COURSE OUTLINE

INTRODUCTION TO DATA SCIENCE

1. Application of Deep Learning in Data Science
2. Deep Learning ecosystem: Python, Jupyter Notebook and TensorFlow/Keras
3. Training Neural Network
4. Understanding Overfitting and Outfitting Concepts, Convolution and Pooling etc
5. CNN Architecture: Layers and Filters
6. Word Embedding

MACHINE LEARNING

1. Introduction to Machine Learning
2. Core Learning Paradigms: Supervised, Unsupervised, and reinforcement Learning
3. Popular algorithms and their practical application
4. Deep Learning fundamentals and neural network architectures
5. Model Evaluation Techniques : Confusion Matrix, ROC, Cross-Validation and Precision-Recall Curve
6. Real-world Machine Learning Application : Autonomous Vehicles, Entertainment and Media, Healthcare, Financial Services and Industrial Automation

NATURAL LANGUAGE PROCESSING

1. Introduction to Natural Language Processing
2. Human – Computer Interaction
3. Applications of NLP in Data Science
4. Exploratory Data Analysis for Text Data
5. Exploring Relationships in Text Data using GGPLOT2
6. Advanced NLP Technique – Text Classification, Advanced Classification, and Deep Learning Integration
7. Feature Engineering and Model Training with Caret
8. Sentiment Analysis Technique
9. Execution of Real-world application

R PROGRAMMING LANGUAGE: GETTING STARTED WITH R

1. Installation and setup of R and Rstudio development environment
2. Understanding Basic R syntax, data types , and structures
3. Working with essential R packages, and functions
4. Mastring control structures and error handling
5. Performing Exploratory data analysis and visualization
6. Data Wrangling in R
7. Uses of Inferential Statistics in Programming