Machine Learning Course For Absolute Beginners by Ankit Srivastava

Unlock the power of Machine Learning! Learn supervised, unsupervised and reinforcement learning with hands-on examples

What you'll learn

Supervised Machine Learning Algorithms and examples

Unsupervised Machine Learning Algorithms and examples

Reinforcement Algorithms and examples

Requirements

Basic understanding of Python Programming Language

Description

Are you curious about Machine Learning but have no prior experience? This course is perfect for you! Designed specifically for beginners, we break down the complexities of Machine Learning into simple, easy-to-understand concepts.Through real-world examples and practical exercises, you’ll explore the foundations of supervised learning, unsupervised learning, and reinforcement learning. Whether you're a student, a professional looking to upskill, or simply a tech enthusiast, this course will provide you with the skills to kickstart your Machine Learning journey.Learn how to perform Exploratory Data Analysis with Python - Pandas, Seaborn, Matplotlib etc. after performing EDA learn how to apply ML algorithms on the datasets, create models and evaluate them.Supervised Learning:Understand how algorithms learn from labeled data to make predictions.Explore linear regression, logistic regression, decision trees, and more.Hands-on example: Predicting house prices, Titanic Survival prediction, etc..Unsupervised Learning:Learn to uncover hidden patterns in data without predefined labels.Topics include clustering.Hands-on example: Customer segmentation for marketing.Reinforcement Learning:Discover how agents learn to make decisions through rewards and penalties.Key concepts:  Q-learning.Hands-on example.Key FeaturesBeginner-friendly, no ML knowledge required.Step by step tutorials on installing required IDEs and libraries.Step-by-step coding demonstrations in Python.Downloadable resources and cheat sheets for quick reference.

Overview

Section 1: Introduction

Lecture 1 Introduction

Lecture 2 What is Machine Learning - Theory

Lecture 3 Supervised Machine Learning - Classification - Theory

Lecture 4 Regression in Machine Learning - Theory and Maths

Lecture 5 What is Unsupervised Machine Learning - Theory

Lecture 6 Reinforcement Machine Learning - Theory

Section 2: IDE Installation and Usage

Lecture 7 How to install Python with System Environmental Variables for Data Science

Lecture 8 How Install Anaconda Navigator for Data Science and use it

Lecture 9 How to install Jupyter Notebook in computer for Data Analytics and Machine Learn

Lecture 10 How to use Google Colab for Data Analysis and Machine Learning with Python

Section 3: Exploratory Data Analysis with Example

Lecture 11 Exploratory Data Analysis on Titanic - Part 1 - Univariate Analysis

Lecture 12 Exploratory Data Analysis on Titanic - Part 2- Biivariate Analysis

Section 4: Regression Examples Using Python

Lecture 13 Linear Regression on One Variable Example

Lecture 14 Multiple Linear Regression Example

Lecture 15 What is Logistic Regression in Machine Learning - theory

Lecture 16 Logistic Regression on Study Hours vs Pass Fail Data

Section 5: Support Vector Machine Theory and Classification Example

Lecture 17 Support Vector Machine Algorithm - Theory

Lecture 18 Support Vector Machine Algorithm IRIS Data Classification using Python

Section 6: Random Forest Algorithm

Lecture 19 Random Forest Algorithm - Theory

Lecture 20 Random Forest Algorithm Example Using Python

Section 7: Decision Tree Algorithm

Lecture 21 Decision Tree Algorithm Theory

Lecture 22 Decision Tree Algorithm implementation with Python on Titanic Dataset

Section 8: Unsupervised Machine Learning - Example

Lecture 23 K Means Clustering Algorithm Part 1 - Theory ( Unsupervised)

Lecture 24 K means clustering algorithm example using Python - Part 2

Section 9: Q Learning Algorithm - Reinforcement Learning

Lecture 25 What is Q Learning Algorithm in Reinforcement Learning - Theory

Lecture 26 Q Learning Using Python Gym Module - Part 1

Lecture 27 Solving Problem Without Reinforcement Learning

Lecture 28 Applying Q Learning on Taxi V3 Environment - Using Python

Beginner Python Developers Curious about Machine Learning