Artificial Intelligence is no longer a science fiction movie and it's actually around us. The best examples of AI are Alpha Go, Sophia(Robot), self-driving car, Amazon Alexa, SHRDLU etc,. Though it might be still unclear exactly what is Artificial Intelligence? Artificial intelligence is a subject of Computer Science which is used for reducing the human effort and automate routine tasks. In simple words, applying the intelligence to the machines so that they can mimic like above/beyond human brains.
Read: Top 10 applications of Machine Learning
Basically, Artificial intelligence is divided into two types, Symbolic learning, and Machine learning. Symbolic learning is an approach which tries to recognize/exhibit the behavior of humans such as Robotics and computer vision, image processing etc,. Machine learning is an approach that helps the machine to learn automatically without being programmed explicitly, these are broadly divided into statistical learning and Deep learning. Below video explains the same in a better way:
Where Artificial Intelligence can be used?
Artificial Intelligence can be used in various fields such as in banking, customer support, agriculture, healthcare, Entertainment, Online advertising etc,.
Before getting started with Artificial Intelligence, check the below
Happy Learning :)
Read: Top 10 applications of Machine Learning
Basically, Artificial intelligence is divided into two types, Symbolic learning, and Machine learning. Symbolic learning is an approach which tries to recognize/exhibit the behavior of humans such as Robotics and computer vision, image processing etc,. Machine learning is an approach that helps the machine to learn automatically without being programmed explicitly, these are broadly divided into statistical learning and Deep learning. Below video explains the same in a better way:
Where Artificial Intelligence can be used?
Artificial Intelligence can be used in various fields such as in banking, customer support, agriculture, healthcare, Entertainment, Online advertising etc,.
Before getting started with Artificial Intelligence, check the below
- If you are good at math like calculus, algebra, probability and distributions etc,. then things get easier for you
- You should have knowledge of any programming language such as C++, Java, Python and R programming language.
- It would be even better if you're familiar with machine learning before jumping into Artificial Intelligence. I recommend checking Top 10 Machine Learning video tutorials first.
There are many resources to learn Artificial Intelligence but the below resources helps to master Artificial Intelligence.
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| Top 10 Best Artificial Intelligence video tutorials |
Artificial Intelligence: Reinforcement Learning in Python
This tutorial gives the best introduction to reinforcement Learning and gives you a fundamental insight into how reinforcement learning really works and why it is useful. Here, you will learn completely learn about the artificial intelligence and machine learning. The instructor walks you through implementing reinforcement learning algorithms in a very clear and structured manner that helps the students to easily understand the pros and cons of each approach. The instructor explains multi-armed bandit problem, the explore-exploit dilemma, Markov Decision Processes, Dynamic Programming, Temporal Difference (TD) Learning and much more. Here, in this tutorial, each lesson involves a coding exercise which helps to get confident and checks your knowledge of understanding. I highly recommend this video tutorials for those want to get started with Artificial Intelligence and Reinforcement learning. After completing this course, I recommend going through the Advanced AI: Deep Reinforcement Learning in Python
Artificial Intelligence A-Z™: Learn How To Build An AI
This video tutorial is a popular video tutorial on the internet. The instructor explains the code fantastically in easy to follow style and course is well structured to grasp the concepts clearly. This video tutorial is divided into three modules and in each module, you will learn a unique way of building an AI. Each module introduces has complexity and difficulty level. In the first module, you will learn to build a Breakout game. In the second module, you will learn about building more complex AI game called Doom and then in the third module, you will learn how to build a self-driving car. You will learn the fundamentals of Reinforcement Learning, Q-learning, Self-Learning Driving car. This tutorial also provides enough examples and assessments to get confident in building an Artificial Intelligence. I would highly recommend this video tutorial to anyone who wants to learn about Neural Networks and anything related to the science of Artificial Intelligence and Deep Q-Learning.
MIT 6.034 Artificial Intelligence, Fall 2010
This tutorial is presented by MIT(Massachusetts Institute of Technology). In this video tutorial, the instructor walks you through the basic knowledge representation, problem-solving, and learning methods of artificial intelligence, The video tutorial helps to understand how artificial intelligence methods work under a variety of circumstances.
Intro to Artificial Intelligence
This video tutorial is part of the Machine Learning Engineer Nanodegree Program. In this tutorial, you will learn Fundamentals and overview of AI, Statistics, Uncertainty, and Bayes networks, Machine Learning, Logic, and Planning, Markov Decision Processes and Reinforcement Learning, Hidden Markov Models and Filters, Adversarial and Advanced Planning. Also, you will also learn about the Image Processing and Computer Vision, Robotics and robot motion planning, Natural Language Processing and Information Retrieval etc,. After completing this course, I would recommend going through the Artificial Intelligence for Robotics.
This video tutorial is presented by NPTEL(National Programme on Technology Enhanced Learning) which is a joint initiative of IITs and IISc colleges(top colleges in India). In this tutorial, professor will walk you through introduction to Artificial Intelligence, problem-solving by Search, searching with costs, informed space search, Heuristic Search A* and beyond, problem reduction search AND/OR graphs, Searching game trees, knowledge-based systems logics, and deductions, logic programming: Prolog, Prolog programming, introduction to planning, GraphPlan, SATplan, Bayesian networks, decision trees, propagation learning etc,. You can also go through the Computer Sc - Artificial Intelligence and Computer Science - Artificial Intelligence
This video tutorial is presented by the Stanford University School of Engineering. In this tutorial, the instructor begins with an introduction of Natural Language Processing (NLP) and the problems NLP faces today, Word Vectors. Word window classification, Singular Value Decomposition. Skip-gram. Continuous Bag of Words (CBOW). Negative Sampling. Hierarchical Softmax. Word2Vec, coreference relation, Tree recursive neural networks, convolutional networks and much more.Stanford - Artificial Intelligence Course
Intro to AI
This video tutorial is presented by UC Berkeley and the lecture Dan Klein, Pieter Abbeel walks you through the AI. Here, the instructor covers all the AI topics such as Uninformed Search, Informed Search, Constraint Satisfaction Problems, Adversarial Search, Markov Decision Processes, Reinforcement Learning, Probability, Markov Models, Bayes' Nets:(Representation, Independence, Inference, Sampling), Machine Learning: Naive Bayes, Perceptrons, Kernels and Clustering, Advanced Applications: NLP, Games, and Robotic Cars and etc,. It is worth to go through this tutorial series.
AI: Artificial Intelligence ( Game AI ) Video Tutorials In Depth
This tutorial helps to learn Game AI which is used for building the intelligent gaming applications with AI in the virtual environment which simulates the real world where entities of the game world behave intelligently like real-world entities and enhance player experience.
AI & Deep Learning with TensorFlow
In this tutorial, the instructor starts with the introduction to deep learning and fundamentals of Neural network using TensorFlow, then you will learn about the master deep networks such as SONAR Dataset Classification, Feature Extraction, Variants of Gradient Descent etc,. after that, the instructor walks you through Convolutional Neural Networks, its applications, and architecture, then you will learn Recurrent Neural Networks and its applications and how to work with it, Then instructor covers Restricted Boltzmann Machine and its applications, working with Autoencoders. At the end of this course, the instructor explains about the TFlearn, Kera etc,.
In this tutorial, the instructor walks you through how to create functional Artificial with the abilities necessary to navigate through the game level, target the player, and to move towards them and attack. Software required: Unreal Engine 4.7.6.
From every video tutorial, you will learn something new, so I believe that the more you explore, the more you learn
Happy Learning :)
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