Artificial Intelligence (AI) is the study of computer science focusing on developing software or machines that exhibit human intelligence. This article is about How to start learning Artificial Intelligence in Six Easy Steps which will give you a comprehensive guide that you can use as a starting point towards learning artificial intelligence.
AI is used to solve real-world problems including search, games, machine learning, logic, understanding natural language, computer vision, expert systems, heuristic classification, constraint satisfaction problems etc.
Types of AI
We can divide AI into 3 different categories based on it’s capabilities:
1.) Strong AI (a machine with consciousness, sentience, and mind):
The idea behind Strong AI is that the machines could represent human minds in the future. If that is the case, those machines will have the ability to reason, think and do all functions that a human is capable of doing. Nanobots, which can help us fight diseases and also make us more intelligent, are being designed. The development of an artificial neural network, which can function as a proper human being, is being looked at as a future application of Strong AI.
2.) Weak AI or “narrow” AI:
The idea behind Weak AI is the fact that machines can be made to act as if they are intelligent. For example, when a human player plays any game against a computer, the human player may feel as if the computer is actually making impressive moves. But the game application is not thinking and planning at all. All the moves it makes are previously fed into the computer by a human and that is how it is ensured that the software will make the right moves at the right times.
3.) Artificial SuperIntelligence (ASI)
Artificial superintelligence is a term referring to the time when the capability of computers will surpass humans. “Artificial intelligence,” which has been much used since the 1970s, refers to the ability of computers to mimic human thought. Artificial superintelligence goes a step beyond, and posits a world in which a computer’s cognitive ability is superior to a human’s.
Where you start depends on what you already know.
Below you’ll find a list of resources to Learn and Practice and how to get started in Artificial Intelligence in 6 Easy steps:
6 Easy Steps To Get Started Learning Artificial Intelligence
STEP 1.) Learn Python & SQL
The primary thing that you have to do is take in a programming language. In spite of the fact that there are a considerable measure of languages that you can begin with, Python is what many prefer to start with because its libraries are much better suited to Machine Learning.
I would would recommend below links:
- Machine Learning with Text in scikit-learn (PyCon 2016)
- Machine learning in Python with scikit-learn
- Machine learning with Python
- Machine Learning Part 1 | SciPy 2016 Tutorial
- Machine Learning Part 2 | SciPy 2016 Tutorial
STEP 2.) Learn Machine Learning from some of the below courses.
Artificial Intelligence: Principles and Techniques from Stanford – a phenomenal educational programs for understudies inspired by adapting more about AI. The course concentrates on foundational standards of AI.
CS405: ARTIFICIAL INTELLIGENCE : CS405 introduces the field of artificial intelligence (AI). Materials on AI programming, logic, search, game playing, machine learning, natural language understanding, and robotics introduce the student to AI methods, tools, and techniques, their application to computational problems, and their contribution to understanding intelligence.
edx.org course on AI: This course gives the fundamentals of Artificial Intelligence (AI), and apply them. Design intelligent agents to solve real-world problems including, search, games, machine learning, logic, and constraint satisfaction problems.
MIT’s course on AI : This course introduces students to the basic knowledge representation, problem solving, and learning methods of artificial intelligence. Upon completion of this course, students should be able to develop intelligent systems by assembling solutions to concrete computational problems; understand the role of knowledge representation, problem solving, and learning in intelligent-system engineering; and appreciate the role of problem solving, vision, and language in understanding human intelligence from a computational perspective.
Learn the Fundamentals of AI – This course is subdivided into 10 lessons, this online course acquaints students with the universe of AI. To understand it, ensure you have some essential information of direct variable based math and likelihood hypothesis you should learn keeping in mind the end goal to be prepared.
Berkeley Video Lecturers: I would recommend the set of video lecturers here.
Also I’ve listed Top 10 Artificial Intelligence & Machine Learning Courses for Beginners and Advanced that will help you turn into the following ML master Google or Apple employs.
STEP 3.) Learn basics of probability theory, statistics, and Maths.
I would would recommend below links:
- Linear algebra–Linear Algebra– MIT 18.06 Linear Algebra by Gilbert Strang
- Probability theory-Probability and Statistics– MIT 6.041 Probabilistic Systems Analysis and Applied Probability by John Tsitsiklis
- Multivariate Calculus
- Graph theory
- Optimization methods
STEP 4.) RECOMMENDED BOOKS to READ
- Artificial Intelligence: A Modern Approach, by Stuart J. Russell and Peter Norvig
- The Quest for Artificial Intelligence, by Nils J. Nilsson
- Practical Artificial Intelligence: Programming in Java , by Mark Watson
- Simply Logical: Intelligent Reasoning by Example, by Peter Flach
- The AI Revolution: Road to Superintelligence
Also I have listed some of Top and Best free machine learning AI ebooks from where you can download and kick start Machine Learning Basics/Statistics for developers to become good at building AI systems quickly.
STEP 5.) PRACTICE FEW EXERCISES
When you have an exhaustive comprehension of your favored programming language and enough practice with the essentials, you should begin to take in more about Machine Learning. In Python, begin learning Scikit-learn, NLTK, SciPy, PyBrain, and Numpy libraries which will be valuable while composing Machine Learning algorithms.
Practice few exercises on Scikit from website:
https://www.edx.org/course/artificial-intelligence-uc-berkeleyx-cs188-1x – For practicing exercises in Python.
Also here is a rundown of assets for you to learn and hone ML:
STEP 6.) Practice—Learn—Practice on your own, step by step you will slowly become AI programmer.
I have listed free open source AI tools Or Softwares which you can use to build your solutions.
Once all these 6 steps are done then you can have a glance at these Top & Best 99 interview questions on AI and Machine Learning and start giving interviews if you want to start career in AI/ML.
PS: Want to know in-depth AI and ML latest resources around the web you MUST see this index page here.
FUTURE of AI
Many people argue that it solves many problems other’s like Elon Musk, Bill Gates say it is risky and very dangerous.
Will Robots take away our Jobs? See which Jobs Robots can take away.
Also see which Jobs Robots cannot steal or Replace from Humans.
AI is not just about Robotics the business traverses over an abundance of different enterprises, including business, auto, retail, pharmaceutical, and various others.
Just watch the below video where AI is currently going.
Well, if you want to be a part of future just go ahead with these simple steps you would not regret later.
Is your Job safe from Artificial Intelligence/Automation/Robots? Check Here.
“Software is eating the world, but AI is going to eat software.” – Jensen Huang – Nvidia CEO