iamslok/ai-engineer-roadmap

A Simplified AI Engineer Roadmap 2024

Simplified AI Engineer Roadmap 2024

Prerequisites

• Skills or interests in Coding and Math

Duration

• 8 Months (4 hours of study Every Day)

Roadmap

Week 1 and 2: Computer Science Fundamentals

• Data representation: Bits and Bytes, Binary number system
• Basics of computer networks, Internet protocols
• Programming basics: variables, strings, numbers, conditions, loops
• Algorithm basics

Week 3 and 4: Beginner's Python

• Variables, Data Structures, Control Flow
• Functions, Modules, File I/O, Exception Handling
• Classes and Objects

Week 5 and 6: Data Structures and Algorithms in Python

• Data Structures: Arrays, Linked Lists, Hash Tables, Stacks, Queues, Trees, Graphs
• Algorithms: Searching, Sorting, Recursion
• Big O Notation

Week 7 and 8: Advanced Python

• Inheritance, Generators, Iterators
• List Comprehensions, Decorators
• Multithreading, Multiprocessing

Week 9: Version Control (Git, GitHub)

• Git and GitHub
• Basic Commands, Branches, Pull Requests

Week 10 and 11: SQL

• Relational Databases
• Basic and Advanced Queries
• Joins, Database Creation, Indexes, Stored Procedures

Week 12: NumPy, Pandas, Data Visualization

• NumPy
• Pandas
• Matplotlib, Seaborn

Week 13 to 16: Math & Statistics for AI

• Linear Algebra, Calculus
• Basic Plots, Measures of Central Tendency and Dispersion
• Probability, Distributions, Correlation, Covariance
• Hypothesis Testing

Week 17: Exploratory Data Analysis (EDA)

• Exploratory Data Analysis on Kaggle Datasets

Week 18 to 21: Machine Learning

• Data Preprocessing, Feature Engineering
• Supervised Learning: Regression, Classification
• Unsupervised Learning: Clustering, Dimensionality Reduction
• Model Evaluation and Hyperparameter Tuning

Week 22: ML Ops

• APIs (FastAPI)
• DevOps Fundamentals: CI/CD, Containerization
• Cloud Platforms (AWS, Azure)

Week 23 and 24: Machine Learning Projects with Deployment

• End-to-End Regression and Classification Projects
• Model Deployment (AWS, Azure)

Week 25 to 27: Deep Learning

• Neural Networks, Forward and Back Propagation
• Convolutional Neural Networks (CNNs)
• Recurrent Neural Networks (RNNs), LSTMs

Week 28 and 29: NLP or Computer Vision

• Natural Language Processing (NLP) or Computer Vision (CV)

Week 30: GenAI

• Large Language Models (LLMs)
• Retrieval Augmented Generation (RAG)

Week 31 and 32: LLM & Langchain

• Vector Databases, Embeddings
• Langchain Framework

Week 33 Onwards

• More Projects
• Online Brand Building
• Job Applications

Tips for Effective Learning

• Focus on digesting, implementing, and sharing knowledge
• Group learning and accountability partners
• Engage with the community (Discord, LinkedIn)
• Complete Roadmap > https://i.am.ai/roadmap