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Introduction

Hi, my name is Harish Gehlot and I am Data Science and Machine Learning Enthusiast. We are seeing that 100 days of machine learning is getting popular. But this is a serious challenge of discipline 📜 and getting too much knowledge 👨‍🔬. So here I've designed a 100 days structure in which what I am doing everyday is discussed. So if you are also interested 🎃, Let's start this journey together.

Structure 🪖

• Day 1

  • In Day 1, we'll learn about Basic concepts of Machine Learning.
  • What is Machine learning ?.
  • Supervised, Unsupervised and Reinforcement Learning .
  • Basic Terminologies used in Machine Learning .

• Day 2

  • Some basic Machine Learning Interview questions .
  • Difference between Supervised and Unsupervised Machine learning algorithm ?
  • Difference between Data Engineer, Data Scienctist, Data Analyst and Machine Learning Engineer ?
  • What is Online and Offline learning ?
  • How Machine Learning is different from Deep Learning ?

• Day 3

  • Machine Learning Lifecycle and their different aspects .

• Day 4

  • Analyze the Data and extract some insights i.e. Exploratory Data Analysis .
  • Use some dataset to visualize and extract information using python library .

• Day 5

  • Learn different methods to impute missing values (Numerical and categorical features).

• Day 6

  • Learn different methods to encode the categorical features .

• Day 7

  • Learn different methods to remove outliers.

• Day 8

  • Learn different methods for feature selection.

• Day 9

  • Generate a sequential pipeline to transform the dataset .

• Day 10 to 15

  • Learn and Implement Linear Regression Algorithm .
  • Machine learning interview question regarding this algorithm .

• Day 16 to 20

  • Logistic Regression
  • Machine learning interview question regarding this algorithm .

• Day 21 to 25

  • Support Vector Machine

• Day 26 to 34

  • Decision Tree Algorithm
  • Random Forest Algorithm
  • Machine learning interview question regarding this algorithms .

• Day 35 to 40

  • ADA Boost and Gradient Boost Algorithm .
  • Machine learning interview question regarding this algorithm .

• Day 41 to 44

  • Naive Bayes Algorithm .
  • Machine learning interview question regarding this algorithm .

• Day 45 to 46

  • KNN Algorithm i.e K Nearest Neighbor

• Day 47 to 49

  • Learn about different metrics that we can use to check the accuracy of the model.

• Day 50 to 54 (Building First Machine Learning Regression Project)

  • Proceeding the whole ML lifecycle (without MLOps) and build the Regression model.
  • Preprocess the data and identify which ML algorith to choose according to the metrics that we've used .

• Day 55 to 57 (Building Second Machine Learning Classification Project)

  • Proceeding the whole ML lifecycle (without MLOps) and build the Classification model.
  • Preprocess the data and identify which ML algorith to choose according to the metrics that we've used .

• Day 58 to 59 (Building Visualization Dashboard)

  • Visualize the dataset and create an insightful dashboard .

• Day 60

  • Let's move to unsupervised learning ?
  • Different Unsupervised Machine Learning Algorithms ?
  • K Means Clustering Algorithm

• Day 61

  • Implementing K Means Clustering algorithm

• Day 62 (Building Recommendation System)

  • Implement the Movie genre recommendation system .

• Day 63

  • What is Deep Learning ?
  • What is Neural Network ?
  • Implementing First Neural Network .

• Day 64

  • Break down the Neural network that we've built in python .