Post 1 | Machine Learning | Introduction

Hello, people. In this new tutorial series, we are going to talk about the different aspects of the Machine Learning.

As an aspiring Data Scientist, I always wanted to get my hands dirty with the concepts of Machine Learning and the Summar Break gave me exactly what I wanted – TIME TO LEARN MACHINE LEARNING CONCEPTS“.

Through these tutorials, we are going to strengthen the Machine Learning concepts and its applications in various areas.

I am learning these concepts from the paid and verified course on Udemy Website and you can view the course contents by clicking here.

The following infographics show the timeline of this Machine Learning tutorial series.

The Timeline: Machine Learning
The Timeline: Machine Learning

This tutorial is the Introduction part of the series.

The infographics show the Big Picture of the categories that we are going to see.

So let me give you the detailed list of the tutorials we are going to cover in this series.

  1. Installation – R and Python
  2. Data Preprocessing
  3. Regression
    • Simple Linear Regression
    • Multiple Linear Regression
    • Polynomial Regression
    • Support Vector Regression
    • Decision Tree Regression
    • Random Forest Regression
    • Evaluating Regression Models Performance
  4. Classification
    • Logistic Regression
    • K-Nearest Neighbors (k-NN)
    • Support Vector Machine (SVM)
    • Kernel SVM
    • Naive Bayes
    • Decision Tree Classification
    • Random Forest Classification
    • Evaluating Classification Models Performance
  5. Clustering
    • K-Means Clustering
    • Hierarchical Clustering
  6. Association Rule Learning
    • Apriori
    • Eclat
  7. Reinforced Learning
    • Upper Confidence Bound (ECB)
    • Thompson Sampling
  8. Natural Language Processing
  9. Deep Learning
    • Artificial Neural Networks
    • Convolutional Neural Networks
  10. Dimensionality Reduction
    • Principle Component Analysis (PCA)
    • Linear Discriminant Analysis (LDA)
    • Kernel PCA
  11. Model Selection and Boosting
    • Model Selection
    • XGBoost

Hope you are not overwhelmed by what is going to come in the future. In my opinion, these all are important things a Data Scientist should know and we are going to work in that direction till we finish all of the stuff mentioned above.

So, get ready and buckle up on this beautiful journey of Machine Learning.

Please follow my blog for further updates. You can check out my LinkedIn profile here. Like my Facebook page here. Follow me on Twitter here and subscribe to my YouTube channel here for the video tutorials.

In the next tutorial, we are going to do the installations of R and Python along with their respective IDEs.

Stay tuned. Cheers!

Published by milindjagre

I founded my blog four years ago and am currently working as a Data Scientist Analyst at the Ford Motor Company. I graduated from the University of Connecticut pursuing Master of Science in Business Analytics and Project Management. I am working hard and learning a lot of new things in the field of Data Science. I am a strong believer of constant and directional efforts keeping the teamwork at the highest priority. Please reach out to me at for further information. Cheers!

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