiosity meets computation, and every line of code brings us closer to unlocking the full potential of intelligent machines.

Are you ready to dive in? Let’s get started.

What is Classification in Machine Learning?

Performance Measures in Classification Models Part-1

Performance Measures(Confusion Matrix) in Classification Models Part-2

Performance Measures(Precision and Recall) in Classification Models Part-3

What is ROC Curve and how to Interpret it?

What is Multiclass Classification?

How to analyze error in classification models in machine learning?

What is MultiLabel classification?

How data science can help Insurance sector?

A Detailed Introducton to Poisson Regression

What is Linear Regression? Learn from Scratch

What is Gradient Descent ? Machine Learning from scratch

What is Stochastic Gradient Descent? Machine Learning from Scratch

What is Mini-batch Gradient Descent? Machine Learning from Scratch

Polynomial Regression | Machine Learning from Scratch

Learning Curves | Machine Learning from Scratch

Understanding and Implementing Batch Gradient Descent for Linear Regression in Python

Regularized Linear Models(Ridge Regression) | Machine Learning from Scratch

What is Lasso Regression? | Machine Learning from Scratch

Information Gain in Machine Learning

What is early stopping? | Machine Learning from Scratch

Support Vector Machines (SVM) Algorithms

ROC and AUC in Evaluating Classification Models

Soft Margin Classification | Machine Learning from Scratch

Nonlinear SVM Classification | Machine Learning from Scratch

Questions asked in Data Scientist Interviews Part 7

How can A linear model learn non-linear/discrete patterns?

Polynomial Kernel | Machine Learning from Scratch

Gaussian RBF Kernel | Machine Learning from Scratch

SVM Regression | Machine Learning from Scratch

Decision Trees | Machine Learning from Scratch

Linear Regression from Scratch: A Step-by-Step Guide

Gini Impurity or Entropy? How to decide the root node in decision tree?

Decision Tree Regression | Machine Learning from Scratch

What is Ensemble Learning? | Machine Learning from Scratch

What is Bagging and Pasting? Machine Learning from Scratch

Random Forests | Machine Learning from Scratch

Boosting( Ensemble) Trees | Machine Learning from Scratch

Gradient Boosting in Machine Learning

What is Stacking of Models in Machine Learning?

Introduction to Dimensionality Reduction

Main Approaches for Dimensionality Reduction

Choosing the Right Number of Dimensions in Dimensionality Reduction

Hierarchical Clustering in Machine Learning

Kernel PCA in Machine Learning

Locally linear Embedding For Dimensionality Reduction in Machine Learning

Introduction to Unsupervised Learning Techniques

What is Clustering? A Simple Approach

Implementation of K-Means Clustering in Machine Learning