Choosing a classification algorithm

First steps with scikit-learn – training a perceptron

Modeling class probabilities via logistic regression

Logistic regression and conditional probabilities

Training a logistic regression model with scikit-learn

Maximum margin classification with support vector machines

Maximum margin intuition

Dealing with a nonlinearly separable case using slack variables

Solving nonlinear problems using a kernel SVM

Kernel methods for linearly inseparable data

Using the kernel trick to find separating hyperplanes in a high-dimensional space

Decision tree learning

Maximizing IG

Building a decision tree

K-nearest neighbors – a lazy learning algorithm

Resolving ties

Parametric versus nonparametric models

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Predicting Continuous Target Variables with Regression Analysis

Introducing linear regression

Simple linear regression

Multiple linear regression

Exploring the Housing dataset

Loading the Housing dataset into a data frame

Visualizing the important characteristics of a dataset

Looking at relationships using a correlation matrix

Implementing an ordinary least squares linear regression model

Ordinary least squares (OLS)

Linear least squares (LLS)

Solving regression for regression parameters with gradient descent

Estimating the coefficient of a regression model via scikit-learn

Fitting a robust regression model using RANSAC

Evaluating the performance of linear regression models

Residual plots

Mean squared error (MSE)

Coefficient of determination

Using regularized methods for regression

Ridge Regression

Least absolute shrinkage and selection operator (LASSO)

Elastic Net

Turning a linear regression model into a curve – polynomial regression

Adding polynomial terms using scikit-learn

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Dealing with nonlinear relationships using random forests

Random forest regression

Working with Unlabeled Data – Clustering Analysis

Grouping objects by similarity using k-means

K-means clustering using scikit-learn

Prototype-based clustering

Hierarchical clustering

Density-based clustering

Organizing clusters as a hierarchical tree

Hierarchical clustering

Agglomerative and divisive hierarchical clustering

Dendrograms

Grouping clusters in bottom-up fashion

Single linkage agglomerative hierarchical clustering

Complete linkage agglomerative hierarchical clustering

Performing hierarchical clustering on a distance matrix

Locating regions of high density via DBSCAN

Density-based spatial clustering of applications with noise (DBSCAN)

Core point

Border point

Noise points

Graph-based clustering

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Modeling complex functions with artificial neural networks

Implementing a Multilayer Artificial Neural Network from Scratch

Deep neural network (DNN)

Single-layer neural network recap

Introducing the multilayer neural network architecture

Activating a neural network via forward propagation

Classifying handwritten digits

Mixed National Institute of Standards and Technology (MNIST)

Obtaining and preparing the MNIST dataset

Multi Layer Perceptron (MLP)

Training an artificial neural network

Computing the logistic cost function

Developing understanding of backpropagation

Training neural networks via backpropagation

About the convergence in neural networks

A few last words about the neural network implementation

Parallelizing Neural Network Training with TensorFlow

TensorFlow and training performance

Performance challenges

What is TensorFlow?

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Data Preprocessing

Hyperparameter Tuning

Building Good Training Sets

Handling categorical data

Ordinal Data

Nominal Data

Partitioning a dataset into separate training and test sets

Bringing features onto the same scale

Selecting meaningful features

Assessing feature importance with random forests

Compressing Data via Dimensionality Reduction

Unsupervised dimensionality reduction via principal component analysis

PCA

Supervised data compression via linear discriminant analysis

LDA

Using kernel principal component analysis for nonlinear mappings

KPCA

Learning Best Practices for Model Evaluation

Hyperparameter Tuning

Streamlining workflows with pipelines

Using k-fold cross-validation to assess model performance

Question Bank: All topics covered