Fundamentals of Machine Learning with scikit-learn
•The Course Overview
•Machine Types and Learning Methods
•Data Formats
•Learnability
•Statistical Learning Approaches
•Elements of Information Theory
•Splitting Datasets
•Managing Data
•Data Scaling and Normalization
•Principal Component Analysis
•Linear Models and Its Example
•Linear Regression with scikit-learn
•Ridge, Lasso, and ElasticNet
•Regression Types
•Logistic Regression
•Stochastic Gradient Descent Algorithms
•Finding the Optimal Hyperparameters
•Classification Metrics
•ROC Curve
•Bayes’ Theorem
•Naive Bayes’ in scikit-learn
•scikit-learn Implementation
•Controlled Support Vector Machines
•Binary Decision Trees
•Decision Tree Classification with scikit-learn
•Ensemble Learning
•Clustering Basics
•DBSCAN and Spectral Clustering
•Evaluation Methods Based on the Ground Truth
•Agglomerative Clustering
•Implementing Agglomerative Clustering
•Connectivity Constraints
•User-Based Systems
•Content-Based Systems
•Hands-On Machine Learning with Python and scikit-Learn
•Demo Machine Learning Product
•Setting Up Our Anaconda Environment
•Launching an iPython Notebook
•Loading and Manipulating Data with Pandas
•ML Objective + Data Splitting and Common Pitfalls
•Descriptive Analytics With Pandas
•Planning Our Preprocessing Stages
•Handling Categorical Data
•Imputing Missing Values
•Handling Outliers
•Feature Extraction
•Feature Selection
•Pipelining Transformers
•Bias/Variance Trade-Off, Overfitting, and Underfitting
•Cross Validation
•Scoring Metrics
•Developing Model Baselines
•Hyper-Parameters and Strategic Search Ranges
•The Importance of Cross Validation in Grid Searches
•“Model Wars” Using Grid Searches
•Final Model Selection and Exposure to Holdout Set
•Model Selection – Where Do We Go Now?
•A Brief Note on Persistence and Version Perils
•Deploying ML Applications Behind a RESTful Endpoint Using Flask
•Learn Machine Learning in 3 Hours
•Operation of an Unsupervised Machine Learning Algorithm
•Operation of a Supervised Machine Learning Algorithm
•Avoid Overfitting and Splitting Data into Training and Testing Sets
•Data Cleaning, Conversion, and Preprocessing
•Using PCA to Easily Explore and Visualize Data
•What Does the Unsupervised K-Means Clustering Algorithm Do?
•Example Problem
•Data Preparation and Processing
•Implementing K-Means Clustering
•Improving Performance and Hyperparameter Fitting
•Operation of the K-Nearest-Neighbor Classification Algorithm
•Implementing K-Nearest-Neighbor Classification
•Operation of the Support Vector Machine Classification Algorithm
•Implementing Support Vector Machine Classification
•Operation of the Support Vector Machine Regression Algorithm
•Implementing Support Vector Machine Regression
•Operation of the Gradient Boosting Algorithm
•