• Getting started with Python
• What is Python?
• Installing Anaconda
• Variables, and Data Structure
• List, tuples and dictionary
• Control Structure
• Functions in python
• Lambda functions
• Object Oriented Programming
• Modules
• Using Packages
• Os package
• time and datetime
• File Handling in Python
• Miscellaneous Functions in python 

Project based on PythonProgramming

• Introduction to Statistics
• Population and Sample  
• Descriptive Statistics v/s Inferential Statistics
• Types of variable
• Categorical and Continuous Data
• Ratio and Interval
• Nominal and Ordinal Data

Descriptive Statistics

• Measure of Central Tendency – Mean, Mode and Median
• Percentile and Quartile
• Measure of Spread – IQR, Variance and Standard Deviation
• Coefficient of Variation
• Measure of Shape – Kurtosis and Skewness
• Correlation Analysis

Inferential Statistics

• Empirical Rule & Chebyshev’s Theorem
• Z Test
•  One Sample T test, independent t test
• ANOVA – f test
• Chi Square test

Working with Numpy

• NumPy Overview
• Properties, Purpose, and Types of ndarray
• Class and Attributes of ndarray Object
• Basic Operations: Concept and Examples
• Accessing Array
• Elements: Indexing, Slicing, Iteration, Indexing with Boolean Arrays
• Shape Manipulation & Broadcasting
• Linear Algebra using numpy
• Stacking and resizing the array
• random numbers using numpy

Working with Pandas

• Data Structures
• Series, DataFrame & Panel
• DataFrame basic properties
• Importing excel sheets, csv files, executing sql queries
• Importing and exporting json files
• Data Selection and Filtering
• Selection of columns and rows
• Filtering Dataframes
• Filtering – AND operaton and OR operation

Working with Pandas

• Data Cleaning
• Handling Duplicates
• Handling unusual values
• handling missing values
• Finding unique values
• Descriptive Analysis with pandas
• Creating new features
• Creating new categorical features from continuous variable
• combining multiple dataframes
• groupby operations
• groupby statistical Analysis
• Apply method
• String Manipulation 

Basic Visualization with matplotlib

• Matplotlib Features
• Line Properties
• Plot with (x, y)
• Controlling Line Patterns and Colors
• Set Axis, Labels, and Legend Properties
• Alpha and Annotation
• Multiple PlotsSubplots
• Advance visualization using seaborn
• Types of Plots and Seaborn
• Boxplots
• Distribution Plots
• Countplots
• Heatmaps
• Voilin plots
• Swarmplots and pointplots

Project on Data Visualization and Analytics

• Data Science Standard Project
• Data Science Project Life cycle
• Project Topic
• Data Capturing
• Data Cleaning 
• Data Analytics
• Working on tools
• Data Visualization tools
• Project Report Completion

• The conceptual idea of linear regression
• Predictive Equation
• Cost function formation
• Gradient Descent Algorithm
• OLS approach for Linear Regression
• Multivariate Regression Model
• Correlation Analysis – Analyzing the dependence of variables
• Apply Data Transformations
• Overfitting 
• L1 & L2 Regularization
• Identify Multicollinearity in Data Treatment on Data
• Identify Heteroscedasticity Modelling of Data
• Variable Significance Identification
• Model Significance Test
• R2, MAPE, RMSE
• Project: Predictive Analysis using Linear Regression 

• Classification Problem Analysis
• Variable and Model Significance
• Sigmoid Function
• Cost Function Formation
• Mathematical Modelling 
• Model Parameter Significance Evaluation
• implementing logistic regression using sklearn
• Performance analysis for classification problem
• Confusion Matrix Analysis
• Accuracy, recall, precision and F1 Score
• Specificity and Sensitivity
• Drawing the ROC Curve
• AUC for ROC 
• Classification Report Analysis
• Estimating the Classification Model
• Project: Predictive Analysis using Logistic Regression

K Nearest Neighbour

• Understanding the KNN
• Distance metrics
• KNN for Regression & classification
• implementing KNN using Python
• Case Study on KNN
• handling overfitting and underfitting with KNN

Decision Tree

• Forming Decision Tree
• Components of Decision Tree
• Mathematics of Decision Tree
• Entropy Approach
• Gini Entropy Approach
• Variance – Decision Tree for Regression
• Decision Tree Evaluation
• Overfitting of Decision Tree
• Handling overfitting using hyperparameters
• Hyperparameters tuning using gridsearch
• Visualizing Decision Tree using graphviz

Support Vector Machines

• Concept and Working Principle
• Mathematical Modelling
• Optimization Function Formation
• Slack Variable
• The Kernel Method and Nonlinear Hyperplanes
• Use Cases
• Programming SVM using Python
• Project – Character recognition using SVM

Ensemble Learning

• Concept of Ensemble Learning 
• Bagging and Boosting
• Bagging – Random Forest
• Random Forest for Classification
• Random Forest for Regression
• Boosting – Gradient Boosting Trees
• Boosting – Adaboost
• Boosting – XGBoost
• Stacking

Clustering 

• Application of clustering
• Hierarchical Clustering
• K Means Clustering
• Use Cases for K Means Clustering
• Programming for K Means using Python
• Customer segmentation using KMeans
• Cluster Size Optimization vs Definition Optimization

Dimensionality Reduction – PCA

• Dimensionality Reduction, Data Compression
• Curse of dimensionality
• Multicollinearity
• Factor Analysis
• Concept and Mathematical modelling
• Use Cases
• Programming using Python

Projects & Case Studies

• A broad overview of terms and technology related to AI
• AI v/s Machine Learning v/s Deep Learning v/s Data Science
• Who should be involved in an AI project?
• Examining team culture, capabilities and readiness
• AI v/s Non AI
• How to decide when to use capabilities of AI for business?
• Feasibility and Profitability Analysis
• Identifying right data components
• Collecting data from outside the room
• Problem Framing
• Deciding the correct validation metric, optimizer
• Data Science and Software Engineering
• Collecting and munging Data
• Experimenting with data, features and Algorithms
• Testing and Validating models
• Version Control
• How to handle Overfitting and Underfitting?
• Size of Data and its impact in AI/ML Project lifecycle

• AI and Data Science end to end project lifecycle
• Data Science Deployment strategy
• Deployment best practices
• Deployment with Flask
• Best Practices for API design for ML services
• Deploying model with nginx and uWSGI – demo
• Machine Learning and DevOps
• Defining scalability
• Tools and techniques for scalable machine learning
• Architecture design patterns for scalable systems
• Machine learning models as services
• Containerizing models
• Best Practices of scaling machine learning models
• Finish off experiments
• Review of what has been taught
• Get individuals to create their own action list
• Final Q&A.