Description
Complete AI Bootcamp. This course is a comprehensive, step-by-step bootcamp that teaches the concepts of artificial intelligence, machine learning, basic mathematics, data analysis, and reinforcement learning from basic to advanced levels. This course is designed as a structured and comprehensive path for those who want to learn the concepts of artificial intelligence and machine learning from scratch. This course guides learners from the most basic principles to applied machine learning, deep learning, and reinforcement learning algorithms. Individuals do not need to have a high level of expertise in mathematics or programming to start this path, as all concepts are taught step by step so that in addition to how to use the algorithms, the reason and logic behind their operation are fully understood. This training program first explains the differences between artificial intelligence, machine learning, and deep learning, and covers topics such as supervised and unsupervised learning, machine learning workflows, parameters and hyperparameters, model training and evaluation, and the concepts of overfitting and underfitting. It then covers the necessary mathematical foundations including probability, statistics, Bayes’ theorem, linear algebra, vectors, and matrices. Students then learn how to preprocess data, handle missing data, normalize, and engineer features using Python, Nampy, and Pandas. The course covers core algorithms such as linear regression, decision trees, random forests, support vector machines, and neural networks, providing valuable skills for specialized model analysis and entry into the world of intelligent learning.
What you will learn
- Understand the concepts and foundations of reinforcement learning including agents, environments, states, actions, rewards, and policies.
- Calculating and applying the Bellman equation to understand value functions, rewards, and the decision-making process in reinforcement learning
- A thorough understanding of Markov decision processes and how to model sequential decisions using states, transitions, and discount factors
- Identifying optimal policies from fixed plans and understanding how to update value estimates through experience in time-difference learning
This course is suitable for people who:
- Students, programmers, and enthusiasts of artificial intelligence and machine learning who want to build a strong conceptual foundation in reinforcement learning
- People who want to know how intelligent agents make decisions using Bellman equations and Markov processes
- Beginners and learners who are familiar with the basics of machine learning and want to improve their skills in sequential decision making and advanced algorithms.
Complete AI bootcamp course details
- Publisher: Udemy
- Instructor: Ranveer Singh
- Training level: Beginner to advanced
- Training duration: 7 hours and 21 minutes
Course syllabus as of 2026/9
Complete AI bootcamp course prerequisites
- No prior Reinforcement Learning experience is required. Basic Python and familiarity with fundamental AI or machine learning concepts are helpful but not mandatory.
Course images
Sample course video
Installation Guide
After Extract, view with your favorite player.
Subtitles: None
Quality: 720p
Download link
Rapidgator link
File(s) password: www.downloadly.ir
File size
2.4 GB