The classic grid world example has been used to illustrate value and policy iterations with Dynamic Programming to solve MDP's Bellman equations. In the following grid, the agent will start at the south-west corner of the grid in (1,1) position and the goal is to move towards the north-east corner, to position (4,3). Once it reaches the goal, the agent will get a reward of +1. During the journey, it should avoid the danger zone (4,2), because this will give out a negative penalty of reward -1. The agent cannot get into the position where the obstacle (2,2) is present from any direction. Goal and danger zones are the terminal states, which means the agent continues to move around until it reaches one of these two states. The reward for all the other states would be -0.02. Here, the task is to determine the optimal policy (direction to move) for the agent at every state (11 states altogether), so that the agent...
Numerical Computing with Python
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Numerical Computing with Python
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Overview of this book
Data mining, or parsing the data to extract useful insights, is a niche skill that can transform your career as a data scientist Python is a flexible programming language that is equipped with a strong suite of libraries and toolkits, and gives you the perfect platform to sift through your data and mine the insights you seek. This Learning Path is designed to familiarize you with the Python libraries and the underlying statistics that you need to get comfortable with data mining.
You will learn how to use Pandas, Python's popular library to analyze different kinds of data, and leverage the power of Matplotlib to generate appealing and impressive visualizations for the insights you have derived. You will also explore different machine learning techniques and statistics that enable you to build powerful predictive models.
By the end of this Learning Path, you will have the perfect foundation to take your data mining skills to the next level and set yourself on the path to become a sought-after data science professional.
This Learning Path includes content from the following Packt products:
• Statistics for Machine Learning by Pratap Dangeti
• Matplotlib 2.x By Example by Allen Yu, Claire Chung, Aldrin Yim
• Pandas Cookbook by Theodore Petrou
Table of Contents (21 chapters)
Title Page
Contributors
About Packt
Preface
Free Chapter
Journey from Statistics to Machine Learning
Tree-Based Machine Learning Models
K-Nearest Neighbors and Naive Bayes
Unsupervised Learning
Reinforcement Learning
Hello Plotting World!
Visualizing Online Data
Visualizing Multivariate Data
Adding Interactivity and Animating Plots
Selecting Subsets of Data
Boolean Indexing
Index Alignment
Grouping for Aggregation, Filtration, and Transformation
Restructuring Data into a Tidy Form
Combining Pandas Objects
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Index
Customer Reviews