Introduction
Artificial Intelligence (AI) is transforming industries by solving complex problems and enhancing efficiency. If you're passionate about technology and have a knack for coding and math, a career as an AI engineer can be incredibly fulfilling. This roadmap will guide you from being a complete beginner to becoming a proficient AI engineer, ready to tackle real-world challenges.
Prerequisites
Before starting, it's important to have a basic understanding of coding and math. These foundational skills will make your learning journey smoother and more effective.
Total Duration: 8 Months (4 Hours of Study Every Day)
Week 1-2: Computer Science Fundamentals 💻
Objective: Understand the basics of computer science.
Topics:
Data Representation: Learn about bits and bytes, how text and numbers are stored, and the binary number system.
Computer Networks: Understand IP addresses, internet routing protocols, and the basics of computer networking.
Protocols: Get familiar with UDP, TCP, HTTP, and how the World Wide Web works.
Programming Basics: Learn about variables, strings, numbers, conditional statements, and loops.
Algorithm Basics: Understand what algorithms are and how they are designed.
Learning Resources: Khan Academy course
Week 3-4: Beginners Python
Objective: Learn the basics of Python programming.
Topics:
Variables and Data Types: Understand how to work with variables, numbers, and strings.
Data Structures: Learn about lists, dictionaries, sets, and tuples.
Control Flow: Master if conditions and for loops.
Functions: Write and use functions, including lambda functions.
Modules: Learn how to install and use Python modules.
File Operations: Read from and write to files.
Exception Handling: Understand how to handle exceptions.
Object-Oriented Programming: Learn the basics of classes and objects.
Learning Resources: Revanth Quick Learn
Week 5-6: Data Structures and Algorithms in Python
Objective: Understand data structures and algorithms.
Topics:
Data Structures Basics: Learn about arrays, linked lists, hash tables, stacks, and queues.
Tree and Graph Structures: Understand how trees and graphs work.
Algorithms: Study binary search, bubble sort, quick sort, and merge sort.
Recursion: Understand the concept of recursion and how to implement recursive algorithms.
Week 7-8: Advanced Python
Objective: Deepen your Python programming knowledge.
Topics:
Advanced Concepts: Learn about inheritance, generators, and iterators.
List Comprehensions and Decorators: Master these powerful Python features.
Concurrency: Understand multithreading and multiprocessing.
Learning Resources: Revanth Quick Learn
Week 9: Version Control (Git, GitHub)
Objective: Learn version control using Git and GitHub.
Topics:
Version Control Basics: Understand what a version control system is and the basics of Git.
Basic Commands: Learn how to add, commit, and push changes.
Branches and Merges: Understand how to work with branches, revert changes, and merge code.
Collaboration: Learn how to use pull requests for code reviews.
Week 10-11: SQL
Objective: Learn SQL for database management.
Topics:
Basics of Relational Databases: Understand the fundamentals of relational databases.
Basic Queries: Learn how to use SELECT, WHERE, LIKE, DISTINCT, BETWEEN, GROUP BY, and ORDER BY.
Advanced Queries: Master CTE, subqueries, and window functions.
Joins: Understand left, right, inner, and full joins.
Database Management: Learn about indexes and stored procedures.
Learning Resources: Khan Academy course
Week 12: Numpy, Pandas, Data Visualization
Objective: Learn essential data manipulation and visualization libraries.
Topics:
Numpy: Understand how to work with arrays and perform matrix operations.
Pandas: Learn how to manipulate data using DataFrames and Series.
Data Visualization: Use Matplotlib and Seaborn for data visualization.
Week 13-16: Math & Statistics for AI
Objective: Build a strong foundation in math and statistics.
Topics:
Statistics: Learn about descriptive vs. inferential statistics, continuous vs. discrete data, and nominal vs. ordinal data.
Linear Algebra: Understand vectors, matrices, eigenvalues, and eigenvectors.
Calculus: Get a grasp of integral and differential calculus basics.
Data Visualization: Learn how to create histograms, pie charts, bar charts, and scatter plots.
Central Tendency and Dispersion: Understand mean, median, mode, variance, and standard deviation.
Probability: Learn the basics of probability and different distributions.
Hypothesis Testing: Understand p-values, confidence intervals, type 1 vs. type 2 errors, and Z-tests.
Week 17: Exploratory Data Analysis (EDA)
Objective: Perform EDA to understand data better.
