Machine Learning

Machine Learning is a branch of artificial intelligence that enables computers to learn from data and make predictions or decisions without explicit programming.

Satyajit Pattnaik

Instructor

Included with Data Science & AI - From Python to GenAI

What you will learn?

Understanding ML Concepts
Hands-on Model Development
Data Preprocessing & Feature Engineering
Algorithm Implementation
Preparation for Advanced AI/ML

This Course Includes

Recorded Lessons: 86
Recorded Hours: 21
Duration: 21 days (Avg)

TD Assessment Available
Access on Mobile

Course Description

Machine Learning (ML) is a subset of Artificial Intelligence (AI) that focuses on building algorithms and models that enable computers to learn patterns from data and improve their performance over time without being explicitly programmed.

It involves various techniques, including supervised learning, unsupervised learning, and reinforcement learning, to solve real-world problems such as image recognition, natural language processing, recommendation systems, and predictive analytics. Machine Learning applications span across industries like healthcare, finance, e-commerce, and autonomous systems, making it a crucial technology in today's data-driven world.

Course Content

86 Lessons | 20hr 13min

Frequently Asked Questions

Basic knowledge of programming (preferably Python), statistics, and linear algebra is recommended. Familiarity with concepts like algorithms and data structures will be beneficial.

Yes, programming skills are essential, especially in Python, as it is one of the most popular languages used in Machine Learning. We will guide you through the relevant coding skills during the course.

Introduction of Machine Learning, Regression, Classification, Ensemble, Clustering and many more.

This course is designed for beginners who have a basic understanding of programming. We will cover the fundamentals of machine learning and progressively move to more advanced concepts.

The duration of the course varies depending on the learning pace.

(4.2)

20hr 13min

₹999

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Machine Learning

Satyajit Pattnaik

What you will learn?

This Course Includes

Course Description

Course Content

Introduction

Agenda 2m

Introduction 4m

Types of ML 18m

Use Cases Part 1 2m

Use Cases Part 2 1m

Pre-Requisites

Features 11m

Train Test Split 18m

Feature Scaling 11m

Standardization Example 4m

Normalization Example 2m

Feature Encoding 12m

Feature Encoding Practicals 10m

Regression

Regression 5m

Regression Metrics 35m

Regression Metrics (Practicals) 18m

SLR 15m

MLR 11m

LR Codes 26m

MLR Example 13m

Polynomial Regression 10m

PR Practical's 26m

Bias Variance Tradeoff 9m

Ridge 13m

Lasso 9m

Ridge Lasso Practical's 44m

Classification

Classification 6m

Type of Classification 6m

Log Loss 15m

Confusion Matrix 13m

AOC RUC 9m

Classification Report 7m

KNN 11m

KNN Excel Example 10m

Classification Practicals Part 1 14m

KNN Code 14m

DT 8m

DT Example Entropy 14m

DT Example Gini Index 20m

DT Code 8m

Visualizing DT 21m

RF New 5m

RF Code 5m

NB 14m

SVMs Part 1 14m

SVMs Part 2 14m

Logistic Regression 19m

Practicals So Far 27m

Issues in Classification 1 8m

Issues in Classification 2 11m

Practicals 35m

Ensemble

Ensemble 28m

Bagging 15m

Bagging vs RF 13m

Bagging Practicals 31m

Bagging Reg Practicals 20m

Boosting 11m

Adaboost 23m

Gradient Boosting 6m

CF vs LF 10m

Cross Entropy 6m

XGB 18m

Practicals 24m

Clustering

Clustering 16m

HC Practicals 14m

Hierarchical Clustering 15m

K Means Clustering 20m

K Means Practicals 2 19m

Mean Shift Theory + Practicals 11m