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Introduction
All About course - Watch before enrolling the course
Data Understanding
All about Data Analysis : Pandas Session - 1
All about Data Analysis : Pandas Session - 2
Numpy in depth
Pandas Notebook
Data Visualization Technique - Exploratory Data Analysis - I
EDA Notebook
Data Visualization Technique - Exploratory Data Analysis - II
EDA notebook 2
Project - I : Data Analyst
Retail_data_analysis_notebook
Retail_DATA
Analyst Interview - I
DATA ANALYSIS USING PANDAS
[Revision - 1] - How to perform data analysis
data_retail_walmat([Revision - 1] - How to perform data analysis)
Data_Analyst_Interview_Questions
Basic Math
Linear Algebra - I
Linear Algebra - II
Linear Algebra - ||| (Code)
Probability and Statistics - I
Probability and Statistics - II
Calculus - 1
Calculus - 2
Calculus - 3
Calculus - 4
Calculus - 5
Calculus - 6
Calculus - 7
Calculus - 8
Calculus - 9
Calculus - 10
Calculus - 11
Models : Zero level understanding
Simple mathematical models
Model warm-up : KNN basics
Simple_Mathematcal_model
Applied Machine learning
Deep dive into KNN
Building Machine learning solution using KNN algorithm
ML Algorithms Different Cases - I
ML Algorithms Different Cases - II
ML Algorithms Different Cases- III
Performance Metrics - I
Performance Metrics - II
Logistic Regression - I
Logistic Regression - II
L2 Regularization : How to avoid overfitting
Logistic Regression: Code
GridSearch & RandomSearch : How to optimized parameters
Mini Project : Loan Approval Prediction - End to End implementation - I
Loan-Approval-Code-1
Mini Project : Loan Approval Prediction - End to End implementation - II
Loan_approval_final - II
Naive Bayes Algorithm - Basics ( What is conditional probability, bayes theorem)
What kind of problems we can solve using Naive bayes algorithm
Math behind Naive bayes - I
Math behind Naive bayes - II
Naive Bayes issue: Why we do Laplace smoothing
Naive bayes issue : Numerical stability issue
Naive Bayes : Hands-on Code
Nive_bayes_code_notebook
Introduction to Decision tree
What is Decision tree : How it is different from other algorithm
What is Entropy
What is information gain
What is gini impurity
How to construct three in Decision Tree
Hand-on Decision tree implementation
Decision Tree Learning material [Optional]
Hands-on : Decision Tree Code
How we split numerical feature in DT
Is feature scaling needed in DT
Overfitting and underfitting in DT
How DT will behave in different cases
What are Ensembles
What is bagging techniques and how it work internally
Some advantages of Bagging Technique
Introduction to Random Forest algorithm
What is OOB
Random Forest code walk through
Boosting vs bagging
Boosting in depth
Residules_Loss_Function
GBDT_XGBoost_code
Introduction to NLP
What is NLP
Problem Statement : What problem we are solving to learn NLP
Text input : How to formulate given problem into ML/DL Problem
Why to convert input text into vector or numerical form
What is Bag of Word : Vectorization technique - I
why pre-processing needed before vectorization
What is stemming and lemmatization
What is TF-IDF : Vectorization technique-2
Hands-on : Text preprocessing
Code_Text_preprocessing
what is unigram bigram and n gram in depth
What is word2vec and other variation for vectorization
Mini Project : Amazon review Sentiment Analysis
Code: Amazon review Sentiment Analysis
What is RNN - Mathematics behind RNN model
Code walkthrough RNN
Case study : Machine learning
Case Study - I
Case Study - II
Case Study - III
Interview Prep For ML/Analyst Role
Interview Prep Session - I
Interview Prep Session - II
Deep Learning
History of Neural networks and Deep Learning
How Biological Neurons work?
Growth of biological neural networks
Mathematical Notation; Feedforward, Backpropagation and weight tunning
ANN_basics
chapter3
[Continue] Backpropagation, Feedforward and activation function
Example - 1
Case Study : Deep learning
Assignments - Data Analyst
Assignment - Machine learning
Assignment - I
GenAI
Structured Query Language
Live Sessions
Doubt Session - 1
Test