Machine Learning for Beginners

Preface

An Introduction to Machine Learning

• Conventional algorithm and machine learning
• Types of learning
• Working
• Applications
• History
• Conclusion

The Beginning: Pre-Processing and Feature Selection

• Introduction
• Dealing with missing values and ‘NaN’
• Converting a continuous variable to categorical variable
• Feature selection
• Chi-Squared test
• Pearson correlation
• Variance threshold
• Conclusion

Regression

• Introduction
• The line of best fit
• Gradient descent method
• Implementation
• Linear regression using SKLearn
• Experiments
• Finding weights without iteration
• Regression using K-nearest neighbors
• Conclusion

Classification

• Introduction
• Basics
• Classification using K-nearest neighbors
• Implementation of K-nearest neighbors
• The KNeighborsClassifier in SKLearn
• Experiments – K-nearest neighbors
• Logistic regression
• Logistic regression using SKLearn
• Experiments – Logistic regression
• Naïve Bayes classifier
• The GaussianNB Classifier of SKLearn
• Implementation of Gaussian Naïve Bayes
• Conclusion

Neural Network I – The Perceptron

• Introduction
• The brain
• The neuron
• The McCulloch Pitts model
• The Rosenblatt perceptron model
• Activation functions
• Implementation
• Learning
• Perceptron using sklearn
• Experiments
• Conclusion

Neural Network II – The Multi-Layer Perceptron

• Introduction
• History
• Introduction to multi-layer perceptrons
• Architecture
• Backpropagation algorithm
• Learning
• Implementation
• Multilayer perceptron using sklearn
• Experiments
• Conclusion
• Practical/Coding

Support Vector Machines

• Introduction
• The Maximum Margin Classifier
• Maximizing the margins
• The non-separable patterns and the cost parameter
• The kernel trick
• SKLEARN.SVM.SVC
• Conclusion

Decision Trees

• Introduction
• Basics
• Discretization
• Coming back
• Containing the depth of a tree
• Implementation of a decision tree using sklearn
• Experiments
• Conclusion

Clustering

• Introduction
• K-means
• Spectral clustering
• Hierarchical clustering
• Implementation
• Conclusion

Feature Extraction

• Introduction
• Fourier Transform
• Patches
• sklearn.feature_extraction.image.extract_patches_2d
• Histogram of oriented gradients
• Principal component analysis
• Conclusion
• Preface

Appendix 1: Cheat Sheet – Pandas

• Creating a Pandas series
• Indexing
• Slicing
• Common methods
• Boolean index
• DataFrame
• Adding a Column in a Data Frame
• Deleting column
• Addition of Rows
• Deletion of Rows
• unique
• Iterating a Pandas Data Frame

Appendix 2: Face Classification

• Introduction
• Data
• Methods
• Observation and Conclusion