Register Yourself for Data Science(AI/ML) Training
Data Science Syllabus
Python Basics
- Introduction to Python
- Variables, Data Types, and Operators
- Control Structures (if/else, for loops)
- Functions and Modules
- Data Structures (Lists, Tuples)
- Data Structures (Dictionaries, Sets)
- Object-Oriented Programming (Classes, Objects)
- Object-Oriented Programming (Inheritance, Polymorphism)
- File Input/Output
- Exception Handling
Data Science Fundamentals
- Introduction to Data Science
- Data Types and Sources
- Data Preprocessing (Cleaning, Handling Missing Data)
- Data Preprocessing (Normalization, Feature Scaling)
- Data Visualization (Matplotlib, Seaborn)
- Data Visualization (Plotting, Visualization Best Practices)
- Data Analysis (Pandas, NumPy)
- Data Analysis (Data Manipulation, Analysis)
- Data Analysis (GroupBy, PivotTables)
- Data Analysis (Merging, Joining Data)
Machine Learning
- Introduction to Machine Learning
- Supervised Learning (Regression, Classification)
- Scikit-learn Library
- Model Selection and Evaluation
- Model Training and Evaluation
- Ensemble Methods
- Deep Learning Introduction
- Neural Networks
- Convolutional Neural Networks
- Recurrent Neural Networks
Advanced Data Science Topics
- Natural Language Processing (NLP)
- Text Preprocessing
- Sentiment Analysis
- Time Series Analysis
- Forecasting
- Web Scraping
- Beautiful Soup
- Scrapy
- Advanced Data Visualization
- Interactive Visualization
Advanced Machine Learning Topics
- Advanced Ensemble Methods
- Advanced Deep Learning Topics
- Transfer Learning
- Generative Adversarial Networks (GANs)
- Reinforcement Learning
- Advanced NLP Topics
- Advanced Time Series Analysis
- Advanced Web Scraping
- Advanced Data Visualization
- Advanced Interactive Visualization
