Data Science & Artificial Intelligence

Syllabus

Ontology of Data Science

  • Business Requirement Gathering and understanding
  • Basic of Probability and Statistics
  • Python as a Data Science/Artificial Intelligence Language

Machine Learning

  • Regression (Lasso, Ridge)
  • Classification (Logistic, Tree based)
  • Clustering (K-means, Fuzzy, Hierarchical, Density Based Clustering)
  • K-Nearest Neighbor
  • Support Vector Machines
  • Forecasting (ARIMA, Holtz Winters)
  • Ensemble Learning
  • Markov Models
  • Dimension Reduction
  • Bagging (Random Forest)
  • Gradient Boosting

Data Wrangling

  • Pandas
  • Outlier treatment
  • Missing values treatment
  • Handling Imbalance Data

Introduction to Deep Learning

  • Neural Network Basics
  • Reinforced And Federated Learning
  • Introduction to GAN
  • GPU, CPU, TPU architecture and their role in DL
  • Deep Neural Network
  • Convolutional Neural Networks (CNN)
  • Recurrent Neural Networks (RNN)

Natural Language Processing

  • Text Mining and Applications
  • Basics of NLP (POS, entity recognition)
  • Applications of Regular expression
  • Sentiment Analysis
  • Topic Modelling
  • Clustering in Text Documents

Computer Vision

  • Image Processing
  • Convolutional features for visual recognition
  • Object Detection
  • Object tracking and Action Recognition
  • Image Segmentation and Synthesis

Data Visualization

  • Tableau
  • Power BI

Tools for AI

  • Introduction to SQL and MySQL
  • Introduction to NoSQL and MongoDB
  • Introduction to Docker and Kubernetes
  • Data Lake and centralization strategy
  • Using cloud specific services for AI solution
  • Creating and Deploying API
  • Introduction to ELK Stack
  • Introduction to Spark and Distributed Computing

Deployment of AI Solutions

  • Unit and System Testing
  • Model Lifecycle management
  • Integration with Dev Ops and relevant architectures
  • Retraining Pipeline
  • Deployment of AI on Cloud
  • AI Ops Best Practices

Prerequisite

University degree with mathematics background, preferably in any of the following disciplines: B.Sc. (Stat, Math, Physics, Chemistry, Geology) or B.E/B.Tech

Course Overview

Course Overview

General Information
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