How to learn AI: Full Guide

 AI Learning Roadmap.

Table Of Contents:

• Step1: Understand Aritficial Intelligence Basics
• Step2: Learn Math
• Step3: Learn Programming Language
• Step4: Learn Big Data
• Step5: Learn Data Science 
• Step6:  Learn Machine Learning Algorithms
• Step7: Learn Deep Learning Algorithms
• Step8: Learn Business Intelligence
• Step9: Work on Projects
• Conclusion

AI has gained widespread popularity in recent times, with its presence being felt across various industries, from self-driving cars and robotics to product recommendations and virtual assistants like Google Assistant, Siri, and Alexa,you can check this article about what is AI. If you want to become an AI engineer, it is essential to possess a certain set of skills, including proficiency in mathematics, programming, big data, data science, machine learning, deep learning, natural language processing, and business intelligence. In this roadmap, we will delve into the steps involved in learning artificial intelligence.

Step1. Understand Artificial Intelligence Basics.

Before learning other essential skills, first, learn the basics of Artificial Intelligence.

 At this step, you have to understand what is Artificial Intelligence, its impact, future trends of Artificial Intelligence, and its applications in various fields.

You can learn these things from any Youtube tutorial or from any FREE course.

I am also going to list some resources to learn the fundamentals of Artificial Intelligence.

• Intro to Artificial Intelligence– Udacity FREE Course

• AI For Everyone– Coursera FREE to Audit Course

• AI Foundations for Everyone Specialization– Coursera

• AI Programming with Python– Udacity

• AI Fundamentals– Udacity FREE Course

• Introduction to Artificial Intelligence with Python-edX

• Artificial Intelligence Full Course– YouTube

• Artificial Intelligence For Beginners– YouTube

Step 2. Learn Math

Your next move should be to learn Math. In the following steps, you will be learning Machine Learning and Deep Learning algorithms, and a solid foundation in math will help you grasp how these algorithms function.

Topics to cover in math include:

• Statistics
• Probability
• Linear Algebra
• Calculus

There are numerous resources available for learning math concepts. I will also provide a list of some recommended resources.

• Intro to Statistics– Udacity FREE Course

• Linear Algebra Refresher Course– Udacity FREE Course

• Basic Statistics (Online Course)

• Statistics and probability (Khan Academy)

• Practical Statistics for Data Scientists (TextBook)

• Data Science: Statistics and Machine Learning Specialization (Online Course)

• Statistics for Data Science (YouTube Video)

• Mathematics for Data Science Specialization  (Online Course)

• Khan Academy

• Data Science Math Skills (Online Course)

Step 3. Learn Programming Language

Once you have a strong foundation in math, your next step should be to learn a programming language.

Familiarity with a programming language is crucial in the field of Artificial Intelligence, as it is the foundation for implementing AI projects.

Some common programming languages used in AI are Python, R, and Java.

Personally, I recommend Python for beginners as it is user-friendly and has a wealth of libraries and packages for Machine Learning and Deep Learning.

• Introduction to Python Programming(Udacity Free Course)

•  Python Tutorial (PYTHON.ORG)

• CS DOJO (YouTube)

• Python 3 Tutorial (SOLOLEARN)

• Python For Data Science(Udemy Free Course)

• Programming with Mosh (YouTube)

• Corey Schafer (YouTube)

Step 4. Learn Big Data

Why is Big Data important?

Because AI models rely on data to be trained, and large datasets require specialized tools for handling and management.

Companies like YouTube and Google use recommendation systems that utilize both AI and Big Data.

Therefore, it is essential to have knowledge of Big Data tools such as Hadoop, Spark, Cassandra, and MongoDB. You can choose to focus on learning just one of these tools.

• Intro to Hadoop and MapReduce(Udacity FREE Course)

• Spark (Udacity FREE Course)

• Hadoop Developer In Real World (Udemy)

• Big Data Specialization (Coursera)

Step 5. Learn Data Science.

In this step, you should focus on learning how to acquire, prepare, analyze, and manipulate data.

There are numerous courses available to help you learn Data Science. I will provide a list of some recommended courses.

• IBM Data Science Professional Certificate– Coursera

• Programming for Data Science with Python– Udacity

• Data Science for Everyone– Datacamp

• The Data Science Course 2023: Complete Data Science Bootcamp– Udemy

• Data Science Tutorial–w3schools

• Career Path Data Science– CodecademyPython – Data Science Tutorial– TutorialsPoint

Step 6. Learn Machine Learning Algorithms

Another crucial skill to acquire is an understanding of machine learning algorithms.

You can begin learning the basics of machine learning through the free course "Machine Learning by Andrew Ng."

Some specific types of machine learning algorithms to focus on include:

• Supervised Learning
• Unsupervised Learning
• Reinforcement Learning

• Machine Learning by Georgia Tech(Udacity Free Course)

• Introduction to Machine Learning Course(Udacity Free Course)

• Machine Learning: Unsupervised Learning (Udacity Free Course)

• Machine Learning by Stanford University(Coursera Free to Audit Course)

• Machine Learning for All by University of London(Coursera Free to Audit Course)

• What is Machine Learning? (Udemy Free Course)

• Machine Learning Fundamentals(edX Free to Audit Course)

Step 7. Learn Deep Learning Algorithms

After you have a solid understanding of machine learning algorithms, your next step should be to learn deep learning algorithms and their associated frameworks, such as Tensorflow or Keras.

Some specific topics to cover in deep learning algorithms include:

• Neural Network
• CNN (Convolutional Neural Network)
• RNN (Recurrent Neural Network)
• GAN (Generative Adversarial Network)
• LSTM (Long Short-Term Memory)

• Deep Learning Specialization (deeplearning.ai)

• Deep Learning– Udacity

• Intro to Deep Learning with PyTorch– Udacity FREE Course

• Intro to TensorFlow for Deep Learning– Udacity FREE Course

• Intro to Deep Learning– Kaggle

• Generative Adversarial Networks (GANs) Specialization– Coursera

• Become a Deep Reinforcement Learning Expert– Udacity

• Deep Learning: Convolutional Neural Networks in Python– Udemy

• Reinforcement Learning– Udacity

• Neural Networks and Deep Learning– Coursera

Step 8. Learn Business Intelligence

Business Intelligence enables companies to make informed decisions based on data. At this stage, it is helpful to learn tools such as Tableau, PowerBI, or Qlikview.

These tools allow you to create visualizations of your findings, making it easier to communicate your results to stakeholders.

• Data Visualization in Tableau– Udacity FREE Course

• Fundamentals of Visualization with Tableau– Coursera FREE to Audit Course

• Introduction to Power BI– DataCamp

• Microsoft Power BI Desktop for Business Intelligence– Udemy

• Data Visualization and Communication with Tableau– Coursera

• Free Training Videos– Tableau

• Creating Dashboards and Storytelling with Tableau– Coursera

Step 9. Work on Projects

Congratulations on acquiring a strong foundation in Artificial Intelligence skills! It's now time to apply your knowledge to real-world projects. 

Practical experience is key to landing a job as an AI Engineer and the more projects you work on, the deeper your understanding of AI will become.In addition, completing projects will add value to your resume. You can use platforms like Kaggle to find AI-related projects and competitions.

Conclusion

In this article, I provided a comprehensive roadmap for learning Artificial Intelligence in 2023. If you have any questions or concerns, please don't hesitate to leave a comment. 

I am happy to assist you.Best of luck in your career and happy learning!