1. Python Programming
   
   • Anaconda Installation
   • Getting Started with VS Code
   • Python Basics-Syntax and Semantics
   • Variables in Python
   • Basics Data Types in Python
   • Operators in Python
2. Python Control Flow
   Conditional Statements (if, elif, else)
   • Loops in Python
3. Inbuilt Data Structures in Python
   List and List Comprehension
   Tuple
   • Sets
   • Dictionaries
4. Functions
   Getting Started with Functions
   • More Coding Example with Functions
   • Lambda Function
   • Map Function
   • Filter Function
5. Importing Creating Modules and Packages
   • Standard Library Overview
6. File Handling
   File Operation
   • Working with File Paths
7. Exception Handling
   Exception Handling With try except else and finally blocks
8. OOPS with Classes and Objects
   Classes and Objects
   Inheritance
   • Polymorphism
   • Encapsulation
   • Abstraction
   • Magic Methods
   • Operator Overloading
   • Custom Exception Handling
9. Advance Python
   Iterators
   • Generators
   • Decorators
10. Data Analysis with Python
    NumPy
    Pandas- DataFrame And Series
    Data Manipulation with Pandas and Numpy
    Reading Data from Various Data Source Using Pandas
    Data Visualization with Matplotlib
    Data Visualization with Seaborn
11. Python Multi-Threading and Multi Processing
    What Is Process and Threads
    • Thread Pool Executor and Process Pool
    • Implement Web Scraping Use Case with Multithreading
    • Real World Use Case Implementation with Multiprocessing
12. Memory management
    Memory Allocation and Deallocation Garbage collection
13. Flask Framework
    Introduction to Flask Framework
    • Integrating HTML with Flask Web App
    • Working with HTTP Verbs Get and Post
    • Building Dynamic Url, Variables Rule and Jinja 2 Template Engine
    • Working with Rest API's And HTTP Verbs Put and Delete
14. Streamlit Web Framework

• Statistics
  What is Statistics And its Application
  • Types of Statistics
  • Population Vs Sample Data
  • Measure of Central Tendency
  • Measure of Dispersion
  • Why Sample Variance Is Divided By n-1?
  • Standard Deviation
  • What Are Variables?
  • What are Random Variables
  • Histograms- Descriptive Statistics
  • Percentile and Quartiles- Descriptive Statistics
  • 5 Number Summary-Descriptive Statistics
  • Correlation and Covariance
• Introduction to Probability
  Addition Rule (For Mutual and Non-Mutual Exclusive Events)
  • Probability-Multiplication Rule (Independent and Dependent Events)
  • The Relationship Between PDF, PMF and CDF
  • Types of Probability Distribution
  • Bernoulli Distribution
  • Binomial Distribution
  • Poisson Distribution
  • Normal/ Gaussian Distribution
  • Standard Normal Distribution and Z Score
  • Uniform Distribution
  • Log Normal Distribution
  • Power Law Distribution
  • Pareto Distribution
  • Central Limit Theorem
  • Estimates
• Inferential Statistics
  Hypothesis Testing and Mechanism
  • What is P value?
  • Z Test- Hypothesis Testing
  • Student t Distribution
  • T Stats with T test Hypothesis Testing
  • Z test Vs T test
  • Type 1 And Type 2 Error
  • Bayes Theorem
  • Confidence Interval and Margin Of Error
  • What is Chi Square Test
  • Chi-square Goodness OF Fit
  • Annova Test
  • Assumptions of Annova
  • Types of Annova
  • Partitioning of Variance in Annova

• Handling Missing Values
• Handling Imbalanced Dataset
• Handling Imbalanced Dataset Using Smote
• Handling Outliers Using Python
• Data Encoding- Nominal Or OHE
• Label and Ordinal Encoding

Target Guided Ordinal Encoding

• Introduction to Machine Learning
  Introduction
  • Types of ML Techniques
  • Equation of Line, 3d, and Hyperplane
  • Distance of a point from a plane
  • Instance based Vs Model based learning
• Linear Regression
  Simple Linear Regression Introduction
  • Understanding Simple Linear Regression Equations
  • Cost Function
  • Convergence Algorithm
  • Multiple Linear regression
  • Performance Metrics
  • MSE, MAE, RMSE
  • Overfitting and Underfitting
  • Linear Regression with OLS
  • Multiple Linear regression
  • Polynomial Regression
• Ridge, Lasso and ElasticNet ML Algorithms
  • Ridge Regression
  • Lasso & ElasticNet
  • Types Of cross Validation
  • Cleaning the Dataset
  • EDA and Feature Engineering
  • Feature Selection
  • Model Training
  • Hyperparameter tuning
• Step By step Project Implementation with Life cycle of ML Project
  Basic Simple Linear Regression Project
  • Multiple Linear Regression Projects with Assumptions
  • Basic Regression Project from Scratch-EDA and Feature Engineering
  • Model Training with Cross Validation Using Lasso Regression
  • Model Training with Ridge and Elastic net With Cross Validation
  • Model Pickling in ML Project
  • End to End ML Project Implementation
• Logistic Regression
  • Logistic Regression In-depth Math Intuition
  • Performance Metrics
  • Logistic Regression OVR
  • Logistic Regression Implementation
  • Grid Search Hyper Parameter
  • Randomized Search CV
  • Logistic OVR
  • Logistic Imbalanced Dataset
  • Logistic Regression ROC
• Support Vector Machines
  Introduction to support vector Machine
  • Soft Margin and Hard Margin
  • SVM Math Intuition
  • SVC Cost function
  • Support Vector Regression
  • SVM Kernels
  • Support Vector Classifiers
  • SVM Kernels implementation
  • Support Vector Regression Implementation
• Naive Bayes Theorem
  Understanding Bayes Theorem
  • Variants of Naive Bayes
  • Naive Bayes Implementation
• K Nearest Neighbour ML Algorithm
  KNN Classification and Regression In-depth Intuition
  • Optimization Of KNN- KD Tree and Ball Tree In-depth Intuition
  • KNN Classifier and Regressor Classification
• Decision Tree Classifier and Regressor
  Introduction to Decision Tree.
  • Entropy and Gini Impurity
  • Information Gain
  • Decision Tree Split for Numerical Features
  • Post Pruning & Pre-Pruning
  • Decision Tree Regression
  • Decision Tree Implementation
  • Decision tree Pre-Pruning
  • Diabetes Prediction Using Decision Tree Regressor
• Random Forest
  Bagging & Boosting Ensemble Techniques.
  • Random Forest Regression
  • Problem Classification
    Feature Engineering
  • Model Training
• Adaboost
  Introduction to Adaboost algorithm
  • Creating Decision Tree Stump
  • Performance of Decision Tree Stump
  • Updating Weights
  • Normalizing Weights and Assigning Bins
  • Selecting New Datapoints for Next tree
  • Final Prediction for Adaboost
  • Adaboost Model Training
  • Adaboost Regressor Model Training
• Gradient Boosting
• XG Boost
  • Unsupervised ML
• PCA
  Curse of Dimensionality
  • Feature Selection and Extraction
  • PCA Maths Intuition
  • Eigen Decomposition on Covariance Matrix
  • PCA Implementation
• Clustering Techniques
  K Means Clustering
  • Hierarchal Clustering
  • Agglomerative Clustering Implementation
  • K Means vs Hierarchal Mean Clustering
  • DBSCAN
    Silhoutte
• Anomaly Detection Algorithms
  • Anomaly Detection using Isolation Forest In-depth Intuition
  • DBSCAN Clustering Anomaly Detection
  • Local Outlier Factor Anomaly Detection
• End to End ML Project with Azure Deployment