•Getting Started with Machine Learning in Python
•The Course Overview
•Machine Learning versus RuleBased Programming
•Understanding What Machine Learning Can Do Using the Tasks Framework
•Creating MachineLearned Models with Python and scikitlearn
•Supervised Versus Unsupervised Learning
•We will fix your machine learning models by understanding your data source
•Dealing with Missing Values An Example
•Standardization and Normalization to Deal with Variables with Different Scales
•Eliminating Duplicate Entries
•How Do We Learn Rules to Classify Objects
•Understanding Logistic Regression Your First Classifier
•Applying Logistic Regression to the Iris Classification Task
•Closing Our First Machine Learning Pipeline with a Simple Model Evaluator
•Creating Formulas That Predict the Future A House Price Example
•Understanding Linear Regression Your First Regressor
•Applying Linear Regression to the Boston House Price Task
•Evaluating Numerical Predictions with Least Squares
•Exploring Unsupervised Learning and Its Usefulness
•Finding Groups Automatically with Kmeans Clustering
•Reducing the Number of Variables in Your Data with PCA
•Smooth out Your Histograms with Kernel Density Estimation
•Create Explainable Models with Decision Trees
•Automatic Feature Engineering with Support Vector Machines
•Deal with Nonlinear Relationships with Polynomial Regression
•Reduce the Number of Learned Rules with Regularization
•Test Your Knowledge
•Python Machine Learning Tips Tricks and Techniques
•Using Feature Scaling to Standardize Data
•Implementing Feature Engineering with Logistic Regression
•Extracting Data with Feature Selection and Interaction
•Combining All Together
•Build Model Based on RealWorld Problems
•Support Vector Machines
•Implementing kNN on the Data Set
•Decision Tree as Predictive Model
•Tricks with Dimensionality Reduction
•Random Forest for Classification
•Gradient Boosting Trees and Bayes Optimization
•CatBoost to Handle Categorical Data
•Implement Blending
•Implement Stacking
•MemoryBased Collaborative Filtering
•ItemtoItem Recommendation with kNN
•Applying Matrix Factorization on Datasets
•Wordbatch for RealWorld Problem
•Validation Dataset Tuning
•Regularizing Model to Avoid Overfitting
•Adversarial Validation
•Perform Metric Selection on Real Data
•Building Predictive Models with Machine Learning and Python
•Introduction to Machine Learning
•Meet the Python Machine Learning Stack
•Making Sure It Works in Your Computer
•Exploring Your First Dataset
•Building Your First Model
•Assessing Your Model
•Finding Issues with Your Data
•Using Pandas to Get Your Data Ready for Modeling
•Building a Model to Assess Your Chances of Surviving the Titanic
•What Makes Models Truly Different
•Understanding the Advantages and Shortcomings of the Most Popular Models
•Trying and Failing to Use an SVM a Random Forest and a Linear Model
•Fixing Our Issues with Our SVM Model
•Fixing Our Issues with the Random Forest Model
•What Does it Mean to Tune a Model Theory
•Grid Search Just Try Everything
•Tune a Linear Model to Predict House Prices
•Tune an SVM to Predict a Politicians Party Based on Their Voting Record
•Advanced Libraries for Machine Learning
•Good Next Steps Kaggle Hackathons YouTube Channels and More
•Troubleshooting Python Machine Learning
•Splitting Your Datasets for Train Test and Validate
•Persist Your Hard Earned Models by Saving Them to Disk
•Calculate Word Frequencies Efficiently in Good ol Python
•Transform Your Variable Length Features into OneHot Vectors
•Finding the Most Important Features in Your Classifier
•Predicting Multiple Targets with the Same Dataset
•Retrieving the Best Estimators after Grid Search
•Regress on Your Pandas Data Frame with Simple Statsmodels OLS
•Extracting Decision Tree Rules from scikitlearn
•Finding Out Which Features Are Important in a Random Forest Model
•Classifying with SVMs When Your Data Has Unbalanced Classes
•Computing TrueFalse PositivesNegatives after in scikitlearn
•Labelling Dimensions with Original Feature Names after PCA
•Clustering Text Documents with scikitlearn Kmeans
•Listing Word Frequency in a Corpus Using Only scikitlearn
•Polynomial Kernel Regression Using Pipelines
•Visualize Outputs Over TwoDimensions Using NumPys Meshgrid
•Drawing Out a Decision Tree Trained in scikitlearn
•Clarify Your Histogram by Labelling Each Bin
•Centralizing Your Color Legend When You Have Multiple Subplots
•Machine Learning versus Rule-Based Programming
•Creating Machine-Learned Models with Python and scikit-learn
•Dealing with Missing Values – An Example
•How Do We Learn Rules to Classify Objects?
•Understanding Logistic Regression – Your First Classifier
•Creating Formulas That Predict the Future – A House Price Example
•Understanding Linear Regression – Your First Regressor
•Finding Groups Automatically with K-means Clustering
•Python Machine Learning Tips, Tricks, and Techniques
•Build Model Based on Real-World Problems
•Memory-Based Collaborative Filtering
•Item-to-Item Recommendation with kNN
•Wordbatch for Real-World Problem
•What Makes Models Truly Different?
•Trying (and Failing) to Use an SVM, a Random Forest and a Linear Model
•What Does it Mean to Tune a Model (Theory)?
•Grid Search – Just Try Everything!
•Tune an SVM to Predict a Politician’s Party Based on Their Voting Record
•Good Next Steps – Kaggle, Hackathons, YouTube Channels, and More
•Splitting Your Datasets for Train, Test, and Validate
•Calculate Word Frequencies Efficiently in Good ol’ Python
•Transform Your Variable Length Features into One-Hot Vectors
•Extracting Decision Tree Rules from scikit-learn
•Computing True/False Positives/Negatives after in scikit-learn
•Clustering Text Documents with scikit-learn K-means
•Listing Word Frequency in a Corpus Using Only scikit-learn
•Visualize Outputs Over Two-Dimensions Using NumPy’s Meshgrid
•Drawing Out a Decision Tree Trained in scikit-learn
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