• NumPy
• Pandas
• Matplotlib, Seaborn, Plotly and Dask

• Introduction to Data Science
• Data Science Lifecycle
• Types of Data
• Checking the quality of data
• Exploratory data analysis
• Feature Engineering
• Tabular Data Preprocessing
  Page 2
• Image Processing using OpenCV
• Audio Data Preprocessing

• Introduction to statistics
• Types of statistics---
• Descriptive and Inferential
  Descriptive Statistics
• Variables and Types of Variables
• Measure of Center and Measure of Spread
• Measures of Central Tendency
• Measures of Dispersion
• Mean, Mode, Median
• Range, Standard Deviation, Variance, Quartile, IQR
• Covariance and Correlation between data

• Inferential Statistics
• Sample v/s Population
• Hypothesis Testing
• Null and Alternative hypotheses
• Type I error vs Type II error
• Establishing a rejection region and a significance level
• What is the p-value and why is it one of the most useful tools for statisticians
• Learning about T-test
• One Sample, two Sample T-test
• Analysis of Variance
• Chi-square Analysis
• Parametric and non-parametric tests

• Introduction to Database

• Component of Database

• Types of Databases

 Relational Database,
 Object-Oriented Database,
 Distributed Database,
 NoSQL Database,
 Graph Database,
 Cloud Database,
 Centralization Database,
 Operational Database
 Vector Databases

• Introduction to Structured Query Language
• What is RDBMS-Relational Database Management System
• Introduction and Types of SQL Operators
• Creating Databases and Tables
• Explore Entities relationships
• DDL & DML Statement
• Select Statement, Aggregate Functions
• Insert into, Where, Order By, Distinct, Group By, Like, In, Between Operators,
• Limit Aliases, and & or Clause
• Update & Delete Query
• SQL Joins-What are Joins, Inner Join, Left Join, Right Join, Full Join
• Multiple Joins-Joining More than two tables

• Subqueries
• How to write Subqueries in SQL
• Conditional Table Expression
• Windows function in SQL
• Views, functions, and Stored Procedure
• Transactions
• String, transformation, regex
• Date time manipulation

• Introduction to Machine learning
• Types of Machine learning
• Application of Machine learning

Linear models

• Introduction to linear regression
• Mathematics behind linear regression
• Leastsquare method
• How to evaluate regression model
• Python implementations
• Interpretation of Model coefficient
• Assumptions of linear regression
• Statistical test in linear regression
• Python implementations
• Interpretation of Model diagnostic plot
• Introduction to Multiple linear regression
• Features Selection and its types
• Python implementations

Regularization algorithm

• What is regularization
• Lasso regression and Its implementation in Python
• Ridge regression and Its implementation in Python
• Elastic Net regression and Its implementation in Python

Generalized Linear models

• What is GLM model
• Introduction to logistic regression
• Mathematics behind logistic regression
• Cost Function
• Odds & Odds ratio
• Model Evaluation
• Python Implementation and Interpretation

Decision Tree algorithm

• Decision Tree Algorithms
• Attribute Selection Measures
• Entropy &Information Gain
• Steps to Estimate Entropy &Information Gain
• Issues with Decision Trees
• Bias Variance Trade Off
• Decision Tree Applications
• Python Implementation

Ensemble model

• Introduction to ensemble
• Bagging, boosting, Stacking and Blending
• Introduction to bagging
• Random forest
• Reason to use
• Random Forest
• Random Forest Types
• Random Forest Applications
• Python Implementation
• Hyperparameter Tuning
• Introduction to boosting
• XgBoost, Adaboost, catboost, LightGBM
• Implementation
• Introduction to stacking
• Implementation
• Introduction to blending
• Implementation

Support Vector Machines

• Types of SVM
• Hyperplane in the SVM algorithm
• Large Margin Intuition
• Kernel in SVM
• SVM Implementation in Python

K nearest Neighbors

• Introduction to KNN
• Mathematics behind KNN
• Implementation in Python

Naïve bayes classifier

• Introduction to naïve bayes
• Mathematics behind Naïve bayes
• Curse of Dimensionality
• Implementation in Python

Dimensionality reduction algorithm

• What is dimensionality reduction
• Introduction to Principal component analysis
• Math behind it
• Implementation in Python

Segmentation techniques

• What is Clustering?
• K-Means Clustering
• When to use K-Means Clustering?
• What is K?
• Euclidean Distance
• K-Means Clustering Example
• Implementation in python
• Agglomerative clustering
• Implementation in python

Association rule algorithm

• Introduction to association rule
• Apriori algorithm
• Market basket optimization
• Implementation

Advance Machine learning

• Casual Machine learning
• Offline Machine learning
• Adversarial Machine learning
• Explainable AI
• Knowledge Distillation
• Time series analysis and forecasting
• Various time series algorithm with their implementations
• Various deployment strategies

• Introduction
• Real Life Applications of Deep Learning
• Difference between Machine learning and deep learning
• Challenges of Deep learning
• Architecture of Deep learning projects
• Various frameworks in deep learning

Deep learning with TensorFlow

• Introduction to TensorFlow

Artificial Neural network

• Introduction
• Backpropagation
• Weight and Bias
• Activation function
• Deep Neural Networks
• Implementation

Convolution Neural Network

• Introduction
• Mathematics behind CNN
• Famous CNN Architectures
• Transfer Learning
• Implementation

Recurrent Neural Network

• Introduction
• Application of RNN
• Problems with RNN
• Introduction to Long short-term memory
• Implementation
• Introduction to GRU
• Implementation
• Introduction to Seq2seq
• Implementation
• Introduction to encoder and decoder
• Implementation
• Introduction to transformer and BERT
• Implementation

Autoencoder

• Introduction
• Architecture explanation
• Application of autoencoder
• Implementation

Generative Adversarial Network

• Introduction
• Generator
• Discriminator
• Implementation

• Introduction
• Neural style transfer
• Neural machine translation
• Hybrid learning
• Algorithm in Hybrid learning along with their implementations
• Deep learning algorithm for time series forecasting

• Introduction to NLP
• What is NLP?
• Typical NLP Tasks
• Understanding text data
• Text preprocessing in Python
• Extracting Features from Text
• Bag-of-Words
• TF-IDF Similarity score
• Cosine similarity
• Sentiment analysis
• Question Generation
• Named entity recognition
• Custom Named entity recognition

• Introduction to PowerBI
• Components of PowerBI
• Types of charts in PowerBI
• How to add buttons in PowerBI
• Using Power query to preprocess the data
• Intro to the M Language
• Data modelling in PowerBI
• How to write and run DAX queries
• How to create dashboard
• How to add security on the dashboard
• Publishing the dashboard
• Refreshing the dashboard periodically

• Introduction to the big data
• Data warehouses
• ETL and ELT
• Introduction to the big data tools
• Hadoop and MapReduce
• Apache Spark
• Hive and Pig
• Apache Kafka
• Apache Airflow

AWS

• Introduction
• Creating and setup an account
• Introduction to the billing section
• IAM
• S3
• EC2
• Lambda
• Glue
• Sagemaker
• Creating an ETL pipeline
• Running machine learning code in Sagemaker
• Analyzing the data using Athena
• Monitoring the logs using CloudWatch
• MapReduce on cloud (EMR)
• Analyzing the data using QuickSight

• Introduction to MLOPS
• Data Management and Versioning
• Experimentation and Model Development
• Continuous Integration/Continuous Deployment
• Model Serving and Deployment
• Containerization and Orchestration
• Monitoring and Logging
• Model Maintenance and Retraining
• Infrastructure and Resource Management
• Security and Compliance

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