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Python Machine Learning for Beginners

Release Date: 2021/03/01
ISBN: 9781801814805
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This course lays the foundations for both a theoretical and practical understanding of machine learning and artificial intelligence, utilizing Python as a beginner-friendly introduction and invitation to further study

Key Features

  • A crash course in Python programming
  • Interactive, guided practice through a series of machine learning exercises
  • Instant access to PDFs, Python codes, and exercises from the publisher's website at no extra cost

Book Description

Machine Learning (ML) and Artificial Intelligence (AI) are here to stay. Yes, that's right. Based on a significant amount of data and evidence, it's obvious that ML and AI are here to stay.

Consider any industry today. The practical applications of ML are really driving business results. Whether it's healthcare, e-commerce, government, transportation, social media sites, financial services, manufacturing, oil and gas, marketing, and sales. You name it. The list goes on. There's no doubt that ML is going to play a decisive role in every domain in the future.

But what does a machine learning professional do?

A machine learning specialist develops intelligent algorithms that learn from data and also adapt to the data quickly. Then, these high-end algorithms make accurate predictions.

Python Machine Learning for Beginners presents you with a hands-on approach to learn ML fast. You'll learn about data analysis and visualization in great detail in the first half of the book. Then, in the second half, you'll learn about machine learning and statistical models for data science.

Each chapter presents you with the theoretical framework behind the different data science and machine learning techniques, and practical examples illustrate the working of these techniques.

By the end of this course, you will have a firm grasp on the theoretical foundations of machine learning and artificial intelligence as well as having explored and practiced various real-world applications through Python.

The code bundle for this course is available at https://www.aispublishing.net/nlp-crash-course1603576259757

What you will learn

  • Get up to speed with Python programming
  • Explore Python NumPy and Pandas libraries for data analysis
  • Practice data visualization via Matplotlib, Seaborn, and Pandas libraries
  • Solve regression problems in ML using Sklearn library
  • Solve classification problems in ML using Sklearn library
  • Study data clustering with ML using Sklearn library
  • Cover deep learning with Python TensorFlow 2.0
  • Perform dimensionality reduction with PCA and LDA using Sklearn

Who this book is for

This course is specifically designed for those students interested in studying machine learning from its theoretical foundations to advanced applications with Python. No prior experience is required.

