If you want to learn how to use the many libraries of Python to extract impactful information from your data and present it as engaging visuals, then this is the ideal Learning Path for you. Some basic knowledge of Python is enough to get started with this Learning Path.

Chapter 1, Journey from Statistics to Machine Learning, introduces you to all the necessary fundamentals and basic building blocks of both statistics and machine learning. All fundamentals are explained with the support of both Python and R code examples across the chapter.

Chapter 2, Tree-Based Machine Learning Models, focuses on the various tree-based machine learning models used by industry practitioners, including decision trees, bagging, random forest, AdaBoost, gradient boosting, and XGBoost with the HR attrition example in both languages.

Chapter 3, K-Nearest Neighbors and Naive Bayes, illustrates simple methods of machine learning. K-nearest neighbors is explained using breast cancer data. The Naive Bayes model is explained with a message classification example using various NLP preprocessing techniques.

Chapter 4, Unsupervised Learning, presents various techniques such as k-means clustering, principal component analysis, singular value decomposition, and deep learning based deep auto encoders. At the end is an explanation of why deep auto encoders are much more powerful than the conventional PCA techniques.

Chapter 5, Reinforcement Learning, provides exhaustive techniques that learn the optimal path to reach a goal over the episodic states, such as the Markov decision process, dynamic programming, Monte Carlo methods, and temporal difference learning. Finally, some use cases are provided for superb applications using machine learning and reinforcement learning.

Chapter 6, Hello Plotting World!, covers the basic constituents of a Matplotlib figure, as well as the latest features of Matplotlib version 2.

Chapter 7, Visualizing Online Data, teaches you how to design intuitive infographics for effective storytelling through the use of real-world datasets.

Chapter 8, Visualizing Multivariate Data, gives you an overview of the plot types that are suitable for visualizing datasets with multiple features or dimensions.

Chapter 9, Adding Interactivity and Animating Plots, shows you that Matplotlib is not limited to creating static plots. You will learn how to create interactive charts and animations.

Chapter 10, Selecting Subsets of Data, covers the many varied and potentially confusing ways of selecting different subsets of data.

Chapter 11, Boolean Indexing, covers the process of querying your data to select subsets of it based on Boolean conditions.

Chapter 12, Index Alignment, targets the very important and often misunderstood index object. Misuse of the Index is responsible for lots of erroneous results, and these recipes show you how to use it correctly to deliver powerful results.

Chapter 13, Grouping for Aggregation, Filtration, and Transformation, covers the powerful grouping capabilities that are almost always necessary during a data analysis. You will build customized functions to apply to your groups.

Chapter 14, Restructuring Data into a Tidy Form, explains what tidy data is and why it’s so important, and then it shows you how to transform many different forms of messy datasets into tidy ones.

Chapter 15, Combining Pandas Objects, covers the many available methods to combine DataFrames and Series vertically or horizontally. We will also do some web-scraping to compare President Trump's and Obama's approval rating and connect to an SQL relational database.

This book assumes that you know the basics of Python and R and how to install the libraries. It does not assume that you are already equipped with the knowledge of advanced statistics and mathematics, like linear algebra and so on.

The following versions of software are used throughout this book, but it should run fine with any more recent ones as well:

>>> import numpy as np 
>>> from scipy import stats 
>>> data = np.array([4,5,1,2,7,2,6,9,3]) 
# Calculate Mean 
>>> dt_mean = np.mean(data) ; 
print ("Mean :",round(dt_mean,2)) 

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