Description

This course covers the basic concepts of machine learning (ML) that are crucial for getting started on the journey of becoming a skilled ML developer. You will become familiar with different algorithms and networks, such as supervised, unsupervised, neural networks, Convolutional Neural Network (CNN), and Super-Resolution Convolutional Neural Network (SRCNN), needed to develop effective ML solutions.

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.
All the recourses for this course are avialable at https://github.com/PacktPublishing/Projects-in-Machine-Learning-From-Beginner-to-Professional

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.

Approach

With the help of engaging project building activities, examples, and quizzes, this course helps you to master the concepts of machine learning and gives you the confidence to build real-world solutions using supervised and unsupervised algorithms, neural networks, and a lot more.

Key Features

Grasp the core concepts of machine learning (ML) * Find out how to use neural networks in ML projects * Learn how to build real-world projects using supervised and unsupervised learning algorithms

Github Repo

https://github.com/PacktPublishing/Projects-in-Machine-Learning-From-Beginner-to-Professional

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.

Course Outline

1. Introduction to Machine Learning (ML)

1. Introduction

This video provides an introduction to this course.

2. What is Machine Learning (ML)?

This video explains the concept of ML.

3. Types and Applications of Machine Learning (ML)

This video explains the various types and applications of ML.

4. Artificial Intelligence (AI) versus Machine Learning (ML)

This video illustrates the difference between AI and ML.

5. Essential Mathematics for Machine Learning (ML) and Artificial Intelligence (AI)

This video highlights some mathematical concepts that are needed to understand ML and AI.

2. Supervised Learning - Part 1

1. Introduction to Supervised Learning

This video provides an introduction to supervised learning.

2. Linear Methods for Classification

This video explains the liner methods for classification.

3. Linear Methods for Regression

This video explains linear methods for regression.

4. Support Vector Machines (SVM)

This video explains the concept of SVM.

5. Basic Expansions

This video explains some basic expansions.

6. Model Selection Procedures

This video focuses on model selection procedures.

7. Bonus! Supervised Learning Project in Python - Part 1

This is the first part of a two-part video that presents a supervised learning project in Python.

8. Bonus! Supervised Learning Project in Python - Part 2

This is the second part of a two-part video that presents a supervised learning project in Python.

3. Unsupervised Learning

1. Introduction to Unsupervised Learning

This video provides an introduction to unsupervised learning.

2. Association Rules

This video focuses on the association rule learning method.

3. Cluster Analysis

This video explains the concept of cluster analysis.

4. Reinforcement Learning

This video explains the concept of reinforcement learning.

5. Bonus! K-Means Clustering Project

This video presents a project on K-means clustering.

4. Neural Networks

1. Introduction to Neural Networks

This video provides an introduction to neural networks.

2. Perceptron

This video explains the concept of perceptron in neural networks.

3. Backpropagation Algorithm

This video explains the backpropagation algorithm in neural networks.

4. Training Procedures

This video focuses on training procedures in neural networks.

5. Convolutional Neural Network (CNN)

This video explains the concept of CNN.

5. Real-world Machine Learning (ML)

1. Introduction to Real-world Machine Learning (ML)

This video provides an introduction to real-world ML.

2. Choosing an Algorithm

This video explains how to choose an algorithm.

3. Design and Analysis of Machine Learning (ML) Experiments

This video focuses on the design and analysis of ML experiments.

4. Common Software for Machine Learning (ML)

This video highlights some common software needed for ML.

6. Final Project

1. Setting up OpenAI Gym

This video explains how to set up OpenAI gym.

2. Building and Training the Network - Part 1

This is the first part of a two-part video that demonstrates how to build and train the network.

3. Building and Training the Network - Part 2

This is the second part of a two-part video that demonstrates how to build and train the network.

7. Project 1 - Board Game Review Prediction

1. Introduction

This video provides an introduction to the project.

2. Building the Dataset - Part 1

This is the first part of a two-part video that explains how to build the dataset for the project.

3. Building the Dataset - Part 2

This is the second part of the two-part video that explains how to build the dataset for the project.

4. Training Models

This video demonstrates how to train models.

8. Project 2 - Credit Card Fraud Detection t

1. Introduction

This video provides an introduction to the project.

2. Credit Card Fraud Detection - Dataset

This video explains how to build a dataset for the project.

3. Credit Card Fraud Detection - Algorithms

This video demonstrates how to work on algorithms for the project.

9. Project 3 - Getting Started with Natural Language Processing (NLP) in Python

1. Introduction

This video provided an introduction to the project.

2. Tokenizing, Stopwords, and Stemming

This video explains the concepts of tokenizing, stopwords, and stemming.

3. Tagging, Chunking, and Named Entity Recognition

This video explains the concepts of tagging, chunking, and named entity recognition.

4. Text Classification

This video explains the process of text classification.

10. Project 4 - Obtaining Near State-of-the-art Performance on Object Recognition Tasks Using Deep Learning

1. Introduction

This video provides an introduction to the project.

2. Loading and Preprocessing the Canadian Institute For Advanced Research - 10 (CIFAR-10) Dataset

This video demonstrates how to load and preprocess the CIFAR-10 dataset.

3. Building and Deploying the All-Convolutional Neural Network (CNN) Network - Part 1

This is the first part of a two-part video that demonstrates how to build and deploy the All-CNN network.

4. Building and Deploying the All- Convolutional Neural Network (CNN) Network - Part 2

This is the second part of a two-part video that demonstrates how to build and deploy the All-CNN network.

11. Project 5 - Image Super-resolution with the Super-Resolution Convolution Neural Network (SRCNN)

1. Introduction

This video provides an introduction to the project.

2. Quality Metrics and Preprocessing Images

This video focuses on quality metrics and preprocessing images.

3. Image Super-resolution Using Deep Learning

This video explains how to perform image super-resolution using deep learning.

12. Project 6 - Natural Language Processing (NLP): Text Classification

1. Introduction

This video provides an introduction to the project.

2. Feature Engineering

This video explains the concept of feature engineering in Natural Language Processing.

3. Deploying Scikit-learn (Sklearn) Classifiers

This video demonstrates how to deploy sklearn classifiers.

13. Project 7 - K-means Clustering for Image Analysis

1. Introduction

This video provides an introduction to the project.

2. Preprocessing Images for Clustering

This video demonstrates how to preprocess images for clustering.

3. Evaluation and Visualization

This video explains the concept of evaluation and visualization.

14. Project 8 - Data Compression and Visualization Using Principal Component Analysis (PCA)

1. Introduction

This video provides an introduction to the project.

2. Elbow Method

This video focuses on the elbow method.

3. Principal Component Analysis (PCA) Compression and Visualization

This video explains how to perform compression and visualization using PCA.

Course Images

Projects in Machine Learning: From Beginner to Professional

By Packt

Booking options

Highlights

Description

Course Content

About The Provider

Tags

Reviews