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Projects in Machine Learning : Beginner To Professional
An Introduction to Machine Learning
Introduction (0:58)
What is Machine Learning (10:53)
Types and Applications of ML (25:45)
AI vs ML (9:43)
Essential Math for ML and AI (17:04)
Supervised Learning - part 1
Introduction to Supervised Learning (13:38)
Linear Methods for Classification (16:35)
Linear Methods for Regression (11:51)
Support Vector Machines (15:42)
Basis Expansions (11:00)
Model Selection Procedures (13:58)
Bonus! Supervised Learning Project in Python Part 1 (15:24)
Bonus! Supervised Learning Project in Python Part 2 (15:23)
Unsupervised Learning
Introduction to Unsupervised Learning (11:36)
Association Rules (14:13)
Cluster Analysis (13:19)
Reinforcement Learning (16:33)
Bonus! KMeans Clustering Project (14:14)
Neural Networks
Introduction to Neural Networks (12:26)
The Perceptron (10:20)
The Backpropagation Algorithm (12:19)
Training Procedures (13:37)
Convolutional Neural Networks (15:55)
Real World Machine Learning
Introduction to Real World ML (10:34)
Choosing an Algorithm (8:44)
Design and Analysis of ML Experiments (10:22)
Common Software for ML (10:46)
Final Project
Setting up OpenAI Gym (12:43)
Building and Training the Network Part 1 (16:14)
Building and Training the Network Part 2 (21:54)
Project 1 Board Game Review Prediction
Intro (1:39)
Board Game Review Prediction - Building the Dataset Part 1 (9:49)
Board Game Review Prediction - Building the Dataset Part 2 (16:41)
Board Game Review Prediction - Training the Models (15:17)
Project 2 Credit Card Fraud Detection
Intro (2:13)
Credit Card Fraud Detection - The Dataset (22:23)
Credit Card Fraud Detection - The Algorithms (20:41)
Project 3 Stock Market Clustering
Intro (1:55)
Stock Market Clustering - Building the Dataset Part 1 (16:08)
Stock Market Clustering - Building the Dataset Part 2 (12:36)
Stock Market Clustering - KMeans and PCA Part 1 (19:10)
Stock Market Clustering - KMeans and PCA Part 2 (20:47)
Project 4 Intro to Natural Language Processing
Intro (1:27)
Tokenizing, Stop Words, and Stemming (22:49)
Tagging, Chunking, and Named Entity Recognition (31:55)
Text Classification (23:57)
Project 5 Object Recognition
Intro (1:20)
Loading and Preprocessing the CIFAR10 Dataset (25:57)
Building and Deploying the All-CNN Network Part 1 (25:24)
Building and Deploying the All-CNN Network Part 2 (20:41)
Project 6 Image Super Resolution
Intro (1:09)
Quality Metrics and Preprocessing Images (34:07)
Image Super Resolution using Deep Learning (47:23)
Project 7 Text Classification
Intro (1:02)
Feature Engineering (48:07)
Deploying Sklearn Classifiers (26:58)
Project 8 - KMeans
Intro (1:07)
Preprocessing Images for Clustering (38:56)
Evaluation and Visualization (28:35)
Project 9 PCA
Intro (0:53)
The Elbow Method (22:50)
PCA Compression and Visualization (29:43)
Essential Math for ML and AI
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