Pathway from Mechanical Engineering to AI Expert with LLM Skills

This roadmap guides you from your mechanical engineering background to AI and large language models expertise. It focuses on core concepts, practical skills, and applied projects to help you switch careers confidently.

Weekly Hours: 10
Estimated Weeks: 8

Phases

Phase 1: AI and Python Foundation

Build a basic understanding of artificial intelligence concepts and learn everyday Python. This prepares you for coding and AI problem-solving, even if you have no previous coding experience.

2 weeks

Understand what AI is
Learn simple Python basics
Solve small coding problems
Recognize AI uses in different fields

Python syntax
Basic programming logic
Problem solving
AI terminology

Intro to AI articles
Beginner Python tutorials
Simple coding challenge sites

Complete 10 beginner Python exercises in one week
Explain what AI means in 3 sentences
Write a simple Python program
List 3 real-world AI examples

Phase 2: Core Machine Learning Concepts

Learn what machine learning is and how it works. Get hands-on with basic datasets and learn steps used in machine learning projects.

2 weeks

Define machine learning
Distinguish types of machine learning
Practice with data cleaning
Work with small datasets

Data handling
Basic data visualization
Supervised vs. unsupervised learning
Model evaluation basics

Machine learning primers
Dataset practice platforms
Intro data visualization guides

Describe supervised and unsupervised learning with examples
Clean and analyze a small CSV dataset
Draw one simple data plot in Python
Evaluate basic model output

Phase 3: Introduction to Deep Learning and LLMs

Discover what deep learning is and how language models work. Start small with basic neural networks and natural language processing concepts.

2 weeks

Explain deep learning basics
Understand what large language models do
Run simple neural network code
Learn text analysis fundamentals

Neural networks basics
Text pre-processing
Basic model building
Interpret LLM outputs

Deep learning articles
Text data example sets
Neural network tutorials

Run a basic neural network on sample data
Summarize what LLMs are in 5 sentences
Preprocess text data for analysis
Test a text classification example

Phase 4: Applied Projects and Communication

Apply your new knowledge by starting a simple AI project with language data. Focus also on explaining concepts clearly to others.

1 weeks

Complete a mini project on text data
Explain your project process
Share simple insights from data
Get feedback from a peer

Project planning
Simple data storytelling
Basic report writing
Taking feedback

Sample AI project briefs
Basic presentation templates

Finish and present a small AI project
Write a 1-page summary explaining your work
Answer questions about your project
Receive feedback from someone else

Phase 5: Next Steps and Career Preparation

Plan your next steps to deepen your AI and LLM knowledge. Prepare basic materials for job or internship applications.

1 weeks

Identify further learning pathways
Update your resume with new skills
Practice describing your transition
Explore entry-level job requirements

Goal setting
Resume writing
Interview self-introduction
Job research

Resume templates
Career planning guides

List 2-3 next learning goals
Draft a resume with AI project experience
Prepare a 1-minute introduction speech
Research 3 AI-related job profiles

Weekly Plan

Week	Focus	Why	Tasks	Deliverables
1	Learn the basics of AI and Python	Fundamentals are key for advanced learning and understanding.	Read about artificial intelligence basics, Install Python on your computer, Complete three beginner Python exercises, Write a short list of real-world AI uses	Short AI summary, Completed Python exercises
2	Practice Python and explore how AI is used	Hands-on coding builds confidence and context.	Solve two more Python challenges, Write a Python program for simple calculations, Research and summarize two AI stories, Practice explaining what AI is to a friend	Simple Python script, Written explanation of AI
3	Begin learning machine learning concepts	Machine learning is the heart of AI.	Read intro to machine learning topics, List types of machine learning, Clean a small dataset using Python, Draw one graph from the data	Cleaned dataset file, Simple data plot
4	Try your first simple machine learning programs	Applying concepts boosts understanding.	Run a basic machine learning example in Python, Evaluate the model’s accuracy, Try another dataset with different data, Summarize what you learned in one paragraph	Code output screenshot, Short learning summary
5	Start with deep learning and language models	Deep learning powers modern AI, especially language AI.	Read simple deep learning articles, Run a basic neural network example, Preprocess and clean some text data, Identify uses of large language models	Neural network code run, Notes on LLM uses
6	Explore LLM applications and simple natural language tasks	Language models are central to new AI products.	Test a text classification example, Summarize main LLM tasks (like chat, translation), Compare outputs from a language model, Explain in your words how LLMs work	Short text classification summary, Explanation of LLM tasks
7	Build and explain a small AI project	Hands-on projects show real progress and skills.	Plan a small project using text data (email sorting, simple chatbot), Develop and test your model, Write a short project summary, Share your project with a friend for feedback	Project summary report, Model output screenshots
8	Plan next steps and prepare for job search	Organizing your work and preparing your story is essential.	Update your resume with new skills and the project, Practice a short introduction about your career path, Research entry AI job listings, Write down your next two learning goals	Updated resume draft, 1-minute introduction speech