This guide is designed to provide you with a structured roadmap for learning various topics related to computer science, programming, data analysis, machine learning, and artificial intelligence. Each section outlines the key concepts you should learn and the resources you can use to master them.

• Data Representation: Learn about bits and bytes, storing text and numbers, and the binary number system.
• Computer Networks: Understand basics of computer networks, IP addresses, and Internet routing protocols.
• Programming Basics: Get acquainted with variables, strings, numbers, if conditions, and loops.
• Algorithm Basics: Learn about the fundamentals of algorithms.

• Python Basics: Cover variables, numbers, strings, lists, dictionaries, sets, tuples, if conditions, for loops, functions, and lambda functions.
• Modules: Understand how to work with modules using pip install.
• File Handling: Learn to read and write files.
• Exception Handling: Explore techniques for handling exceptions.
• Object-Oriented Programming: Understand classes and objects.

• Data Structures: Learn about arrays, linked lists, hash tables, stacks, queues, trees, and graphs.
• Algorithms: Cover binary search, bubble sort, quick sort, merge sort, and recursion.

• Advanced Concepts: Understand inheritance, generators, iterators, list comprehensions, decorators, multithreading, and multiprocessing.

• Version Control System: Learn about Git and GitHub.
• Basic Commands: Understand commands like add, commit, push, pull, and branch.
• Branching and Merging: Explore branching, reverting changes, and merging.
• Pull Requests: Understand how to create and manage pull requests.

• Database Fundamentals: Cover basics of relational databases.
• SQL Queries: Learn basic and advanced SQL queries, joins, and database manipulation.

• Mathematics Basics: Understand descriptive and inferential statistics, linear algebra, calculus, probability, and distributions.
• Statistical Analysis: Learn about correlation, covariance, central limit theorem, hypothesis testing, and exploratory data analysis.

• Preprocessing: Cover data preprocessing techniques like handling missing values, data normalization, encoding, and feature engineering.
• Model Building: Understand supervised and unsupervised learning, regression, classification, linear models, tree-based models, model evaluation, and hyperparameter tuning.

• API Development: Learn about FastAPI for Python server development.
• DevOps Fundamentals: Understand CI/CD pipelines, containerization using Docker and Kubernetes.
• Cloud Platforms: Gain familiarity with cloud platforms like AWS, Azure, etc.

• Regression Project: Develop a property price prediction model and deploy it to AWS.
• Classification Project: Build a sports celebrity image classification model and deploy it.

• Neural Networks: Understand neural network architecture, forward and backward propagation, and building multilayer perceptrons.
• Specialized Architectures: Learn about convolutional neural networks (CNNs), sequence models like RNNs and LSTMs.

• Natural Language Processing (NLP): Cover regex, text representation, text classification, and fundamentals of Spacy & NLTK library.
• Computer Vision: Explore image processing techniques and deep learning models for computer vision tasks.

• Online Courses: Platforms like Coursera, Udemy, and edX offer comprehensive courses on these topics.
• Books: Refer to textbooks and reference materials recommended by experts in the field.
• Tutorials and Documentation: Utilize tutorials and official documentation provided by programming languages, libraries, and frameworks.
• Hands-on Projects: Build real-world projects to apply your knowledge and gain practical experience.

This guide serves as a roadmap for your learning journey. Feel free to explore each topic at your own pace and dive deeper into areas that interest you the most. Remember to practice regularly and seek help from online communities and forums whenever needed. Happy learning!

For any further assistance or clarifications, feel free to reach out to the community or the respective learning resources provided.

Best regards,

Chand Rayee