1. Core Python:
- Variables: Containers for storing data values.
- Numbers: Integers, floating-point numbers, and complex numbers for numerical operations.
- Strings: Ordered sequences of characters used for text manipulation.
- If condition: Conditional statements for executing code based on certain conditions.
- For loop: Iterative control structure used for iterating over sequences or collections.
- Lists, Dictionaries, Sets, Tuples: Fundamental data structures for storing and organizing data.
- JSON: Lightweight data interchange format for transmitting data between a server and a web application.
- Functions and Lambda Functions: Blocks of reusable code for performing specific tasks.
- Classes and objects, OOPs Concepts, Inheritance, Polymorphism: Object-oriented programming concepts for creating modular and reusable code.
- Scope: Defines the visibility and accessibility of variables within a program.
- Logging: Mechanism for recording events occurring in a software application.
- Modules: Python files containing definitions and statements to be used in other Python scripts.
- Exception handling: Mechanism for handling errors and exceptions in Python code.
- Generators, Iterators: Constructs for creating iterators in an efficient and concise manner.
- File handling: Operations for reading from and writing to files.
- List Comprehensions: Concise syntax for creating lists based on existing lists.
- Decorators: Functions that modify the behavior of other functions without changing their code.
- Multithreading, Multiprocessing, Async IO: Techniques for concurrent execution of code to improve performance and responsiveness.
2. Data Structures:
- Memory Management: Mechanisms for efficiently allocating and deallocating memory resources.
- Hashing Concepts: Techniques for generating fixed-size representations of data.
- Queues, Stacks, and other primary Python Structures: Abstract data types for storing and retrieving data in specific orders.
3. Git:
- Version control system for tracking changes in source code during software development.
4. Numpy, Pandas, Data Visualization, Exploratory Data Analysis:
- Numpy: Library for numerical computing in Python, providing support for large, multi-dimensional arrays and matrices.
- Pandas: Library for data manipulation and analysis, offering data structures and operations for working with structured data.
- Data Visualization: Techniques for visually representing data to extract insights and patterns.
- Exploratory Data Analysis (EDA): Process of analyzing datasets to summarize their main characteristics, often using statistical graphics and other data visualization methods.
5. SQL Concepts:
- Different types of Joins: Techniques for combining rows from multiple tables based on related columns.
- Memory Management in RDMS: Strategies for optimizing memory usage in Relational Database Management Systems.
- Common Table Expressions, Window Functions, Sub Query: Advanced SQL features for complex data manipulation and analysis.
6. Deep Learning:
- RNN, Forecasting models, Transformers: Advanced neural network architectures for sequence modeling, time series forecasting, and natural language processing tasks.
- Text preprocessing: Techniques for cleaning and preparing text data for deep learning models.
- LLM Models in Hugging face: Large Language Models developed by Hugging Face for various NLP tasks.
7. Prompt engineering:
- OpenAI, Langchain, Llamaindex: Platforms and tools for building and fine-tuning large language models for specific tasks.
8. Fine-tune LLM Models in Hugging face:
- Process of customizing pre-trained language models to perform specific tasks or adapt to specific domains by fine-tuning their parameters and training them on task-specific data.
9. Github Actions for CI/CD, containerization USING Docker and container orchestration using Kubernetes
10.Pytest for Unit Testing of the Python code
Classes will include:
1. Lectures on each topic
2. Quiz on each topic
3. Assignment on each topic
4. Three projects to show on Github
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