Topics:
Data Cleaning: Learn how to clean and preprocess data.
Data Visualization: Use various plots to explore data.
Insights: Derive meaningful insights from the data.
Week 18-21: Machine Learning
Objective: Learn machine learning concepts and techniques.
Topics:
Preprocessing: Handle missing values, outliers, data normalization, and encoding.
Feature Engineering: Learn how to engineer features for machine learning models.
Model Building:
Supervised Learning: Understand regression vs. classification.
Linear Models: Learn about linear regression, logistic regression, and gradient descent.
Nonlinear Models: Study decision trees, random forests, and XGBoost.
Model Evaluation: Learn how to evaluate models using various metrics.
Hyperparameter Tuning: Understand GridSearchCV and RandomSearchCV.
Unsupervised Learning: Learn about K-means clustering, hierarchical clustering, and PCA.
Week 22: ML Ops ⚙️
Objective: Understand ML operations and deployment.
Topics:
API Development: Learn how to develop APIs with FastAPI.
DevOps Fundamentals: Understand CI/CD pipelines and containerization using Docker and Kubernetes.
Cloud Platforms: Get familiar with AWS, Azure, or other cloud platforms.
Week 23-24: Machine Learning Projects with Deployment
Objective: Complete end-to-end ML projects.
Projects:
Regression Project: Develop a property price prediction model.
Classification Project: Create a sports celebrity image classification model.
Deployment: Deploy these projects on cloud platforms like AWS or Azure.
Week 25-27: Deep Learning
Objective: Master deep learning concepts and neural networks.
Topics:
Neural Networks: Learn about forward and back propagation.
Multilayer Perceptrons: Build and train multilayer perceptrons.
Advanced Architectures: Study Convolutional Neural Networks (CNNs) and Recurrent Neural Networks (RNNs).
Week 28-30: NLP or Computer Vision & Generative AI 📃
Objective: Specialize in either NLP or Computer Vision.
Natural Language Processing (NLP):
Topics:
Regular Expressions (Regex).
Text Representation: Count vectorizer, TF-IDF, BOW, Word2Vec, Embeddings.
Text Classification: Naïve Bayes.
NLP Libraries: Spacy and NLTK.
End-to-End Project.
Computer Vision (CV):
Topics:
Basic Image Processing: Filtering, edge detection, image scaling, rotation.
OpenCV: Learn to use this library for image processing.
CNNs: Deep dive into convolutional neural networks.
Data Preprocessing and Augmentation: Techniques for enhancing image data.
Week 31-32: LLM & Langchain 📃
Objective: Understand large language models and the Langchain framework.
Topics:
Large Language Models (LLMs): Understand the basics of LLMs, vector databases, and embeddings.
Retrieval Augmented Generation (RAG): Learn how to implement RAG.
Langchain Framework: Get familiar with the Langchain framework for building advanced AI applications.
Week 33 Onwards: Advanced Learning and Career Development
Objective: Continue learning and start building your professional presence.
Actions:
Work on more projects to deepen your understanding.
Build your online brand through LinkedIn, Kaggle, Discord, and open-source contributions.
Apply for jobs, prepare for interviews, and build a strong professional network.
Tips for Effective Learning
Digest, Implement, Share: Spend less time consuming information and more time digesting, implementing, and sharing what you’ve learned. This approach not only solidifies your understanding but also helps you build a portfolio that showcases your skills.
Group Learning: Join study groups or learning communities to collaborate and hold each other accountable. Group discussions can provide new perspectives and solutions to problems you might not have considered.
Practice Regularly: Consistency is key. Make sure you dedicate a few hours each day to learning and practicing. Regular practice helps reinforce concepts and improve your problem-solving skills.
Seek Feedback: Don’t hesitate to seek feedback on your projects and code. Constructive criticism can provide valuable insights and help you improve.
Stay Updated: AI is a rapidly evolving field. Stay updated with the latest research, tools, and technologies by following AI influencers, reading research papers, and participating in online forums and communities.
Conclusion
Embarking on a journey to become an AI engineer is both challenging and rewarding. By following this roadmap, you can systematically build the skills needed to excel in this field. Remember, the key to success lies in consistent learning, practical application, and staying curious. As you progress, you’ll not only gain technical expertise but also develop the critical thinking and problem-solving abilities that are essential for a successful career in AI. Good luck on your journey to becoming an AI engineer!
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