Projects in Machine Learning: From Beginner to Professional

Projects in Machine Learning: From Beginner to Professional

by Eduonix Learning Solutions
Released January 2021
Publisher(s): Packt Publishing
ISBN: 9781789138245

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Video description

From self-driving cars to artificial intelligence (AI) bots, machine learning (ML) is slowly spreading its reach and making our devices smarter. If you have ever wanted to play a role in the future of technology development, then here is your chance to get started with ML. This course breaks the complex topics of ML into simple concepts that are easier to understand.

The course starts with an introduction to ML, explaining its applications in the real-world and how it is different from AI. Next, you will learn supervised and unsupervised algorithms and understand the role of neural networks in ML. Once you understand the ML algorithms, you will dive into building interesting projects to consolidate your learning. You will learn how to build a board game review prediction model, how to build a credit card fraud detection model, how to tokenize word and sentences using natural language processing), how to build an object recognition model, how to build an image quality improvement model, how to build a text classification model, how to build an image analysis model, and how to build a data compression model.

By the end of this course, you will have gained the skills to create real-world ML solutions.

What You Will Learn

  • Detect credit card fraud by using probability densities
  • Become familiar with the natural language processing methodology
  • Use the Canadian Institute for Advanced Research-10 (CIFAR-10) object recognition dataset to implement a deep neural network
  • Improve image quality using Super-Resolution Convolutional Neural Network (SRCNN)
  • Solve a text classification task using multiple classification algorithms
  • Use K-means clustering in an unsupervised algorithm

Audience

If you want to understand machine learning (ML) algorithms and concepts to build effective ML solutions for the modern world, this course is for you. Basic Python skills and a good understanding of mathematics are needed to get started with this course.

About The Author

Eduonix Learning Solutions: Eduonix learning Solutions is a premier training and skill development organization which was started with a vision to bring world class training content, pedagogy and best learning practices to everyone's doorsteps . Eduonix aims to identify and provide the best learning and training environment. It identifies industry veterans and content creators around the globe and bring it to the global audience using number of intuitive platforms for easy and affordable access to quality content. Eduonix offers easy to understand online courses and workshops for everyday people. If you have ever wanted to learn a new skill, but don't want to attend four years of college to do it, we have a solution for you.

Table of contents

  1. Chapter 1 : Introduction to Machine Learning (ML)
    1. Introduction
    2. What is Machine Learning (ML)?
    3. Types and Applications of Machine Learning (ML)
    4. Artificial Intelligence (AI) versus Machine Learning (ML)
    5. Essential Mathematics for Machine Learning (ML) and Artificial Intelligence (AI)
  2. Chapter 2 : Supervised Learning – Part 1
    1. Introduction to Supervised Learning
    2. Linear Methods for Classification
    3. Linear Methods for Regression
    4. Support Vector Machines (SVM)
    5. Basic Expansions
    6. Model Selection Procedures
    7. Bonus! Supervised Learning Project in Python – Part 1
    8. Bonus! Supervised Learning Project in Python – Part 2
  3. Chapter 3 : Unsupervised Learning
    1. Introduction to Unsupervised Learning
    2. Association Rules
    3. Cluster Analysis
    4. Reinforcement Learning
    5. Bonus! K-Means Clustering Project
  4. Chapter 4 : Neural Networks
    1. Introduction to Neural Networks
    2. Perceptron
    3. Backpropagation Algorithm
    4. Training Procedures
    5. Convolutional Neural Network (CNN)
  5. Chapter 5 : Real-world Machine Learning (ML)
    1. Introduction to Real-world Machine Learning (ML)
    2. Choosing an Algorithm
    3. Design and Analysis of Machine Learning (ML) Experiments
    4. Common Software for Machine Learning (ML)
  6. Chapter 6 : Final Project
    1. Setting up OpenAI Gym
    2. Building and Training the Network – Part 1
    3. Building and Training the Network – Part 2
  7. Chapter 7 : Project 1 - Board Game Review Prediction
    1. Introduction
    2. Building the Dataset – Part 1
    3. Building the Dataset – Part 2
    4. Training Models
  8. Chapter 8 : Project 2 - Credit Card Fraud Detection t
    1. Introduction
    2. Credit Card Fraud Detection - Dataset
    3. Credit Card Fraud Detection - Algorithms
  9. Chapter 9 : Project 3 – Getting Started with Natural Language Processing (NLP) in Python
    1. Introduction
    2. Tokenizing, Stopwords, and Stemming
    3. Tagging, Chunking, and Named Entity Recognition
    4. Text Classification
  10. Chapter 10 : Project 4 – Obtaining Near State-of-the-art Performance on Object Recognition Tasks Using Deep Learning
    1. Introduction
    2. Loading and Preprocessing the Canadian Institute For Advanced Research – 10 (CIFAR-10) Dataset
    3. Building and Deploying the All-Convolutional Neural Network (CNN) Network – Part 1
    4. Building and Deploying the All- Convolutional Neural Network (CNN) Network – Part 2
  11. Chapter 11 : Project 5 – Image Super-resolution with the Super-Resolution Convolution Neural Network (SRCNN)
    1. Introduction
    2. Quality Metrics and Preprocessing Images
    3. Image Super-resolution Using Deep Learning
  12. Chapter 12 : Project 6 – Natural Language Processing (NLP): Text Classification
    1. Introduction
    2. Feature Engineering
    3. Deploying Scikit-learn (Sklearn) Classifiers
  13. Chapter 13 : Project 7 – K-means Clustering for Image Analysis
    1. Introduction
    2. Preprocessing Images for Clustering
    3. Evaluation and Visualization
  14. Chapter 14 : Project 8 – Data Compression and Visualization Using Principal Component Analysis (PCA)
    1. Introduction
    2. Elbow Method
    3. Principal Component Analysis (PCA) Compression and Visualization

Product information

  • Title: Projects in Machine Learning: From Beginner to Professional
  • Author(s): Eduonix Learning Solutions
  • Release date: January 2021
  • Publisher(s): Packt Publishing
  • ISBN: 9781789138245