Computer Programming: 2 Books in 1: Machine Learning for Beginners + Python for Beginners

Length: 330 pages
Edition: 1
Language: English
Publication Date: 2019-12-27
ISBN-10: B08383L5L9
Sales Rank: #1851992 (See Top 100 Books)

Are you a newbie to Computer Programming? Are you curious to learn why Python is the best way to begin?

Probably you want to start by learning practical techniques that could easily make you become a programmer, developer or simply make you learn how it goes. You probablywould like to learn with examples and images of how the code was written, and afterward an explanation of what is the task and job of each element on the code.

This 2 books in 1 is exactly what you’re looking for !

“Computer Programming” is a complete guide to start learning Python and Machine Learning.

Machine Learning is one of the most exciting developments to come up of computer science since its founding and Python is one of the most used programming language all around the world. The knowledge of Machine Learning and Python is going to give you lots of advantages on your programming technique.

Here’s what you’ll find inside:

Introduction to Data Science, AI, Machine Learning and uses of Python;
See how Machine Learning is being used by companies like Amazon, Netflix and Google;
Benefits of Python and differences between it and other languages;
Python basic concepts, as well as Conditional Statements, Loops, Functions, Modules, OOP etc..;
And much more…

Introduction
Chapter 1: What is Machine Learning – and the evolution of machines
    A Quick History of Computer Science
    The Evolution of Machines
    The Evolution of Artificial Intelligence
    Machine Learning
    Training and Test Data
    Machine Learning vs. Artificial Intelligence and Deep Learning
    What Sectors and Industries Use Machine Learning
        Government
        Financial Services
        Transportation and Shipment
        Mining, Oil and Gas
        Retail and online marketing
        Healthcare
Chapter 2: Introduction to Data Science
    What is Data Science?
    Is Data Science Really a Thing?
    The Role of Machine Learning in Data Science
    The Tasks of Data Science
        Predictive Analysis
        Prescriptive Analytics
        Pattern Recognition
        Classification and Anomaly Detection
    Examples of Data Science in Use
    Data Science as a Career
Chapter 3: Supervised Machine Learning
    Supervised Learning
    Selecting an Algorithm
        Bias Errors
        Variance Errors
        Noisy Data
    Classification vs. Regression
    Variations on Supervised Learning
Chapter 4:Unsupervised Learning
    Clustering
        K-Means Clustering
        Mean Shift Clustering
    Neural Networks
    Markov Algorithm
Chapter 5: Reinforcement Learning
    Types of Reinforcement Learning
    The State Model of Reinforcement Learning
    Short Term Rewards
    When are Rewards Given
    Episodes
    Continuous Tasks
    Q Tables
Chapter 6: Algorithms for Supervised Learning
    Linear Regression
    Logistic Regression
    Decision Trees
    Random Forest
    Nearest Neighbor
Chapter 7: Tips for Your Work in Machine Learning
    Understand the Difference Between Prediction and Classification
    Knowing Which Type of Learning to Use
    Data Selection
    Tools to Use
    Practice
    Utilize Mixed Learning Models
    Have Realistic Expectations
Chapter 8: The Future of Machine Learning
Introduction
Chapter 1: Introductory Chapter
    What Is Python?
    Which Are the Advantages of Using Python?
    Differences Between Python and Other Programming Languages
    Introduction to Data Science
    Machine Learning
        What Is Machine Learning?
        How Is ML Classified?
        What Are Some of the Current Applications of ML?
    Artificial Intelligence (AI)
        Fundamentals of Artificial Intelligence
        Why Is Artificial Intelligence Used?
        Artificial Intelligence and Programming Languages
    Python and Artificial Intelligence
        What Are the Features and Advantages of Python with AI?
        Characteristics of Artificial Intelligence
        In Which Areas Is AI Applied?
    Why Python? Why It Is Highly Recommended in the Above Topics?
    How to Start Installing Python and Its Interpreter?
    Using Python with Shell and IDLE
Chapter 2: Basic Concepts
    How Can We Declare a Variable?
    Data Types
    Operators
    Interactivity and Its Importance
    Basic Functions for Interactivity
        Standard Output with print()
        Standard Input with input()
Chapter 3: Conditionals
    If Statement
    Else Statement
    Elif Statement
Chapter 4: Loops
    While Loop
    Statements Used in the While Loop
    For Loops
Chapter 5: Functions
    Some Python Functions
    How Can I Create My Own Function?
    Parameters
    Return Statement
    Lambda Function
    Filter() Function
    Map() Function
    What Is the Difference Between Def and Lambda?
    Variables
    Global Variables
    Local Variables
Chapter 6: Modules
    Why Should You Use Modules?
    How Do We Create a Module on Python?
    Import Statement
    How to Import a Module?
Chapter 7: OOP (Object-Oriented Programming)
    What Is a Class?
Chapter 8: File management
    How to Access a File?
        Open() Function
        Read([ ]) Method
        Readlines() Method
        Close() Function
    What Is a Buffer?
    Errors
    Encoding
    Newline
    Handling files with the "OS" module
    xlsx Files:
    Handling PDF files
    Handling of BIN Type Files
Chapter 9: Exceptions
    What Is the Possible Solution to This Problem?
Chapter 10: Other Topics
    Python and Serial Communication in Electronic Devices
    Python and the Databases
    Python and Graphical Interfaces
Conclusion