Machine Learning Essentials (2023)

Kickstart Machine Learning, understand maths behind essential algorithms, implement them in python & build 8+ projects!

What you'll learn

Jumpstart the world of AI & ML

Maths of Machine Learning

Regression & Classification Techniques

Linear & Logistic Regression

K-Nearest Neighbours, K-Means

Naive Bayes, Text Classification

Decision Trees & Random Forests

Ensemble Learning - Bagging & Boosting

Dimensionality Reduction

Neural Networks

8+ Hands on Projects

Requirements

Python Programming

Basics of Numpy, Pandas, Matplotlib

Description

Read to jumpstart the world of Machine Learning & Artificial intelligence?This hands-on course is designed for absolute beginners as well as for proficient programmers who want kickstart Machine Learning for solving real life problems. You will learn how to work with data, and train models capable of making "intelligent decisions" Data Science has one of the most rewarding jobs of the 21st century and fortune-500 tech companies are spending heavily on data scientists! Data Science as a career is very rewarding and offers one of the highest salaries in the world. Unlike other courses, which cover only library-implementations this course is designed to give you a solid foundation in Machine Learning by covering maths and implementation from scratch in Python for most statistical techniques.This comprehensive course is taught by Prateek Narang & Mohit Uniyal, who not just popular instructors but also have worked in Software Engineering and Data Science domains with companies like Google. They have taught thousands of students in several online and in-person courses over last 3+ years. We are providing you this course to you at a fraction of its original cost! This is action oriented course, we not just delve into theory but focus on the practical aspects by building 8+ projects. With over 170+ high quality video lectures, easy to understand explanations and complete code repository this is one of the most detailed and robust course for learning data science.Some of the topics that you will learn in this course.Logistic RegressionLinear RegressionPrincipal Component AnalysisNaive BayesDecision TreesBagging and BoostingK-NNK-MeansNeural NetworksSome of the concepts that you will learn in this course.Convex OptimisationOverfitting vs UnderfittingBias Variance TradeoffPerformance MetricsData Pre-processingFeature EngineeringWorking with numeric data, images & textual dataParametric vs Non-Parametric TechniquesSign up for the course and take your first step towards becoming a machine learning engineer! See you in the course!

Overview

Section 1: Introduction

Lecture 1 Course Overview

Lecture 2 Artificial Intelligence

Lecture 3 Machine Learning

Lecture 4 Deep Learning

Lecture 5 Computer Vision

Lecture 6 Natural Language Processing

Lecture 7 Automatic Speech Recognition

Lecture 8 Reinforcement Learning

Lecture 9 Pre-requisites

Lecture 10 Code Repository

Section 2: Supervised vs Unsupervised Learning

Lecture 11 Supervised Learning Introduction

Lecture 12 Supervised Learning Example

Lecture 13 Unsupervised Learning

Section 3: Linear Regression

Lecture 14 Introduction to Linear Regression

Lecture 15 Notation

Lecture 16 Hypothesis

Lecture 17 Loss / Error Function

Lecture 18 Training Idea

Lecture 19 Gradient Descent Optimisation

Lecture 20 Gradient Descent Code

Lecture 21 Gradient Descent - for Linear Regression

Lecture 22 The Math of Training

Lecture 23 Code 01 - Data Generation

Lecture 24 Code 02 - Data Normalisation

Lecture 25 Code 03 - Train Test Split

Lecture 26 Code 04 - Modelling

Lecture 27 Code 05 - Predictions

Lecture 28 R2 Score

Lecture 29 Code 06 - Evaluation

Lecture 30 Code 07 - Visualisation

Lecture 31 Code 08 - Trajectory [Optional]

