Did You Know: The knowledge provided about the Artificial Intelligence | AI is not enough for a high school student to start a career in this field.

Welcome to the Learn X page! We have compiled the list of all the steps you need to follow to become a professional in Artificial Intelligence | AI, a complete step by step guide on  ‘How to Learn AI’

Python is the basis (Python List)

Python is the basic language that you will need to learn to code various algorithms. The reason we are asking you to learn python and not any other language is that it is easy and also used in most of the machine learning algorithms all over the world. Some of the best Python books (both basic and advanced) available out there in the market are,

Understand the basics of What is Machine Learning

The next step is to learn machine learning basics. If you are not thorough with the basics, you will not be able to learn the complexities of the language. You can refer to online courses from Learn X. Most of the courses are free of cost and are very helpful. Although they will not be simple to understand for a high school student, the practice can help you become master.

It’s Time to apply

Once you have learned the basics of the Machine Learning and Algorithms, move on to Artificial Intelligence | AI. Then it is time to apply this in the daily life scenarios. The best courses from MIT OpenCourseWare and edX org (list mentioned below) let you understand ‘What is Artificial Intelligence’ & ‘Artificial Intelligence definition’ and apply it in real life without the knowledge of the university level mathematics (AI Maths).

Explore as much as possible

Once you have become aware of all the basics and have learned the language, it’s time to explore. You can visit AI Today (aitoday.xyz) AI Website to gather Artificial Intelligence Article (AI News), AI Products and AI Technology (Technological Advances). You can upgrade your knowledge from basics to mastery with the help of AI Bookshelf (Artificial Intelligence Books). Try and optimize your performance as much as possible.

Know Your Interests

The field of Artificial Intelligence is vast, and there are many categories. You need to find the area of interest that suits you the best. You can then specialize in this area for your career choices. The fields are:

  • Machine Learning
  • Neural Mimic (Neural Network in Artificial Intelligence)
  • Natural Language Processing (NLP)
  • Reinforcement Learning
  • Big Data
  • Internet of Things (Internet of Everything)
  • Robotics Engineering (How to make a Robot)
  • Bot (What is a Bot, Build a Bot)

Once you have understood your area of interest, it is important for you to understand its use in the real world. So here are a few steps you need to take as a high schooler:

  • Read as many research articles as possible from Library Z – Engines
  • Take advice from the experienced people in the field and learn from Library Z – Community Hall
  • Do not miss any update on the field.
  • Try to make your contributions to the field on your level.

Artificial Intelligence | AI is a field that has the most potential to turn into something huge in the Future of AI. Hence, if you want to excel in these, you need to follow the given steps. So, wait no more and get started.

Massachusetts Institute of Technology (MIT): Opencourseware

Graduate Courses:

Mathematics of Machine Learning (Fall 2015)
Instructor(s) Prof. Philippe Rigollet

Affective Computing (Fall 2015)
Instructor(s) Prof. Rosalind W. Picard

Prediction: Machine Learning and Statistics (Spring 2012)
Instructor(s) Prof. Cynthia Rudin

The Society of Mind (Fall 2011)
Instructor(s) Prof. Marvin Minsky

Underactuated Robotics (Spring 2009)
Instructor(s) Prof. Russell Tedrake

Adventures in Advanced Symbolic Programming (Spring 2009)
Instructor(s) Prof. Gerald Sussman

Topics in Statistics: Statistical Learning Theory (Spring 2007)
Instructor(s) Prof. Dmitry Panchenko

Machine Learning (Fall 2006)
Instructor(s) Rohit Singh (Teaching Assistant), Prof. Tommi Jaakkola, Ali Mohammad (Teaching Assistant)

Identification, Estimation, and Learning (Spring 2006)
Instructor(s) Prof. Harry Asada

Advanced Natural Language Processing (Fall 2005)
Instructor(s) Prof. Michael Collins, Prof. Regina Barzilay

Medical Decision Support (Fall 2005)
Instructor(s) Prof. Lucila Ohno-Machado, Prof. Staal Vinterbo

Knowledge-Based Applications Systems (Spring 2005)
Instructor(s) Prof. Randall Davis

Cognitive Robotics (Spring 2005)
Instructor(s) Prof. Brian Charles Williams

Medical Artificial Intelligence (Spring 2005)
Instructor(s) Prof. Lucila Ohno-Machado, Prof. Peter Szolovits

Ambient Intelligence (Spring 2005)
Instructor(s) Prof. Patricia Maes

Relational Machines (Spring 2005)
Instructor(s) Prof. Cynthia Breazeal

Pattern Recognition for Machine Vision (Fall 2004)
Instructor(s) Dr. Bernd Heisele, Dr. Yuri Ivanov

Computational Models of Discourse (Spring 2004)
Instructor(s) Prof. Regina Barzilay

Special Topics in Media Technology: Cooperative Machines (Fall 2003)
Instructor(s) Prof. Cynthia Breazeal

