Data Science & Artificial Intelligence
Ontology of Data Science
- Business Requirement Gathering and understanding
- Basic of Probability and Statistics
- Python as a Data Science/Artificial Intelligence Language
- 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
- 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
- Image Processing
- Convolutional features for visual recognition
- Object Detection
- Object tracking and Action Recognition
- Image Segmentation and Synthesis
- 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
University degree with mathematics background, preferably in any of the following disciplines: B.Sc. (Stat, Math, Physics, Chemistry, Geology) or B.E/B.Tech
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