If you are reading this post, definitely you are new to Artificial Intelligence world and also you have chosen the correct path to survive in the world with upcoming technology. In this post you will get  a clear understanding of how to set your learning path to become Data Scientist or Data Analyst or Data Engineer or Machine Learning Practitioner.

First and foremost obstacle for every fresher is Programming Language or coding skill. If you are the one who is afraid of programming, then do not worry about this I am going to make it simple. Believe me in this era everyone should know about a programming languages. At least any one of the programming languages. If you are new to  programming or coding my suggestion is  just to  start with Python(such a easy one).

Ok good, in this post I have listed the Road map for the same. Before you start the learning process prepare the mind to get exact understanding of each concepts. It comes only when you have deterministic goal to learn AI. In  AI every domain is interconnected each other.

1)Python

  • Get an overview about the purpose of programming language.
  • Start with print statement 
  • variables & Assignments operator
  • Control Structures 
  • Class & Function -Object Oriented Programming System

If you learn these basic topics of python then you are good go with project based learning(PBL). PBL is the instead learning randomly you can learn the project goal.

2)Overview of AI

  • Get an overview about the purpose and end goal of Artificial intelligence
  • Domains under AI
  • Machine Learning 
  • Data Science
  • Deep Learning
  • Natural Language Processing 
  • Time Series Analysis 

These are the main five domains under AI. As a beginner at least  you should learn Data Science and Machine learning to start your career as a Data Analyst/ Data Engineer/ Data scientist.

Tips: The end goal of Artificial Intelligence is for Prediction. If AI is for prediction then Sub domains is also for prediction.

3) Machine Learning

After understanding the overall idea about AI, you can start with Machine Learning. The question comes into your mind is Why have to start with Machine Learning? Because as fresher cannot able to under the code at initial stage. In ML, complexity of code is less and python libraries are time saver. I train my learner’s with Machine Learning. It worked well. Most importantly you will understand the flow of AI, so that you can easily learn advanced topics of AI.

Supervised Learning

  • Regression 
  • Classification 

Unsupervised Learning

  • Clustering 

4) Data Science

After Understanding the flow of machine learning concepts as well as hands-on using python you can start with data science concepts. Data Science is nothing but statistics. My tip to learn statics is finding out the comparison between the similar concepts and also try to understand the pattern of the statics questions.

Exploratory  Data Analysis

  • Univariate Analysis 
  • Bivariate Analysis 
  • Multivariate Analysis 
  • Deterministic Question(Customised Question based on problem  statement )

5) Deep Learning

Once you are familiar with Data science and Machine Learning concepts you can start with Deep Learning concepts. In Deep learning we create the Artificial Brain to do a task. Believe me  it’s easy. 

Supervised Learning 

  • Artificial Neural Network 
  • Convolution Neural Network
  • Recurrent Neural Network 
  • LSTM
  • GAN

The above mentioned topics are very basic concepts you should for the deep learning.

6) Natural Language Processing

Once you covered the Deep learning, then easy to learn NLP. In NLP there are preprocessing method to create a model.Learn the pure basics of NLP like how to process the text.

  • RegEx
  • Topic Modelling 
  • Sentimental Analysis 

The above mentioned topics are very basic concepts you should for the NLP.

7) Time Series Analysis

Time series can be learned even after the Data science concepts. But my wish is I learn at last because it will help to recall the machine learning concepts again.

  • Assumptions for Time Series.
  • Auto Regression 
  • Moving Average
  • Auto Regression Moving Average
  • Auto Regression Integrated Moving Average
  • Seasonal Auto Regression Integrated Moving Average
  • Vector Auto Regression

Tips: 

  • Take a resolution that you will learn the future technology.
  • Don’t forget to recall the concepts until it settles in the mindDaily put at least 2 hours daily 
  • Learn it with interest.

Don’t miss the chance to learn Future Technology.If you have any doubts , please feel free to contact me.

admin@hopelearning.net