Natural Language and the Computer Representation of Knowledge (Spring 2003)
Instructor(s) Prof. Robert Berwick

Statistical Learning Theory and Applications (Spring 2003)
Instructor(s) Dr. Ryan Rifkin, Dr. Sayan Mukherjee, Prof. Tomaso Poggio, Alex Rakhlin

Medical Decision Support (Spring 2003)
Instructor(s) Prof. Isaac Kohane, Prof. Lucila Ohno-Machado, Prof. Peter Szolovits, Prof. Staal Vinterbo

Techniques in Artificial Intelligence (SMA 5504) (Fall 2002)
Instructor(s) Prof. Tomás Lozano-Pérez, Prof. Leslie Kaelbling

Common Sense Reasoning for Interactive Applications (Fall 2002)
Instructor(s) Prof. Henry Lieberman

Out of Context: A Course on Computer Systems That Adapt To, and Learn From, Context (Fall 2001)
Instructor(s) Prof. Henry Lieberman

Undergraduate Courses:

Introduction to Computer Science and Programming in Python (Fall 2016)
Instructor(s) Dr. Ana Bell, Prof. Eric Grimson, Prof. John Guttag

The Battlecode Programming Competition (January IAP 2013)
Instructor(s) Maxwell Mann

Minds and Machines (Fall 2011)
Instructor(s) Prof. Alex Byrne

Introduction to Electrical Engineering and Computer Science I (Spring 2011)
Instructor(s) Prof. Leslie Kaelbling, Prof. Jacob White, Prof. Harold Abelson, Prof. Dennis Freeman, Prof. Tomás Lozano-Pérez, Prof. Isaac Chuang

A Gentle Introduction to Programming Using Python (January IAP 2011)
Instructor(s) Sarina Canelake

Artificial Intelligence (Fall 2010)
Instructor(s) Prof. Patrick Henry Winston

Principles of Autonomy and Decision Making (Fall 2010)
Instructor(s) Prof. Brian Charles Williams, Prof. Emilio Frazzoli

A Gentle Introduction to Programming Using Python (January IAP 2008)
Instructor(s) Mihir Kedia, Aseem Kishore

The Human Intelligence Enterprise (Spring 2006)
Instructor(s) Prof. Patrick Henry Winston

Artificial Intelligence (Spring 2005)
Instructor(s) Prof. Leslie Kaelbling, Prof. Tomás Lozano-Pérez

Mobile Autonomous Systems Laboratory (January IAP 2005)
Instructor(s) This course is run by students, so there is no faculty member associated with this class.

Autonomous Robot Design Competition (January IAP 2005)
Instructor(s) This course is run by students, so there is no faculty member associated with this class.

Robocraft Programming Competition (January IAP 2005)
Instructor(s) Prof. Michael Ernst

Machine Vision (Fall 2004)
Instructor(s) Prof. Berthold Horn

The Human Intelligence Enterprise (Spring 2002)
Instructor(s) Prof. Patrick Henry Winston

edX org | Free online courses (webcourses) from top universities

Artificial Intelligence:

Columbia University – Artificial Intelligence
Instructor(s) Professor Ansaf Salleb-Aouissi

Microsoft -Introduction to Artificial Intelligence (AI)
Instructor(s) Graeme Malcolm

Microsoft – Essential Mathematics for Artificial Intelligence
Instructor(s) Graeme Malcolm

Python:

Harvey Mudd College – CS For All: Introduction to Computer Science and Python Programming
Instructor(s) Zachary Dodds

Georgia Institute of Technology – Introduction to Computing using Python
Instructor(s) David Joyner

Microsoft – Introduction to Python: Creating Scalable, Robust, Interactive Code
Instructor(s) Paige Bailey, Anas Salah Eddin, Eric Camplin

Microsoft – Programming with Python for Data Science
Instructor(s) Authman Apatira, Jonathan Sanito

Microsoft – Introduction to Python: Absolute Beginner
Instructor(s) Eric Camplin

Microsoft – Introduction to Python for Data Science
Instructor(s) Filip Schouwenaars, Jonathan Sanito

University of Texas at Arlington – Introduction to Programming Using Python
Instructor(s) Farhad Kamangar

UC San Diego – Python for Data Science
Instructor(s) Ilkay Altintas, Leo Porter

Microsoft – Introduction to Python: Fundamentals
Instructor(s) Eric Camplin

Harvard University – Using Python for Research
Instructor(s) Jukka-Pekka “JP” Onnela

Columbia University – Analytics in Python
Instructor(s) Hardeep Johar

Machine Learning:

Columbia University – Machine Learning
Instructor(s) Professor John W. Paisley

Columbia University – Machine Learning for Data Science and Analytics
Instructor(s) Professor Ansaf Salleb-Aouissi, Cliff Stein, David Blei, Itsik Peer, Mihalis Yannakakis, Peter Orbanz