Section 4: Linear Regression - Multiple Features

Lecture 32 Introduction

Lecture 33 Hypothesis

Lecture 34 Loss Function

Lecture 35 Training & Gradient Updates

Lecture 36 Code 01 - Data Prep

Lecture 37 Code 02 - Hypothesis

Lecture 38 Code 03 - Loss Function

Lecture 39 Code 04 - Gradient Computation

Lecture 40 Code 05 - Training Loop

Lecture 41 A Note about Shapes

Lecture 42 Code 06 - Evaluation

Lecture 43 Linear Regression using Sk-Learn

Section 5: Logistic Regression

Lecture 44 Binary Classification Introduction

Lecture 45 Notation

Lecture 46 Hypothesis Function

Lecture 47 Binary Cross-Entropy / Loss Function

Lecture 48 Gradient Update Rule

Lecture 49 Code 01 - Data Prep

Lecture 50 Code 02 - Hypothesis / Logit Model

Lecture 51 Code 03 - Binary Cross Entropy Loss

Lecture 52 Code 04 - Gradient Computation

Lecture 53 Code 05 - Training Loop

Lecture 54 Code 06 - Visualise Decision Boundary

Lecture 55 Code 07 - Predictions & Accuracy

Lecture 56 Logistic Regression using Sk-Learn

Lecture 57 Multiclass Classification : One Vs Rest

Lecture 58 Multiclass Classification : One Vs One

Section 6: Dimensionality Reduction/ Feature Selection

Lecture 59 Curse of Dimensionality

Lecture 60 Feature Selection Vs. Feature Extraction

Lecture 61 Filter Method

Lecture 62 Wrapper Method

Lecture 63 Embedded Method

Lecture 64 Feature Selection - Code

Section 7: Principal Component Analysis (PCA)

