English | MP4 | AVC 1280×720 | AAC 44KHz 2ch | 6h 43m | 1.07 GB
A complete guide to master machine learning concepts and create real-world ML solutions
If you’ve ever wanted the 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 its 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. 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. At the end of each unit, the course includes quizzes to help you evaluate your learning on the subject.
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.
What You Will Learn
- Learn core concepts of machine learning
- Learn about different types of machine learning algorithms
- Build real-world projects using supervised and unsupervised learning algorithms
- Learn to implement neural networks
Table of Contents
01 Introduction
02 What is Machine Learning
03 Types and Applications of ML
04 AI vs ML
05 Essential Math for ML and AI
06 Introduction to Supervised Learning
07 Linear Methods for Classification
08 Linear Methods for Regression
09 Support Vector Machines
10 Basis Expansions
11 Model Selection Procedures
12 Bonus! Supervised Learning Project in Python Part 1
13 Bonus! Supervised Learning Project in Python Part 2
14 Introduction to Unsupervised Learning
15 Association Rules
16 Cluster Analysis
17 Reinforcement Learning
18 Bonus! KMeans Clustering Project
19 Introduction to Neural Networks
20 The Perceptron
21 The Backpropagation Algorithm
22 Training Procedures
23 Convolutional Neural Networks
24 Introduction to Real World ML
25 Choosing an Algorithm
26 Design and Analysis of ML Experiments)
27 Common Software for ML
28 Setting up OpenAI Gym
29 Building and Training the Network Part 1
30 Building and Training the Network Part 2
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