OBERON GLOBAL SOLUTIONS PVT LTD

SYLLABUS - DATA ANALYTICS TRAINING

Semester 1: Foundations of Machine Learning

Module 1: Introduction to Machine Learning (2 weeks)

• Overview of machine learning

• Types of machine learning (supervised, unsupervised, reinforcement learning)

• Basic terminology and concepts

Module 2: Mathematics for Machine Learning (8 weeks)

• Linear algebra

• Calculus

• Probability and statistics

• Optimization

Module 3: Programming Fundamentals (6 weeks)

• Introduction to Python programming

• Key libraries (NumPy, Pandas)

• Data manipulation and analysis

Module 4: Data Preprocessing and Exploratory Data Analysis (4 weeks)

• Data cleaning and preprocessing techniques

• Exploratory Data Analysis (EDA)

Module 5: Supervised Learning (8 weeks)

• Regression

• Classification

• Model evaluation and selection

Semester 2: Advanced Machine Learning

Module 6: Unsupervised Learning (8 weeks)

• Clustering

• Dimensionality reduction

• Association analysis

Module 7: Neural Networks and Deep Learning (12 weeks)

• Introduction to neural networks

• Deep learning architectures (CNNs, RNNs)

• Training deep learning models

Module 8: Natural Language Processing (6 weeks)

• Text preprocessing

• Sentiment analysis

• Named Entity Recognition (NER)

Module 9: Reinforcement Learning (6 weeks)

• Basics of reinforcement learning

• Markov decision processes

• Q-learning and policy gradients

Semester 3: Specialized Topics and Applications

Module 10: Model Deployment and Production (8 weeks)

• Model deployment strategies

• API development

• Monitoring and scaling

Module 11: Specialized Applications (10 weeks)

• Computer vision

• Speech recognition

• Time series analysis

Module 12: Capstone Project (14 weeks)

• Real-world machine learning project

• Collaborative work on a comprehensive project

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