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Python Machine Learning Workbook for Beginners

Release Date: 2021/03/01
ISBN: 9781801813907
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A practical guide to machine learning with Python through the presentation and guided completion of ten real-world projects

Key Features

  • Step-by-step roadmap to data science and machine learning
  • A Python crash course in machine learning
  • 10 machine learning and data science projects for practical study

Book Description

Machine Learning (ML) is the lifeblood of businesses worldwide. ML tools empower organizations to identify profitable opportunities fast and help them to better understand potential risks. The ever-expanding data, cost-effective data storage, and competitively priced powerful processing continue to drive the growth of ML.

This is the best time you could enter the exciting machine learning universe. Industries are reinventing themselves constantly by developing more advanced data analysis models. These models analyze larger and more complex data than ever while delivering instantaneous and more accurate results on enormous scales.

In this backdrop, it is evident that hands-on practice is everything in machine learning. Tons of theory will amount to nothing if you don't have enough hands-on practice. Textbooks and online classes mislead you into a false sense of mastery. The easy availability of learning resources tricks you and you become overconfident. But when you try to apply the theoretical concepts you have learned, you realize it's not that simple.

This is where projects play a crucial role in your learning journey. Projects are doubtless the best investment of your time. You'll not only enjoy learning but you'll also make quick progress. And unlike studying boring theoretical concepts, you'll find that working on projects is easier to stay motivated.

The projects in this book cover ten different interesting topics. Each project will help you refine your ML skills and apply them in the real world. These projects also present you with an opportunity to enrich your portfolio, making it simpler to find a great job, explore interesting career paths, and even negotiate a higher pay package. Overall, this learning-by-doing book will help you accomplish your machine learning career goals faster.

The code bundle for this course is available at https://www.aispublishing.net/ai-sciences-book

What you will learn

  • House price prediction using linear regression
  • Filtering spam email messages using Naive Bayes algorithm
  • Predicting used car sale price using Feedforward Artificial Neural Networks
  • Predicting stock market trends with RNN (LSTM)
  • Language translation using Seq2Seq encoder-decoder LSTM
  • Classifying cats and dogs images using Convolutional Neural Networks
  • Movie recommender system using item-based collaborative filtering
  • Face detection with OpenCV in Python
  • Handwritten English character recognition with CNN
  • Customer segmentation based on income and spending

Who this book is for

The scripts, images, and graphs are clear and provide visuals to the text description. If you are new to ML and self-study is your only option, then this book is a must.

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
    1. Book Approach
    2. Who Is This Book For?
    3. How to Use This Book?
  9. About the Author
  10. 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
  11. 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: Chapter 2.1
  12. Project 1: House Price Prediction Using Linear Regression
    1. 1.1. Importing Libraries
    2. 1.2. Importing the Dataset
    3. 1.3. Data Visualization
    4. 1.4. Divide Data into Features and Labels
    5. 1.5. Divide Data into Training and Test Sets
    6. 1.6. Training Linear Regression Algorithm
    7. 1.7. Evaluating the Performance of a Trained Model
    8. 1.8. Making Predictions on a Single Data Point
    9. Exercise 1.1
  13. Project 2: Filtering Spam Email Messages Using Naive Bayes’ Algorithm
    1. 2.1. Installing the Required Libraries
    2. 2.2. Importing the Libraries
    3. 2.3. Importing the Dataset
    4. 2.4. Data Visualization
    5. 2.5. Cleaning the Data
    6. 2.6. Convert Text to Numbers
    7. 2.7. Training the Model
    8. 2.8. Evaluating Model Performance
    9. 2.9. Making Predictions on Single Instance
    10. Exercise 2.1
  14. Project 3: Predicting Used Car Sale Price Using Feedforward Artificial Neural Networks
    1. 3.1. Installing the Required Libraries
    2. 3.2. Importing the Libraries
    3. 3.3. Importing the Dataset
    4. 3.4. Data Visualization and Preprocessing
    5. 3.5. Converting Categorical Columns to Numerical
    6. 3.6. Dividing Data into Training and Test Sets
    7. 3.7. Creating and Training Neural Network Model with Tensor Flow Keras
    8. 3.8. Evaluating the Performance of a Neural Network Model
    9. 3.9. Making Predictions on a Single Data Point
    10. Exercise 3.1
  15. Project 4: Predicting Stock Market Trends with RNN (LSTM)
    1. 4.1. Recurrent Neural Networks (RNN)
    2. 4.1.1. What Is an RNN and LSTM?
    3. 4.2. Predicting Future Stock Prices via LSTM in TensorFlow Keras
    4. 4.2.1. Training the Stock Prediction Model
    5. 4.2.2. Testing the Stock Prediction Model
    6. Exercise 4.1
  16. Project 5: Language Translation using Seq2Seq Encoder-Decoder LSTM
    1. 5.1. Creating Seq2Seq Training Model for Language Translation
    2. 5.2. Making Predictions Using Seq2Seq
    3. Exercise 5.1
  17. Project 6: Classifying Cats and Dogs Images Using Convolutional Neural Networks
    1. 6.1. How CNN Classifies Images?
    2. 6.2. Cats and Dogs Image Classification with a CNN
    3. 6.2.1. Creating Model Architecture
    4. 6.2.2. Image Augmentation
    5. 6.2.3. Dividing Data into Training & Test Sets
    6. 6.2.4. Training a CNN Model
    7. 6.2.5. Making Prediction on a Single Image
    8. Exercise 6.1
  18. Project 7: Movie Recommender System Using Item-Based Collaborative Filtering
    1. 7.1. What Is Collaborative Filtering?
    2. 7.2. Importing the Required Libraries
    3. 7.3. Importing the Dataset
    4. 7.4. Data Preprocessing
    5. 7.5. Data Visualization
    6. 7.6. Item-based Collaborative Filtering
    7. 7.6.1. Finding Recommendations Based on a Single Movie
    8. 7.6.2. Finding Recommendations Based on Multiple Movies
    9. Exercise 7.1
  19. Project 8: Face Detection with OpenCV in Python
    1. 8.1. OpenCV for Face Detection
    2. 8.2. Installing the Libraries and Importing Images
    3. 8.3. Detecting Whole Faces
    4. 8.4. Detecting Eyes
    5. 8.5. Detecting Smile
    6. 8.6. Face Detection from Live Videos
    7. Exercise 8.1
  20. Project 9: Handwritten English Character Recognition with CNN
    1. 9.1. Importing the Required Libraries
    2. 9.2. Importing the Dataset
    3. 9.3. Data Analysis and Preprocessing
    4. 9.4. Training and Fitting CNN Model
    5. 9.5. Model Evaluation
    6. 9.6. Making Predictions on a Single Image
    7. Exercise 9.1
  21. Project 10: Customer Segmentation Based on Income and Spending
    1. 10.1. K-Means Clustering
    2. 10.2. Importing the Required Libraries
    3. 10.3. Importing the Dataset
    4. 10.4. Data Analysis
    5. 10.5. K-Means Clustering
    6. 10.6. Elbow Method for Finding K Value
    7. 10.7. Finding Customers to Target for Marketing
    8. Exercise 10.1
  22. Exercise Solutions
    1. Chapter: Exercise 2.1
    2. Exercise 1.1
    3. Exercise 2.1
    4. Exercise 3.1
    5. Exercise 4.1
    6. Exercise 5.1
    7. Exercise 6.1
    8. Exercise 7.1
    9. Exercise 8.1
    10. Exercise 9.1
    11. Exercise 10.1
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