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Artificial Intelligence and Machine Learning: Complete Guide

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Artificial Intelligence and Machine Learning: Complete Guide, available at $19.99, has an average rating of 4.26, with 189 lectures, based on 122 reviews, and has 1953 subscribers.

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You will learn about The theoretical and practical basis of the main Artificial Intelligence algorithms Implement Artificial Intelligence algorithms from scratch and using pre-defined libraries Learn the intuition and practice about machine learning algorithms for classification, regression, association rules, and clustering Learn Machine Learning without knowing a single line of code Use Orange visual tool to create, analyze and test algorithms Use Python programming language to create Artificial Intelligence algorithms Learn the basics of programming in Python Use greedy search and A* (A Star) algorithms to find the shortest path between cities Implement optimization algorithms for minimization and maximization problems Implement an AI to predict the amount of tip to be given in a restaurant, using fuzzy logic Use data exploration techniques applied to a COVID-19 disease database Create a reinforcement learning agent to simulate a taxi that needs to learn how to pick up and drop off passengers Implement artificial neural networks and convolutional neural networks to classify images of the characters Homer and Bart, from the Simpsons cartoon Learn natural language processing techniques and create a sentiment classifier Detect and recognize faces using computer vision techniques Track objects in video using computer vision Generate new images that do not exist in the real world using Artificial Intelligence This course is ideal for individuals who are People interested in starting their studies in Artificial Intelligence, Machine Learning, Data Science or Deep Learning or People who want to study Artificial Intelligence, however, don't know where to start or Undergraduate students studying subjects related to Artificial Intelligence or Anyone interested in Artificial Intelligence or Entrepreneurs who want to apply machine learning to commercial projects or Entrepreneurs who want to create efficient solutions to real problems in their companies It is particularly useful for People interested in starting their studies in Artificial Intelligence, Machine Learning, Data Science or Deep Learning or People who want to study Artificial Intelligence, however, don't know where to start or Undergraduate students studying subjects related to Artificial Intelligence or Anyone interested in Artificial Intelligence or Entrepreneurs who want to apply machine learning to commercial projects or Entrepreneurs who want to create efficient solutions to real problems in their companies.

Enroll now: Artificial Intelligence and Machine Learning: Complete Guide

Summary

Title: Artificial Intelligence and Machine Learning: Complete Guide

Price: $19.99

Average Rating: 4.26

Number of Lectures: 189

Number of Published Lectures: 189

Number of Curriculum Items: 189

Number of Published Curriculum Objects: 189

Original Price: $22.99

Quality Status: approved

Status: Live

What You Will Learn

  • The theoretical and practical basis of the main Artificial Intelligence algorithms
  • Implement Artificial Intelligence algorithms from scratch and using pre-defined libraries
  • Learn the intuition and practice about machine learning algorithms for classification, regression, association rules, and clustering
  • Learn Machine Learning without knowing a single line of code
  • Use Orange visual tool to create, analyze and test algorithms
  • Use Python programming language to create Artificial Intelligence algorithms
  • Learn the basics of programming in Python
  • Use greedy search and A* (A Star) algorithms to find the shortest path between cities
  • Implement optimization algorithms for minimization and maximization problems
  • Implement an AI to predict the amount of tip to be given in a restaurant, using fuzzy logic
  • Use data exploration techniques applied to a COVID-19 disease database
  • Create a reinforcement learning agent to simulate a taxi that needs to learn how to pick up and drop off passengers
  • Implement artificial neural networks and convolutional neural networks to classify images of the characters Homer and Bart, from the Simpsons cartoon
  • Learn natural language processing techniques and create a sentiment classifier
  • Detect and recognize faces using computer vision techniques
  • Track objects in video using computer vision
  • Generate new images that do not exist in the real world using Artificial Intelligence