Table of Contents

  1. Cover Page
  2. Title Page
  3. Copyright
  4. How to contact us
  5. About the Publisher
  6. AI Publishing Is Searching for Authors Like You
  7. Table of Contents
  8. Preface
  9. About the Author
  10. Get in Touch with Us
  11. Download the PDF version
  12. Chapter 1: Introduction and Environment Set Up
    1. 1.1. Difference between Data Science and Machine Learning
    2. 1.2. Steps in Learning Data Science and Machine Learning
    3. 1.3. Environment Setup
    4. 1.3.1. Windows Setup
    5. 1.3.2. Mac Setup
    6. 1.3.3. Linux Setup
    7. 1.3.4. Using Google Colab Cloud Environment
  13. Chapter 2: Python Crash Course
    1. 2.1. Writing Your First Program
    2. 2.2. Python Variables and Data Types
    3. 2.3. Python Operators
    4. 2.4. Conditional Statements
    5. 2.5. Iteration Statements
    6. 2.6. Functions
    7. 2.7. Objects and Classes
    8. 2.8. Data Science and Machine Learning Libraries
    9. 2.8.1 NumPy
    10. 2.8.2. Matplotlib
    11. 2.8.3. Seaborn
    12. 2.8.4. Pandas
    13. 2.8.5. Scikit Learn
    14. 2.8.6. TensorFlow
    15. 2.8.7. Keras
    16. Exercise 2.1
    17. Exercise 2.2
  14. Chapter 3: Python NumPy Library for Data Analysis
    1. 3.1. Advantages of NumPy Library
    2. 3.2. Creating NumPy Arrays
    3. 3.2.1 Using Array Methods
    4. 3.2.2. Using Arrange Method
    5. 3.2.3. Using Ones Method
    6. 3.2.4. Using Zeros Method
    7. 3.2.5. Using Eyes Method
    8. 3.2.6. Using Random Method
    9. 3.3. Reshaping NumPy Arrays
    10. 3.4. Array Indexing and Slicing
    11. 3.5. NumPy for Arithmetic Operations
    12. 3.5.1. Finding Square Roots
    13. 3.5.2. Finding Logs
    14. 3.5.3. Finding Exponents
    15. 3.5.4. Finding Sine and Cosine
    16. 3.6. NumPy for Linear Algebra Operations
    17. 3.6.1. Finding Matrix Dot Product
    18. 3.6.2. Element-wise Matrix Multiplication
    19. 3.6.3. Finding Matrix Inverse
    20. 3.6.4. Finding Matrix Determinant
    21. 3.6.5. Finding Matrix Trace
    22. Exercise 3.1
    23. Exercise 3.2
  15. Chapter 4: Introduction to Pandas Library for Data Analysis
    1. 4.1. Introduction
    2. 4.2. Reading Data into Pandas Dataframe
    3. 4.3. Filtering Rows
    4. 4.4. Filtering Columns
    5. 4.5. Concatenating Dataframes
    6. 4.6. Sorting Dataframes
    7. 4.7. Apply Function
    8. 4.8. Pivot & Crosstab
    9. 4.9. Arithmetic Operations with Where
    10. Exercise 4.1
    11. Exercise 4.2
  16. Chapter 5: Data Visualization via Matplotlib, Seaborn, and Pandas Libraries
    1. 5.1. What is Data Visualization?
    2. 5.2. Data Visualization via Matplotlib
    3. 5.2.1. Line Plots
    4. 5.2.2. Titles, Labels, and Legends
    5. 5.2.3. Plotting Using CSV and TSV files
    6. 5.2.4. Scatter Plots
    7. 5.2.5. Bar Plots
    8. 5.2.6. Histograms
    9. 5.2.7. Pie Charts
    10. 5.3. Data Visualization via Seaborn
    11. 5.3.1. The Dist Plot
    12. 5.3.2 The Joint Plot
    13. 5.3.3. The Pair Plot
    14. 5.3.4. The Bar Plot
    15. 5.3.5. The Count Plot
    16. 5.3.6. The Box Plot
    17. 5.3.7. The Violin Plot
    18. 5.4. Data Visualization via Pandas
    19. 5.4.1. Loading Datasets with Pandas
    20. 5.4.2. Plotting Histograms with Pandas
    21. 5.4.3. Pandas Line Plots
    22. 5.4.4. Pandas Scatter Plots
    23. 5.4.5. Pandas Bar Plots
    24. 5.4.6. Pandas Box Plots
    25. Exercise 5.1
    26. Exercise 5.2
  17. Chapter 6: Solving Regression Problems in Machine Learning Using Sklearn Library
    1. 6.1. Preparing Data for Regression Problems
    2. 6.1.1. Dividing Data into Features and Labels
    3. 6.1.2. Converting Categorical Data to Numbers
    4. 6.1.3. Divide Data into Training and Test Sets
    5. 6.1.4. Data Scaling/Normalization
    6. 6.2. Linear Regression
    7. 6.3. KNN Regression
    8. 6.4. Random Forest Regression
    9. 6.5. Support Vector Regression
    10. 6.6. K Fold Cross-Validation
    11. 6.7. Making Prediction on a Single Record
    12. Exercise 6.1
    13. Exercise 6.2
  18. Chapter 7: Solving Classification Problems in Machine Learning Using Sklearn Library
    1. 7.1. Preparing Data for Classification Problems
    2. 7.1.1. Dividing Data into Features and Labels
    3. 7.1.2. Converting Categorical Data to Numbers
    4. 7.1.3. Divide Data into Training and Test Sets
    5. 7.1.4. Data Scaling/Normalization
    6. 7.2. Logistic Regression
    7. 7.3. KNN Classifier
    8. 7.4. Random Forest Classifier
    9. 7.5. Support Vector Classification
    10. 7.6. K-Fold Cross-Validation
    11. 7.7. Predicting a Single Value
    12. Exercise 7.1
    13. Exercise 7.2
  19. Chapter 8: Data Clustering with Machine Learning Using Sklearn Library
    1. 8.1. K Means Clustering
    2. 8.1.1. Clustering Dummy Data with Sklearn
    3. 8.1.2. Clustering Iris Dataset
    4. 8.2. Hierarchical Clustering
    5. 8.2.1. Clustering Dummy Data
    6. 8.2.2. Clustering the Iris Dataset
    7. Exercise 8.1
    8. Exercise 8.2
  20. Chapter 9: Deep Learning with Python TensorFlow 2.0
    1. 9.1. Densely Connected Neural Network
    2. 9.1.1. Feed Forward
    3. 9.1.2. Backpropagation
    4. 9.1.3. Implementing a Densely Connected Neural Network
    5. Importing Required Libraries
    6. Importing the Dataset
    7. Dividing Data into Training and Test Sets
    8. Creating a Neural Network
    9. Evaluating the Neural Network Performance
    10. 9.2. Recurrent Neural Networks (RNN)
    11. 9.2.1. What Is an RNN and LSTM?
    12. What Is an RNN?
    13. Problems with RNN
    14. What Is an LSTM?
    15. 9.3. Predicting Future Stock Prices via LSTM in Keras
    16. 9.3.1. Training the Stock Prediction Model
    17. 9.3.2. Testing the Stock Prediction Model
    18. 9.4. Convolutional Neural Network
    19. 9.4.1. Image Classification with CNN
    20. 9.4.2. Implementing CNN with TensorFlow Keras
    21. Exercise 9.1
    22. Exercise 9.2
  21. Chapter 10: Dimensionality Reduction with PCA and LDA Using Sklearn
    1. 10.1. Principal Component Analysis
    2. 10.2. Linear Discriminant Analysis
    3. Exercise 10.1
    4. Exercise 10.2
  22. Exercises Solutions
    1. Exercise 2.1
    2. Exercise 2.2
    3. Exercise 3.1
    4. Exercise 3.2
    5. Exercise 4.1
    6. Exercise 4.2
    7. Exercise 5.1
    8. Exercise 5.2
    9. Exercise 6.1
    10. Exercise 6.2
    11. Exercise 7.1
    12. Exercise 7.2
    13. Exercise 8.1
    14. Exercise 8.2
    15. Exercise 9.1
    16. Exercise 9.2
    17. Exercise 10.1
    18. Exercise 10.2
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