Lecture 65 Introduction to PCA

Lecture 66 Conceptual Overview of PCA

Lecture 67 Maximising Variance

Lecture 68 Minimising Distances

Lecture 69 Eigen Values & Eigen Vectors

Lecture 70 PCA Summary

Lecture 71 Understanding Eigen Values

Lecture 72 PCA Code

Lecture 73 Choosing the right dimensions

Section 8: K-Nearest Neigbours

Lecture 74 Introduction

Lecture 75 KNN Idea

Lecture 76 KNN Data Prep

Lecture 77 KNN Algorithm Code

Lecture 78 Euclidean and Manhattan Distance

Lecture 79 Deciding value of K

Lecture 80 KNN and Data Standardisation

Lecture 81 KNN Pros and Cons

Lecture 82 KNN using Sk-Learn

Section 9: PROJECT - Face Recognition

Lecture 83 OpenCV - Working with Images

Lecture 84 OpenCV - Video Input from WebCam

Lecture 85 Object Detection using Haarcascades

Lecture 86 Face Detection in Images

Lecture 87 Face Detection in Live Video

Lecture 88 Face Recognition Project Intro

Lecture 89 Face Recognition 01 - Data Collection

Lecture 90 Face Recognition 02 - Loading Data

Lecture 91 Face Recognition 03 - Predictions using KNN

Section 10: K-Means

Lecture 92 K-Means Algorithm

Lecture 93 Code 01 - Data Prep

Lecture 94 Code 02 - Init Centers

Lecture 95 Code 03 - Assigning Points

Lecture 96 Code 04 - Updating Centroids

Lecture 97 Code 05 - Visualizing K-Means & Results

Section 11: Project - Dominant Color Extraction

Lecture 98 Introduction

Lecture 99 Reading Images

Lecture 100 Finding Clusters

Lecture 101 Dominant Color Swatches

Lecture 102 Image in K-Colors

Section 12: Naive Bayes Algorithm

Lecture 103 Bayes Theorem

Lecture 104 Derivation of Bayes Theorem

Lecture 105 Bayes Theorem Question

Lecture 106 Naive Bayes Algorithm

Lecture 107 Naive Bayes for Text Classification

Lecture 108 Computing Likelihood

Lecture 109 Understanding Golf Dataset

Lecture 110 CODE - Prior Probability

Lecture 111 CODE - Conditional Probability

Lecture 112 CODE - Likelihood

Lecture 113 CODE - Prediction

Lecture 114 Implementing Naive Bayes - Sklearn

Section 13: Multinomial Naive Bayes

Lecture 115 Multinomial Naive Bayes

Lecture 116 Laplace Smoothing

Lecture 117 Multinomial Naive Bayes | Example

Lecture 118 Bernoulli Naive Bayes

Lecture 119 Bernoulli Naive Bayes | Example

Lecture 120 Bias Variance Tradeoff

Lecture 121 Gaussian Naive Bayes

Lecture 122 CODE - Variants of Naive Bayes

Section 14: PROJECT : Spam Classifier

Lecture 123 Project Overview

Lecture 124 Data Clearning

Lecture 125 WordCloud

Lecture 126 Text Featurization

Lecture 127 Model Building

Lecture 128 Model Evaluation

Section 15: Decision Trees

Lecture 129 Decision Trees Introduction

Lecture 130 Decision Trees Example

Lecture 131 Entropy

Lecture 132 CODE : Entropy

Lecture 133 Information Gain

Lecture 134 CODE : Split Data

Lecture 135 CODE : Information Gain

Lecture 136 Construction of Decision Trees

Lecture 137 Stopping Conditions

Section 16: Decision Trees Implementation

Lecture 138 CODE - Decision Tree Node

Lecture 139 CODE - Train Decision Tree

Lecture 140 CODE - Assign Target Variable to Each Node

Lecture 141 CODE - Stopping Conditions

Lecture 142 CODE - Train Child Nodes

Lecture 143 CODE - Explore Decision Tree Model

Lecture 144 CODE - Prediction

Lecture 145 Handling Numeric Features

Lecture 146 Bias Variance Tradeoff

Lecture 147 Decision Trees for Regression

Lecture 148 Decision Tree Code - Sklearn

Section 17: PROJECT : Titanic Survival Prediction

Lecture 149 Project Overview

Lecture 150 Exploratory Data Analysis

Lecture 151 Exploratory Data Analysis - II

Lecture 152 Data Preparation for ML Model

Lecture 153 Handling Missing Values

Lecture 154 Decision Tree Model Building

Lecture 155 Visualize Decision Tree

Section 18: Ensemble Learning : Bagging

Lecture 156 Ensemble Learning

Lecture 157 Bagging Model

Lecture 158 Why Bagging Helps

Lecture 159 Random Forest Algorithm

Lecture 160 Bias Variance Tradeoff

Lecture 161 CODE : Random Forest

Section 19: Ensemble Learning : Boosting

Lecture 162 Boosting Introduction

Lecture 163 Boosting Intuition

Lecture 164 Boosting : Mathematical Formulation

Lecture 165 Concept of Pseudo Residuals

Lecture 166 GBDT Algorithm

Lecture 167 Bias Variance Tradeoff

Lecture 168 CODE - Gradient Boosting Decision Trees

Lecture 169 XGBoost

Lecture 170 Adaptive Boosting (AdaBoost)

Section 20: PROJECT : Customer Churn Prediction

Lecture 171 Project Overview

Lecture 172 Exploratory Data Analysis

Lecture 173 Data Visualisation

Lecture 174 Finding relations

Lecture 175 Data Preparation

Lecture 176 Model Building

Lecture 177 Hyperparameter tuning

Section 21: Deep Learning Introduction - Neural Network

Lecture 178 Biological Neural Network

Lecture 179 A Neuron

Lecture 180 How does a perceptron Learns?

Lecture 181 Gradient Descent Updates

Lecture 182 Neural Networks

Lecture 183 3 Layer NN

Lecture 184 Why Neural Nets?

Lecture 185 Tensorflow Playground

Lecture 186 CODE -Data Preparation

Lecture 187 CODE - Model Building

Lecture 188 CODE - Model Training and Testing

Section 22: PROJECT : Pokemon / Image Classification

Lecture 189 Introduction

Lecture 190 The Data

Lecture 191 Structured Data

Lecture 192 Data Loading

Lecture 193 Data Preprocessing

Lecture 194 Model Architecture

Lecture 195 Softmax Function

Lecture 196 Model Training

Lecture 197 Model evaluation

Lecture 198 Predictions

Programmers who are curious to about Machine Learning and Artificial Intellgence,Working professionals who want to build a career in data science,Developers who wants to learn a new skill and build ML based projects,University and college students who want to strengthen their understanding of Machine Learning