English | MP4 | AVC 1280×720 | AAC 48KHz 2ch | 15.5 Hours | 4.24 GB
A complete guide to master machine learning concepts and create real world ML solutions
Update: This course has been updated to include 9 projects that will give you a real-world experience with different concepts of Machine Learning. Keep an eye out for more projects that will be added to this course in the future!
If you’ve ever wanted Jetsons to be real, well we aren’t that far off from a future like that. If you’ve ever chatted with automated robots, then you’ve definitely interacted with machine learning. From self-driving cars to AI bots, machine learning is slowly spreading it’s reach and making our devices smarter.
Artificial intelligence is the future of computers, where your devices will be able to decide what is right for you. Machine learning is the core for having a futuristic reality where robot maids and robodogs exist. Machine learning includes the algorithms that allow the computers to think and respond, as well as manipulate the data depending on the scenario that’s placed before them.
So, if you’ve ever wanted to play a role in the future of technology development, then here’s your chance to get started with Machine Learning. Because machine learning is complex and tough, we’ve designed a course to help break it down into more simple concepts that are easier to understand.
This course covers the basic concepts of machine learning that are crucial to get started on the journey of becoming a developer for machine learning. This course covers all the different algorithms that are required to simulate the right environment for your computer.
The course will start at the very beginning and delve right into machine learning, before breaking down the most important concepts principles. However, the course does require you to have a mathematical background as machine learning relies heavily on mathematical concepts. It also requires you to have some experience with Python principles which will be required when we put the algorithms to test in actual real-world Python projects.
The course covers a number of different machine learning algorithms such as supervised learning, unsupervised learning, reinforced learning and even neural networks. From there you will learn how to incorporate these algorithms into actual projects so you can see how they work in action! But, that’s not all. In addition to quizzes that you’ll find at the end of each section, the course also includes a 6 brand new projects that can help you experience the power of Machine Learning using real-world examples!
9 Projects That Are Included in This Course:
Project 1 -Board Game Review Prediction – In this project, you’ll see how to perform a linear regression analysis by predicting the average reviews on a board game in this project.
Project 2 – Credit Card Fraud Detection – In this project, you’ll learn to focus on anomaly detection by using probability densities to detect credit card fraud.
Project 3 – Stock Market Clustering – Learn how to use the K-means clustering algorithm to find related companies by finding correlations among stock market movements over a given time span.
Project 4 – Getting Started with Natural Language Processing In Python – This project will focus on Natural Language Processing (NLP) methodology, such as tokenizing words and sentences, part of speech identification and tagging, and phrase chunking.
Project 5– Obtaining Near State-of-the-Art Performance on Object Recognition Tasks Using Deep Learning – In this project, will use the CIFAR-10 object recognition dataset as a benchmark to implement a recently published deep neural network.
Project 6 – Image Super Resolution with the SRCNN – Learn how to implement and use a Tensorflow version of the Super Resolution Convolutional Neural Network (SRCNN) for improving image quality.
Project 7 – Natural Language Processing: Text Classification – In this project, you’ll learn an advanced approach to Natural Language Processing by solving a text classification task using multiple classification algorithms.
Project 8 – K-Means Clustering For Image Analysis – In this project, you’ll learn how to use K-Means clustering in an unsupervised learning method to analyze and classify 28 x 28 pixel images from the MNIST dataset.
Project 9 – Data Compression & Visualization Using Principle Component Analysis – This project will show you how to compress our Iris dataset into a 2D feature set and how to visualize it through a normal x-y plot using k-means clustering.
What Will I Learn?
- Learn core concepts of Machine Learning
- Learn about differnt types of machine learning algorithms
- Build real world projects using Supervised and Unsupervised learning algorithms
- Learn to implement neural networks
Table of Contents
An Introduction to Machine Learning
1 Introduction
2 What is Machine Learning
3 Types and Applications of ML
4 AI vs ML
5 Essential Math for ML and AI
6 Quiz- Questions- Section1
7 Quiz- Answers – Section 1
Supervised Learning – part 1
8 Introduction to Supervised Learning
9 Quiz- Answers – Section 2
10 Linear Methods for Classification
11 Linear Methods for Regression
12 Support Vector Machines
13 Basis Expansions
14 Model Selection Procedures
15 Bonus! Supervised Learning Project in Python Part 1
16 Bonus! Supervised Learning Project in Python Part 2
17 Quiz- Questions- Section 2
Unsupervised Learning
18 Introduction to Unsupervised Learning
19 Association Rules
20 Cluster Analysis
21 Reinforcement Learning
22 Bonus! KMeans Clustering Project
23 Quiz- Questions- Section 3
24 Quiz- Answers – Section 3
Neural Networks
25 Introduction to Neural Networks
26 The Perceptron
27 The Backpropagation Algorithm
28 Training Procedures
29 Convolutional Neural Networks
Real World Machine Learning
30 Introduction to Real World ML
31 Choosing an Algorithm
32 Design and Analysis of ML Experiments
33 Common Software for ML
34 Quiz- Questions- Section 5
35 Quiz- Answers – Section 5
Warmup Project
36 Setting up OpenAI Gym
37 Building and Training the Network Part 1
38 Building and Training the Network Part 2
Project 1 Board Game Review Prediction
39 Intro
40 Board Game Review Prediction – Building the Dataset Part 1
41 Board Game Review Prediction – Building the Dataset Part 2
42 Board Game Review Prediction – Training the Models
Project 2 Credit Card Fraud Detection
43 Intro
44 Credit Card Fraud Detection – The Dataset
45 Credit Card Fraud Detection – The Algorithms
Project 4 Intro to Natural Language Processing
46 Intro
47 Tokenizing, Stop Words, and Stemming
48 Tagging, Chunking, and Named Entity Recognition
49 Text Classification
Project 5 Object Recognition
50 Intro
51 Loading and Preprocessing the CIFAR10 Dataset
52 Building and Deploying the All-CNN Network Part 1
53 Building and Deploying the All-CNN Network Part 2
Project 6 Image Super Resolution
54 Intro
55 Quality Metrics and Preprocessing Images
56 Image Super Resolution using Deep Learning
Project 7 Text Classification
57 Intro
58 Feature Engineering
59 Deploying Sklearn Classifiers
Project 8 – KMeans
60 Intro
61 Preprocessing Images for Clustering
62 Evaluation and Visualization
Project 9 PCA
63 Intro
64 The Elbow Method
65 PCA Compression and Visualization
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