Artificial Intelligence and Machine Learning: Complete Guide

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.