The Artificial Intelligence (AI) and Machine Learning (ML) Fundamentals course provides you with machine learning and neural networks skills from the ground up using real-world examples. After you complete this course, you will be excited to revamp your current projects or build new intelligent networks.

Machine learning and neural networks are pillars on which you can build intelligent applications. Artificial Intelligence and Machine Learning Fundamentals begin by introducing you to Python and discussing AI search algorithms. You will cover in-depth mathematical topics, such as regression and classification, illustrated by Python examples.

As you make your way through the course, you will progress to advanced AI techniques and concepts, and work on real-life datasets to form decision trees and clusters. You will be introduced to neural networks, a powerful tool based on Moore’s law.

By the end of this course, you will be confident when it comes to building your own AI applications with your newly acquired skills!

After completing this course, you will be able to:

• Understand the importance, principles, and fields of AI
• Implement basic Artificial Intelligence concepts with Python
• Apply regression and classification concepts to real-world problems
• Perform predictive analysis using decision trees and random forests
• Carry out clustering using the k-means and mean shift algorithms
• Understand the fundamentals of deep learning via practical examples

Lesson 1: Principles of Artificial Intelligence

• Fields and Applications of Artificial Intelligence
• AI Tools and Learning Models
• The Role of Python in Artificial Intelligence
• Python for Game AI

Lesson 2: AI with Search Techniques and Games

• Heuristics
• Pathfinding with the A* Algorithm
• Game AI with the Minmax Algorithm and Alpha-Beta Pruning

Lesson 3: Regression

• Linear Regression with One Variable
• Linear Regression with Multiple Variables
• Polynomial and Support Vector Regression

Lesson 4: Classification

• The Fundamentals of Classification
• Classification with Support Vector Machines

Lesson 5: Using Trees for Predictive Analysis

• Introduction to Decision Trees
• Random Forest Classifier

Lesson 6: Clustering

• Introduction to Clustering
• The k-means Algorithm
• Mean Shift Algorithm

Lesson 7: Deep Learning with Neural Networks

• TensorFlow for Python
• Introduction to Neural Networks
• Deep Learning

This course is for you if you Artificial Intelligence and Machine Learning Fundamentals is for software developers and data scientists who want to enrich their projects with machine learning. You do not need any prior experience in AI. However, it’s recommended that you have knowledge of high school-level mathematics and at least one programming language (preferably Python).

Hardware:

For the optimal student experience, we recommend the following hardware configuration:

• Processor: Intel Core i5 or equivalent
• Memory: 8 GB RAM
• Storage: 35 GB available space
• An internet connection

Software:

• OS: Windows 7 SP1 64-bit, Windows 8.1 64-bit or Windows 10 64-bit, Ubuntu
• Linux, or the latest version of macOS
• Browser: Google Chrome (latest version)
• Anaconda (latest version)
• IPython (latest version)