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WEEK 1  WHAT'S IN THERE  TASK 1  TASK 2  TASK 3  

Day 0 
Getting Started by setting up Anaconda
environment. 
Installing anaconda for Windows  Installing anaconda for Mac  Installing anaconda for Linux  
Day 1 
Hey there, excited to start learning? First, we will begin with learning the basics of Python. 
Beginner Tutorial for Python Programming.(videos
13) 
Moving Ahead (videos 4  6) 
Wrapping up on basics (videos 7  10) 

Day 2 
Gaining an overview of NumPy and Pandas. You will be
using them extensively in your Data Science
journey. 
Basics of Numpy (till 40 mins is sufficient) Numpy Notebook 
Intro to Pandas (videos 1 to 10)  Data Analysis with Pandas (videos 11 to 20)  
Day 3 
Continuing with Pandas, let's use it for data
cleaning and transformations 
Diving into Pandas (videos 25 to 33)  Pandas Notebook  Kaggle Microcourse on Pandas (only exercise)  
Day 4 
Data Visualization helps us in gaining insights from
the data through visuals like graphs and maps. We
would look into some common libraries which are
Matplotlib, Seaborn, and Plotly. 
Intro to Matplotlib GFG article on Matplotlib (optional but helpful) 
Intro to Seaborn GFG Tutorial on Seaborn 
Intro to Plotly (required till 1 hr) 

Day 5 
Dealing with large tables can at times become
overwhelming. Thus we may want to summarize the
content of tables using Descriptive Statistics. 
Data Types in Statistics.  Measurement of Central Tendency. (videos 3  7) 
Normal Distribution (videos 18  20) Quantile Plots 
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WEEK 2  WHAT'S IN THERE  TASK 1  TASK 2  TASK 3 

Day 1 
Hey, excited for Week 2? Often the data we deal with can have various issues like missing values, categorical values and outliers. Today we will learn about basic techniques to deal with such issues! 
Introduction to Feature Engineering Outlier Analysis 
Handling Missing Values Practical Handling Missing Values 
Exploratory Data Analysis Handling Categorical Variables 
Day 2  Today we aren't going to be too technical, let us just motivate ourselves about Machine Learning, get to know its application, and have a rudimentary understanding of what Machine Learning is. 
What is ML , common problems solved by ML 
Supervised and unsupervised learning  (optional)[Linear Algebra, Refresher required for those who don't have mathematical base] 
Day 3  Starting at the grassroots level, we study in depth the simplest ML model Linear Regression, alongwith Cost function and Gradient Descent. Don't worry if it sounds too hard, trust us it isn't. 
Linear Regression Blog  Linear Regression with One Variable (Videos 9  14) 
Linear Regression with One Variable (Videos 15  20) 
Day 4  Let us spice things up a bit, we study Linear regression again but this time with Multiple features. 
Linear Regression with Multiple Variables (Videos 21  24)  Linear Regression without Scikitlearn  Linear Regression with Scikitlearn Linear Regression with 1 variable from scratch 
Day 5  Today we will be introduced to our first ever classification model, Logistic Regression. Let's get to it.  Logistic Regression Videos 31  36 
Logistic Regression Blog  Logistic Regression with Scikitlearn Logistic Regression from scratch 
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WEEK 3  WHAT'S IN THERE  TASK 1  TASK 2  TASK 3 

Day 1 
Since we have covered 2 basic ML models, let us take a break and learn about Overfitting, Underfitting and the BiasVariance Tradeoff. These can help in telling you the complexity of your model  how well your model has used your data. This will be followed by Regularization.  BiasVariance Video Blog 
Overfitting and Regularisation (37  41) 
L1 L2 Regularization Lasso and Ridge Regression 
Day 2  Today we will give you an introduction to Feature Transforations and how these are used for different types of data  All Feature Transformation 
Scaling methods  Categorical Encoding Bag of Words 
Day 3  Today, we'll take a closer look at what the AUCROC Score is and various other Evaluation Metrics to evaluate our machine leaning algorithms.  AUC  ROC curve AUC  ROC curve Blog 
Confusion Matrix Confusion Matrix Blog 
Evaluation Metrics Evaluation Metrics (Optional Topics included) 
Day 4  Today, we look into Naive and Gaussian Naive Baye's Algorithms. Naïve Bayes algorithm is a supervised learning algorithm, which is based on Bayes theorem and used for solving classification problems. 
Multinomial Naive Bias Classifier  Gaussian Naive Bias 
Naive Bias implementation using Scikitlearn 
Day 5  Let's have a look at Support Vector Machine (SVM)  SVM 1  SVM 2 
SVM 3 SVM Blog with code implementation 
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WEEK 4  WHAT'S IN THERE  TASK 1  TASK 2  TASK 3 

Day 1 
A model's performance can be greatly increased by tuning its hyperparameters and at the same time it is also important to look for how accurate our model is. For this, we can use Grid search methods and CrossValidation.  Cross Validation Code Implementation 
What is hyperparameter tuning ? 
Implementing Random Search method. 
Day 2  Today, we shall learn about Decision Trees and Random Forest which will create the foundation for many advanced Machine Learning Algorithms.  DecisionTrees (videos 46 49)  Random Forest (videos 52  53) 
Decision Trees Notebook Random Forest implementation 
Day 3  Let's explore what boosting is and some of its variations  Gradient Boosting (videos 59  61) XGBoost 
XGBoost continued 
Catboost 1 Catboost 2 
Day 4  Let's look at some more variations of boosting algorithms and how they can be used for specialized tasks.  Adaboost 
Kaggle Intermediate microcourse 
LightGBM AdaBoost implementation 
Day 5  Today we learn about KNN. KNearest Neighbour is one of the simplest Machine Learning algorithms based on Supervised Learning technique. 
KNN video 
KNN Blog 
KNN Implementation 
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WEEK 5  WHAT'S IN THERE  TASK 1  TASK 2  TASK 3 

Day 1  Welcome to the final week of Learning, as next week is Capstone Project This week we will discuss Neural Networks and Unsupervised Learning. Today Let us go into the foundations of Neural Networks 
Neural Networks (1  23) 

Day 2  Today we will first get an intuitive understanding of how Neural Networks work and then implement a small Neural Network from scratch using Python  Understanding Neural Networks 
Neural Networks with Python 

Day 3  Today we will learn how to use the Keras library to implement Neural Networks. Keras is a popular Deep learning library which makes using Neural Networks very simple for us  Regression with Keras  Classification with Keras  
Day 4  Let us have a look at unsupervised learning, its uses and types. We will also look at one particular algorithm the Kmeans method  Unsupervised Learning  Kmeans Clustering  
Day 5  Today we will discuss PCA and its application through scikitlearn  Tools and techniques for Deep Learning. 24  43 
PCA using sklearn 
LINK  DEADLINE  INSTRUCTIONS 

Capstone Project 
July 14, 2024 