The Georgia Institute of Technology – Machine Learning
Instructor(s) Charles Isbell

UC San Diego – Machine Learning Fundamentals
Instructor(s) Sanjoy Dasgupta

Microsoft – Principles of Machine Learning
Instructor(s) Graeme Malcolm, Steve Elston, Cynthia Rudin

Microsoft – Deep Learning Explained
Instructor(s) Jonathan Sanito, Sayan Pathak, Roland Fernandez

Neuron:

École polytechnique fédérale de Lausanne (EPFL) – Neuronal Dynamics
Instructor(s) Wulfram Gerstner

École polytechnique fédérale de Lausanne (EPFL) – Simulation Neuroscience
Instructor(s) Henry Markram, Idan Segev, Sean Hill, Felix Schürmann, Eilif Muller, Srikanth Ramaswamy, Werner Van Geit, Samuel Kerrien, Lida Kanari

Natural Language Processing:

Microsoft – Natural Language Processing (NLP)
Instructor(s) Xiaodong He, Roland Fernandez, Lei Ma

Reinforcement Learning:

Microsoft – Reinforcement Learning Explained
Instructor(s) Roland Fernandez, Adith Swaminathan, Kenneth Tran, Katja Hofmann, Matthew Hausknecht, Jonathan Sanito

Big Data:

Tsinghua University- 数据挖掘:理论与算法 | Data Mining: Theories and Algorithms for Tackling Big Data
Instructor(s) Bo Yuan

University of Adelaide – Programming for Data Science
Instructor(s) Dr. Katrina Falkner, Dr. Nick Falkner, Dr. Claudia Szabo

University of Adelaide – Computational Thinking and Big Data
Instructor(s) Dr. Markus Wagner, Dr. Lewis Mitchell, Dr. Simon Tuke

University of Adelaide – Big Data Fundamentals
Instructor(s) Dr. Frank Neumann, Vahid Roostapour, Aneta Neumann, Dr Wanru (Kelly) Gao

University of Adelaide – Big Data Analytics
Instructor(s) Dr. Lewis Mitchell, Dr. Simon Tuke, David Suter

University of Adelaide – Big Data Capstone Project
Instructor(s) Dr. Nick Falkner, Gary Glonek, Dr. Lingqiao Liu, Gavin Meredith, Ian Knight

UC San Diego – Big Data Analytics Using Spark
Instructor(s) Yoav Freund

University of California, Berkeley – Big Data Analysis with Apache Spark
Instructor(s) Anthony D. Joseph

Microsoft – Developing Big Data Solutions with Azure Machine Learning
Instructor(s) Graeme Malcolm, Steve Elston

Georgia Institute of Technology – Big Data Analytics in Healthcare
Instructor(s) Jimeng Sun

Internet of Things:

Curtin University – Introduction to the Internet of Things (IoT)
Instructor(s) Iain Murray AM, Nazanin Mohammadi

Curtin University – IoT Sensors and Devices
Instructor(s) Iain Murray AM, Nazanin Mohammadi, We-Juet (Bert) Wong

Curtin University – IoT Networks and Protocols
Instructor(s) Iain Murray AM, Nazanin Mohammadi

Curtin University – IoT Programming and Big Data
Instructor(s) Johannes U. Herrmann, Aloke Phatak, Valerie Maxville

Curtin University – Cybersecurity and Privacy in the IoT
Instructor(s) Iain Murray AM, Nazanin Mohammadi, Eleanor Sandry

Curtin University – IoT Capstone Project
Instructor(s) Iain Murray AM

Robotics:

Massachusetts Institute of Technology (MIT) – Underactuated Robotics
Instructor(s) Russ Tedrake, Robin Deits, Twan Koolen

Columbia University – Robotics
Instructor(s) Professor Matei Ciocarlie

University of Pennsylvania – Robotics: Fundamentals
Instructor(s) Camillo J. Taylor, Mark Yim

University of Pennsylvania – Robotics: Vision Intelligence and Machine Learning
Instructor(s) Jianbo Shi, Kostas Daniilidis, Dan Lee

University of Pennsylvania – Robotics: Dynamics and Control
Instructor(s) Vijay Kumar, Ani Hsieh

University of Pennsylvania – Robotics: Locomotion Engineering
Instructor(s) Dan Koditschek

ETH Zurich – Autonomous Mobile Robots
Instructor(s) Roland Siegwart, Marco Hutter, Margarita Chli, Davide Scaramuzza, Martin Rufli

Bots:

Microsoft – Developing Intelligent Apps and Bots
Instructor(s) Gerry O’Brien, Amy Nicholson

ai.google Education:

Google’s AI Education is free of charge course targeted to bring machine learning skills and related ideas to all or any users’ doorstep without much trouble or inconvenience and fizzle. Although there are a few prerequisites to be looked at before taking this program that you need to have a simple developer knowledge, writing rules and introductory degree of algebra knowledge.

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