Now Technology is moving very fast towards Artificial Intelligence and Machine Learning. I have seen many students, beginners are confusing on “WHERE to START” So this is very necessary to understand the right approaches so people can learn the technology in a smart manner and use it. So this article will provide all the important topics you need to learn in AI and I also described the timings of each topic to learn.

We are assuming you belong to the science background and having a basic knowledge of Linear mathematics, Calculus, and Probability & Density Functions and Random Variables. If we talk about the programming language go straight to the Python. 

For Packages, you can use Python Anaconda Package One of the best packages for data and machine learning and a wonderful friend of python lovers. You will get all the useful needed packages and library like “numpy, matplotlib, scipy” so you don’t need to install it separately.

Now we come to the main point of the blog where you will know what you need to learn exactly to understand Artificial Intelligence and Machine Learning. I have also included the number of weeks to understand the working and functionality of AI. 

What you Need to Learn to become a master in Artificial Intelligence and Machine Learning

Lets the Journey Begins : 

Introduction to AI and its applications. (3 weeks)

Python:- Basics Data Types, Conditional Statements, Looping, Control Statements, String, List And Dictionary Manipulations, Python Functions, Modules And Packages, Object Oriented Programming in Python, Regular Expressions, Exception Handling.

Introduction to Database Management System & SQL, Database Interaction in Python.

Data Analysis & visualization – using numpy, matplotlib, scipy

R Programming:- Basics – Vectors, Factors, Lists, Matrices, Arrays, Data Frames, Reading data.

Data visualization – barplot, pie, scatterplot, histogram, scatter matrix

Statistical Analysis -Summary Statistics, Probability distributions in R- Normal distribution, Poisson distribution, Binomial distribution. Correlation and Regression

Machine Learning & Deep Learning (3 Weeks)

Linear Regression, Supervised Learning, Unsupervised Learning, Support Vector Machines(SVM), Decision Trees, Basics of Neural Network, Boosting and Optimization

Deep Learning Concepts, Deep Neural Networks, Convolutional Neural Network(CNN), Recurrent Neural Network(RNN), Tensorflow, Keras, Introduction to Generative Adversarial Networks(GAN)

Natural Language Processing (NLP) (1 Week)

Basics of text processing, Lexical processing, Syntax and Semantics, Other problems in text analytics

AI Platforms & Reinforcement Learning (2 Weeks)

Introduction to AI/Cognitive platforms and Understand the basics of Reinforcement Learning and its applications in Artificial Intelligence.

That’s it.

I hope you have understood the topics which you need to learn and be master of AI in just 9 weeks.