Who Should Attend

  • People interested in starting their studies in Artificial Intelligence, Machine Learning, Data Science or Deep Learning
  • People who want to study Artificial Intelligence, however, don't know where to start
  • Undergraduate students studying subjects related to Artificial Intelligence
  • Anyone interested in Artificial Intelligence
  • Entrepreneurs who want to apply machine learning to commercial projects
  • Entrepreneurs who want to create efficient solutions to real problems in their companies

Target Audiences

  • People interested in starting their studies in Artificial Intelligence, Machine Learning, Data Science or Deep Learning
  • People who want to study Artificial Intelligence, however, don't know where to start
  • Undergraduate students studying subjects related to Artificial Intelligence
  • Anyone interested in Artificial Intelligence
  • Entrepreneurs who want to apply machine learning to commercial projects
  • Entrepreneurs who want to create efficient solutions to real problems in their companies

The fields of Artificial Intelligence and Machine Learning are considered the most relevant areas in Information Technology. They are responsible for using intelligent algorithms to build software and hardware that simulate human capabilities. The job market for Machine Learning is on the rise in various parts of the world, and the trend is for professionals in this field to be in even higher demand. In fact, some studies suggest that knowledge in this area will soon become a prerequisite for IT professionals.

To guide you into this field, this course provides both theoretical and practical insights into the latest Artificial Intelligence techniques. This course is considered comprehensive because it covers everything from the basics to the most advanced techniques. By the end, you will have all the necessary tools to develop Artificial Intelligence solutions applicable to everyday business problems. The content is divided into seven parts: search algorithms, optimization algorithms, fuzzy logic, machine learning, neural networks and deep learning, natural language processing, and computer vision. You will learn the basic intuition of each of these topics and implement practical examples step by step. Below are some of the projects/topics that will be covered:

  • Finding optimal routes on city maps using greedy search and A* (star) search algorithms

  • Selection of the cheapest airline tickets and profit maximization using the following algorithms: hill climb, simulated annealing, and genetic algorithms

  • Prediction of the tip you would give to a restaurant using fuzzy logic

  • Classification using algorithms such as Naïve Bayes, decision trees, rules, k-NN, logistic regression, and neural networks

  • Prediction of house prices using linear regression

  • Clustering bank data using k-means algorithm

  • Generation of association rules with Apriori algorithm

  • Data preprocessing, dimensionality reduction, and outlier detection in databases

  • Prediction of stock prices using time series analysis

  • Data visualization and exploration in the context of the COVID-19 disease database

  • Building of a reinforcement learning agent to control a taxi for passenger transportation

  • Classification of cat and dog images using convolutional neural networks

  • Classification of Homer and Bart images from The Simpsons cartoon using convolutional neural networks

  • POS tagging, lemmatization, stemming, word cloud, and named entity recognition using natural language processing techniques

  • Implementation of a sentiment classifier in the context of a Twitter dataset

  • Face detection and recognition in images

  • Object tracking in videos

  • Generation of images that do not exist in the real world using advanced Computer Vision techniques

Each type of problem requires different techniques for its solution, so by covering all AI areas, you’ll know which techniques to use in various scenarios! Throughout the course, we will use the Python programming language and the graphical tool Orange. If you are not familiar with Python, you will have access to over 5 hours of video exercises covering the basics of this programming language. This course is suitable for your first exposure to Artificial Intelligence, as it covers all the necessary topics in theory and practice. If you are more advanced in this field, you can use this course as a reference to learn new areas and review concepts.

