Hands-On Unsupervised Learning with Python
•The Course Overview
•Benefits of Unsupervised Learning
•How Market Basket Analysis Works
•How Market Basket Analysis Works (Continued)
•The Apriori Algorithm – Preparing the Data
•Understanding and Implementing the Apriori Algorithm
•Finding Association Rules
•Visualizing and Interpreting Association Rules
•Unsupervised Learning and the Curse of Dimensionality
•Approaches to Dimensionality Reduction
•The Key Ideas Behind PCA
•The Key Ideas Behind PCA (Continued)
•The Linear Algebra Behind PCA
•The Linear Algebra Behind PCA (Continued)
•PCA in Practice
•PCA in Practice (Continued)
•Clustering – Key Concepts
•Clustering Algorithm in Practice
•Evaluate Clustering Results
•Case Study – K-Means and Wholesale Data
•Case Study – K-Means and Wholesale Data (Continued)
•Test Your Knowledge
•Hands-on Supervised Machine Learning with Python
•Getting Our Machine Learning Environment Setup
•Supervised Learning
•Hill Climbing and Loss Functions
•Model Evaluation and Data Splitting
•Introduction to Parametric Models and Linear Regression
•Implementing Linear Regression from Scratch
•Introduction to Logistic Regression Models
•Implementing Logistic Regression from Scratch
•Parametric Models –Pros/Cons
•The Bias/Variance Trade-off
•Introduction to Non-Parametric Models and Decision Trees
•Decision Trees
•Implementing a Decision Tree from Scratch
•Various Clustering Methods
•Implementing K-Nearest Neighbors from Scratch
•Non-Parametric Models –Pros/Cons
•Recommender Systems and an Introduction to Collaborative Filtering
•Matrix Factorization
•Matrix Factorization in Python
•Content-Based Filtering
•Neural Networks and Deep Learning
•Neural Networks
•Use Transfer Learning
•Supervised and Unsupervised Learning with Python
•Artificial Intelligence and Its Need
•Applications and Branches of AI
•Defining Intelligence Using Turing Test
•Making Machines Think Like Humans
•General Problem Solver
•Building an Intelligent Agent
•Installing Python 3 and Packages
•Loading Data
•Supervised Versus Unsupervised Learning
•What is Classification?
•Preprocessing Data
•Label Encoding
•Logistic Regression and Naïve Bayes Classifier
•Confusion Matrix
•Support Vector Machines
•Classifying Income Data
•What is Regression?
•Building a Single and Multivariable Regressor
•Estimating Housing Prices
•What is Ensemble Learning?
•What Are Decision Trees
•What are Random and Extremely Random Forests?
•Dealing with Class Imbalance
•Finding Optimal Training Parameters
•Computing Relative Feature Importance
•Predicting Traffic
•Clustering Data with K-Means Algorithm
•Estimating the Number of Clusters
•Estimating the Quality of Clustering
•Building a Classifier
•Segmenting the Market
•Creating a Training Pipeline
•Extracting the Nearest Neighbors
•Building a K-Nearest Neighbors Classifier
•Computing similarity scores
•Finding Similar Users
•Building a Movie Recommendation System
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