Machine learning and Python: Introduction to NumPy, SciPy, and Matplotlib-Installing Python - Chewing data efficiently with NumPy and intelligently with SciPy-Learning NumPy-Our first (tiny) machine learning application-Reading in the data-Preprocessing and cleaning the data - Choosing the right model and learning algorithm -Starting with a simple straight line-Training and testing.

Learning How to Classify with Real-world Examples: The Iris dataset-The first step is visualization -Building our first classification model- Building more complex classifiers- A more complex dataset and a more complex classifier- Binary and multiclass classification.

Clustering- Finding Related Posts: Measuring the relatedness of posts- Preprocessing – similarity measured as similar number of common words- Clustering- Solving our initial challenge- Tweaking the parameters- Topic Modeling- Latent Dirichlet Allocation- Comparing similarity in topic space.

Classification – Detecting Poor Answers- Fetching the data- Creating our first classifier- Deciding how to improve- Classification II – Sentiment Analysis- Introducing the Naive Bayes classifier- Creating our first classifier and tuning it- Cleaning tweets.

Regression – Recommendations- Predicting house prices with regression- Penalized regression- P greater than N scenarios. Big(ger) Data: Learning about big data -Using jug to break up your pipeline into tasks- Using Amazon Web Services (AWS).