Course Curriculum

Chapter 1: Introduction

Lecture 1: Course content

Lecture 2: Terminology

Lecture 3: Course materials

Chapter 2: Part 1 – Search algorithms

Lecture 1: Introduction

Lecture 2: Search – intuition

Lecture 3: Heuristics – intuition

Lecture 4: Ordered arrays – intuition

Lecture 5: Ordered arrays – implementation

Lecture 6: Creating the city map

Lecture 7: Greedy search – intuition

Lecture 8: Greedy search – implementation

Lecture 9: A* search – intuition

Lecture 10: A* search – implementation

Lecture 11: HOMEWORK

Lecture 12: Homework solution

Chapter 3: Part 2 – Optimization algorithms

Lecture 1: Optimization algorithms – intuition

Lecture 2: Case study – flight schedule

Lecture 3: Representing the problem

Lecture 4: Printing the solution

Lecture 5: Fitness function

Lecture 6: Hill climb – intuition

Lecture 7: Hill climb – implementation

Lecture 8: Simulated annealing – intuition

Lecture 9: Simulated annealing – implementation

Lecture 10: Genetic algorithm – intuition

Lecture 11: Genetic algorithm – implementation

Lecture 12: HOMEWORK

Lecture 13: Homework solution

Chapter 4: Part 3 – Fuzzy logic

Lecture 1: Introduction

Lecture 2: Applications of fuzzy logic

Lecture 3: Fuzzy logic – intuition

Lecture 4: Implementation 1

Lecture 5: Implementation 2

Lecture 6: Implementation 3

Lecture 7: HOMEWORK

Lecture 8: Homework solution

Chapter 5: Part 4 – Machine learning

Lecture 1: Introduction

Lecture 2: Machine learning and Data Science

Chapter 6: Classification

Lecture 1: What is classification?

Lecture 2: Naïve Bayes – intuition

Lecture 3: Naïve Bayes in Orange

Lecture 4: Decision trees – intuition

Lecture 5: Decision trees in Orange

Lecture 6: Rule based learning – intuition

Lecture 7: Rule based learning in Orange

Lecture 8: kNN (k nearest neighbors) – intuition

Lecture 9: kNN (k nearest neighbors) in Orange

Lecture 10: SVM (Support Vectors Machines) – intuition

Lecture 11: SVM (Support Vectors Machines) in Orange

Lecture 12: Logistic regression – intuition

Lecture 13: Logistic regression in Orange

Lecture 14: Crossvalidation

Lecture 15: HOMEWORK

Lecture 16: Homework solution

Lecture 17: Image classification in Orange

Chapter 7: Regression

Lecture 1: What is regression?

Lecture 2: Linear regression – intuition

Lecture 3: Linear regression in Orange

Lecture 4: HOMEWORK

Lecture 5: Homework solution

Chapter 8: Clustering

Lecture 1: What is clustering?

Lecture 2: K-means algorithm – intuition

Lecture 3: K-means algorithm in Orange

Lecture 4: HOMEWORK

Lecture 5: Homework solution

Lecture 6: Clustering images in Orange

Chapter 9: Association rules

Lecture 1: What are association rules?

Lecture 2: Apriori algorithm

Lecture 3: Apriori in Orange

Lecture 4: HOMEWORK

Lecture 5: Homework solution

Chapter 10: Additional topics

Lecture 1: Missing values and normalization

Lecture 2: Discretization

Lecture 3: Feature selection

Lecture 4: Dimensionality reduction using PCA

Lecture 5: PCA and clustering

Lecture 6: Outliers detection

Lecture 7: Time series 1

Lecture 8: Time series 2

Lecture 9: Basic charts

Lecture 10: COVID dataset 1

Lecture 11: COVID dataset 2

Lecture 12: COVID dataset 3

Chapter 11: Reinforcement learning

Lecture 1: Introduction

Lecture 2: Intuition

Lecture 3: Implementation 1 – environment

Lecture 4: Implementation 2 – training 1

Lecture 5: Implementation 3 – training 2

Lecture 6: Implementation 4 – evaluation

Instructors

Rating Distribution

  • 1 stars: 2 votes
  • 2 stars: 5 votes
  • 3 stars: 10 votes
  • 4 stars: 28 votes
  • 5 stars: 77 votes

Frequently Asked Questions


How long do I have access to the course materials?


You can view and review the lecture materials indefinitely, like an on-demand channel.


Can I take my courses with me wherever I go?


Definitely! If you have an internet connection, courses on Udemy are available on any device at any time. If you don’t have an internet connection, some instructors also let their students download course lectures. That’s up to the instructor though, so make sure you get on their good side!

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