Chapter 1: Introduction and First Steps – Take a Deep Breath
Who is using Python today?
Setting up the environment
What you need for this course
How you can run a Python program
How is Python code organized
Guidelines on how to write good code
Chapter 2: Object-oriented Design
Introducing object-oriented
Specifying attributes and behaviors
Hiding details and creating the public interface
Chapter 3: Objects in Python
Organizing module contents
Chapter 4: When Objects Are Alike
Chapter 5: Expecting the Unexpected
Chapter 6: When to Use Object-oriented Programming
Adding behavior to class data with properties
Chapter 7: Python Data Structures
Chapter 8: Python Object-oriented Shortcuts
Python built-in functions
An alternative to method overloading
Functions are objects too
Chapter 9: Strings and Serialization
Chapter 10: The Iterator Pattern
Chapter 11: Python Design Patterns I
Chapter 12: Python Design Patterns II
The abstract factory pattern
Chapter 13: Testing Object-oriented Programs
Imitating expensive objects
How much testing is enough?
Chapter 15: Introducing Data Analysis and Libraries
Data analysis and processing
An overview of the libraries in data analysis
Python libraries in data analysis
Chapter 16: NumPy Arrays and Vectorized Computation
Data processing using arrays
Linear algebra with NumPy
Chapter 17: Data Analysis with pandas
An overview of the pandas package
The pandas data structure
The essential basic functionality
Indexing and selecting data
Working with missing data
Advanced uses of pandas for data analysis
Chapter 18: Data Visualization
The matplotlib API primer
Plotting functions with pandas
Additional Python data visualization tools
Chapter 19: Time Series
Working with date and time objects
Downsampling time series data
Upsampling time series data
Chapter 20: Interacting with Databases
Interacting with data in text format
Interacting with data in binary format
Interacting with data in MongoDB
Interacting with data in Redis
Chapter 21: Data Analysis Application Examples
Chapter 22: Getting Started with Data Mining
A simple affinity analysis example
A simple classification example
Chapter 23: Classifying with scikit-learn Estimators
Preprocessing using pipelines
Chapter 24: Predicting Sports Winners with Decision Trees
Sports outcome prediction
Chapter 25: Recommending Movies Using Affinity Analysis
The movie recommendation problem
The Apriori implementation
Extracting association rules
Chapter 26: Extracting Features with Transformers
Creating your own transformer
Chapter 27: Social Media Insight Using Naive Bayes
Chapter 28: Discovering Accounts to Follow Using Graph Mining
Chapter 29: Beating CAPTCHAs with Neural Networks
Artificial neural networks
Improving accuracy using a dictionary
Chapter 30: Authorship Attribution
Attributing documents to authors
Chapter 31: Clustering News Articles
Extracting text from arbitrary websites
Chapter 32: Classifying Objects in Images Using Deep Learning
Application scenario and goals
Setting up the environment
Chapter 33: Working with Big Data
Application scenario and goals
Chapter 34: Next Steps…
Chapter 1 – Getting Started with Data Mining
Chapter 2 – Classifying with scikit-learn Estimators
Chapter 3: Predicting Sports Winners with Decision Trees
Chapter 4 – Recommending Movies Using Affinity Analysis
Chapter 5 – Extracting Features with Transformers
Chapter 6 – Social Media Insight Using Naive Bayes
Chapter 7 – Discovering Accounts to Follow Using Graph Mining
Chapter 8 – Beating CAPTCHAs with Neural Networks
Chapter 9 – Authorship Attribution
Chapter 10 – Clustering News Articles
Chapter 11 – Classifying Objects in Images Using Deep Learning
Chapter 12 – Working with Big Data
Chapter 35: Giving Computers the Ability to Learn from Data
How to transform data into knowledge
The three different types of machine learning
An introduction to the basic terminology and notations
A roadmap for building machine learning systems
Using Python for machine learning
Chapter 36: Training Machine Learning Algorithms for Classification
Artificial neurons – a brief glimpse into the early history of machine learning
Implementing a perceptron learning algorithm in Python
Adaptive linear neurons and the convergence of learning
Chapter 37: A Tour of Machine Learning Classifiers Using scikit-learn
Choosing a classification algorithm
First steps with scikit-learn
Modeling class probabilities via logistic regression
Maximum margin classification with support vector machines
Solving nonlinear problems using a kernel SVM
K-nearest neighbors – a lazy learning algorithm
Chapter 38: Building Good Training Sets – Data Preprocessing
Dealing with missing data
Handling categorical data
Partitioning a dataset in training and test sets
Bringing features onto the same scale
Selecting meaningful features
Assessing feature importance with random forests
Chapter 39: Compressing Data via Dimensionality Reduction
Unsupervised dimensionality reduction via principal component analysis
Supervised data compression via linear discriminant analysis
Using kernel principal component analysis for nonlinear mappings
Chapter 40: Learning Best Practices for Model Evaluation and Hyperparameter Tuning
Streamlining workflows with pipelines
Using k-fold cross-validation to assess model performance
Debugging algorithms with learning and validation curves
Fine-tuning machine learning models via grid search
Looking at different performance evaluation metrics
Chapter 41: Combining Different Models for Ensemble Learning
Implementing a simple majority vote classifier
Evaluating and tuning the ensemble classifier
Bagging – building an ensemble of classifiers from bootstrap samples
Leveraging weak learners via adaptive boosting
Chapter 42: Predicting Continuous Target Variables with Regression Analysis
Introducing a simple linear regression model
Exploring the Housing Dataset
Implementing an ordinary least squares linear regression model
Fitting a robust regression model using RANSAC
Evaluating the performance of linear regression models
Using regularized methods for regression
Turning a linear regression model into a curve – polynomial regression
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