Roadmap to Become an AI Engineer

Overview

• AI and ML Engineers are in high demand.
• AI roles offer some of the highest salaries in tech, but come with high expectations.
• Requires strong coding and math skills.
• Study plan of 4 hours daily for 8 months using free resources.
• Importance of a lifelong learning mindset.

Getting Started

• Evaluate if AI Engineering is a suitable career for you: interest in coding and math is critical.
• Alternative roles if AI Engineering isn't a fit: AI Sales, AI Product Manager, AI Ethics Executive.

Week-by-Week Study Plan

Week 0: Research

• Investigate learning resources and courses to avoid scams.
• Check LinkedIn posts on common scams and ensure the legitimacy of instructors.

Weeks 1-2: Computer Science Fundamentals

• For non-CS background: Khan Academy course covering basics.
  • Topics: bits and bytes, storing text and numbers, basics of computer networks, HTTP, World Wide Web, basics of programming.
• Software engineering fundamentals are crucial.

Weeks 3-4: Python Basics

• Learn Python using YouTube playlists (own channel, Corey Schaffer).
• Focus on first 16 tutorials covering beginner logic in Python.
• Complete linked exercises.
• Build LinkedIn profile during this period using a provided checklist.

Weeks 5-6: Data Structures and Algorithms

• Learn about memory and CPU trade-offs, data structures work, etc.
• Recommended YouTube playlist with exercises.
• Stay motivated by watching inspirational videos.

Weeks 7-8: Advanced Python

• Topics: inheritance, generators, iterators, list comprehensions, multi-threading, multi-processing.
• Watch videos 17-27 in mentioned playlist and practice exercises.
• Follow AI influencers on LinkedIn to stay updated.

Weeks 9-11: SQL and Relational Databases

• Learn SQL to query data stored in relational databases.
• Recommended resources: Khan Academy, W3Schools, SQL Bolt.
• Participate in SQL resume project challenge on codebasics.io.

Week 12: NumPy and Pandas

• Learn NumPy and Pandas for data cleaning and exploration.
• Refer to linked learning resources and practical exercises.

Weeks 13-15: Math and Statistics for AI

• Critical topics: calculus, linear algebra, hypothesis testing, etc.
• Recommended resources: Khan Academy, StatQuest, 3Blue1Brown.
• Courses linked for industry project-based learning.

Weeks 16-17: Exploratory Data Analysis (EDA)

• Use NumPy, Pandas, Matplotlib for EDA.
• Practice EDA on Kaggle datasets.
• Work on exercises and problem statements.

Weeks 18-21: Machine Learning

• Focus on statistical machine learning, pre-processing techniques, and model building.
• Recommended YouTube playlist covering theory, coding, exercises.
• Learn about Scrum and Kanban for project management.

Week 22: MLOps

• Learn fundamentals of MLOps similar to DevOps but for ML projects.
• Key topics: API, FastAPI, Docker, Kubernetes, cloud platforms (AWS, Azure).
• Practice using tools like SageMaker.

Weeks 23-24: ML Projects

• Build machine learning projects (one regression, one classification).
• Recommended YouTube project playlists for end-to-end learning.
• Create ATS compliant resume and project portfolio website.

Weeks 25-27: Deep Learning

• Learn neural networks, convolutional neural networks, sequence models.
• Recommended TensorFlow playlist on YouTube.
• Build end-to-end deep learning project for practical experience.

Weeks 28-30: Specialization in NLP or Computer Vision

• Choose either NLP or Computer Vision.
• Recommended YouTube playlists for theory and practical exercises.

Weeks 31-32: LLM and Langchain

• Learn about Large Language Models (LLM) and Langchain framework.
• Practice with linked projects to add to your resume.

Continued Learning

• Keep working on more projects and build online credibility through LinkedIn and Kaggle.
• Apply for jobs actively.
• Adopt effective learning strategy: more time in practicing and implementing rather than just watching tutorials.
• Utilize group learning to stay motivated.

Additional Tips

• Importance of effective learning strategies: focus more on digesting and implementing rather than just consuming tutorials.
• Group learning and community engagement are crucial for sustained motivation.
• Presentation skills and online presence can heavily influence job prospects.