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30-Day Money-Back Guarantee

This course includes:

  • 8 hours on-demand video
  • Full lifetime access
  • Access on mobile and TV
Development Data Science Machine Learning

Artificial Intelligence and Machine Learning Fundamentals

Learn to develop real-world applications powered by the latest advances in intelligent systems
Rating: 4.3 out of 54.3 (49 ratings)
316 students
Created by Packt Publishing
Last updated 2/2020
English
English [Auto]
30-Day Money-Back Guarantee

What you'll learn

  • Understand the importance, principles, and fields of AI
  • Learn to 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
  • Perform clustering using the k-means and mean shift algorithms
  • Understand the fundamentals of deep learning via practical examples

Course content

7 sections • 53 lectures • 7h 47m total length

  • Preview10:40
  • Installation and Setup
    04:30
  • Lesson Overview
    03:21
  • Introduction to AI and Machine Learning
    08:13
  • How Does AI Solve Real World Problems?
    14:22
  • Fields and Applications of Artificial Intelligence
    08:30
  • AI Tools and Learning Models
    06:45
  • The Role of Python in Artificial Intelligence
    14:17
  • A Brief Introduction to the NumPy Library
    06:58
  • Python for Game AI
    11:52
  • Breadth First Search and Depth First Search
    13:58
  • Lesson Summary
    02:06
  • Test Your Knowledge
    8 questions

  • Lesson Overview
    11:04
  • Heuristics
    12:49
  • Tic-Tac-Toe
    10:04
  • Pathfinding with the A* Algorithm
    07:35
  • Introducing the A* Algorithm
    19:29
  • Game AI with the Minmax Algorithm
    09:34
  • Game AI with Alpha-Beta Pruning
    08:17
  • Lesson Summary
    01:23
  • Test Your Knowledge
    5 questions

  • Lesson Overview
    02:35
  • Linear Regression with One Variable
    13:15
  • Fitting a Model on Data with scikit-learn
    13:51
  • Linear Regression with Multiple Variables
    10:41
  • Preparing Data for Protection
    09:15
  • Polynomial and Support Vector Regression
    13:37
  • Lesson Summary
    01:31
  • Test Your Knowledge
    5 questions

  • Lesson Overview
    00:53
  • The Fundamentals of Classification Part 1
    06:36
  • The Fundamentals of Classification Part 2
    12:03
  • The k-nearest neighbor Classifier
    13:27
  • Classification with Support Vector Machines
    13:08
  • Lesson Summary
    01:35
  • Test Your Knowledge
    5 questions

  • Lesson Overview
    01:18
  • Introduction to Decision Trees
    14:03
  • Entropy
    07:30
  • Gini Impurity
    11:08
  • Precision and Recall
    15:34
  • Random Forest Classifier
    09:04
  • Random Forest Classification Using scikit-learn
    06:42
  • Lesson Summary
    01:31
  • Test Your Knowledge
    5 questions

  • Lesson Overview
    01:22
  • Introduction to Clustering
    11:28
  • The k-means Algorithm
    13:38
  • Mean Shift Algorithm
    12:58
  • Lesson Summary
    01:31
  • Test Your Knowledge
    5 questions

  • Lesson Overview
    01:14
  • TensorFlow for Python
    13:06
  • Introduction to Neural Networks
    15:39
  • Forward and Backward Propagation
    14:11
  • Training the TensorFlow Model
    09:02
  • Deep Learning
    05:35
  • Lesson Summary
    03:09
  • Test Your Knowledge
    4 questions

Requirements

  • You do not need any prior experience in AI.
  • We recommend that you have knowledge of high school level mathematics and at least one programming language, preferably Python.

Description

Machine learning and neural networks are fast becoming pillars on which you can build intelligent applications. The course will begin by introducing you to Python and discussing using AI search algorithms. You will learn math-heavy topics, such as regression and classification, illustrated by Python examples.

You will then progress on to advanced AI techniques and concepts, and work on real-life data sets to form decision trees and clusters. You will be introduced to neural networks, which is a powerful tool benefiting from Moore's law applied on 21st-century computing power. By the end of this course, you will feel confident and look forward to building your own AI applications with your newly-acquired skills!

About the Author

Zsolt Nagy is an engineering manager in an ad tech company heavy on data science. After acquiring his MSc in inference on ontologies, he used AI mainly for analyzing online poker strategies to aid professional poker players in decision making. After the poker boom ended, he put extra effort into building a T-shaped profile in leadership and software engineering.

Who this course is for:

  • This course is ideal for software developers and data scientists, who want to enrich their projects with machine learning.

Instructor

Packt Publishing
Tech Knowledge in Motion
Packt Publishing
  • 3.9 Instructor Rating
  • 56,310 Reviews
  • 344,180 Students
  • 1,422 Courses

Packt has been committed to developer learning since 2004. A lot has changed in software since then - but Packt has remained responsive to these changes, continuing to look forward at the trends and tools defining the way we work and live. And how to put them to work.

With an extensive library of content - more than 4000 books and video courses -Packt's mission is to help developers stay relevant in a rapidly changing world. From new web frameworks and programming languages, to cutting edge data analytics, and DevOps, Packt takes software professionals in every field to what's important to them now.

From skills that will help you to develop and future proof your career to immediate solutions to every day tech challenges, Packt is a go-to resource to make you a better, smarter developer.

Packt Udemy courses continue this tradition, bringing you comprehensive yet concise video courses straight from the experts.



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