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Learning Path

Complete AI Engineer Path

Master AI and machine learning from fundamentals to deployment. Build real-world ML models and deploy them to production.

4 Phases
4 Courses
2 Tools
16-24 weeks
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Perfect For You If:

You're a beginner or intermediate learner ready to commit

You want a structured, step-by-step roadmap

You can dedicate 5-10 hours per week to learning

You want to see real results within 16-24 weeks

Not Right For You If:

You're looking for a "get rich quick" scheme

You want results without putting in the work

You're already an expert in this field

You can't commit at least 5 hours per week

Quick Wins You'll Get
Early victories you'll achieve on this path
  • Build and deploy your first machine learning model within 3 weeks
  • Create a portfolio of 5+ AI projects to showcase to employers
  • Understand neural networks deeply enough to debug and optimize them
  • Contribute to open-source AI projects and build your professional network
Beginner Mistakes to Avoid
Don't make these common errors that slow down progress

Skipping the fundamentals

Don't jump to advanced topics before mastering basics

Taking too many courses at once

Focus on one course at a time and complete it

Not building projects

Apply what you learn immediately with real projects

Giving up too early

Results take time—stick with it for at least 16-24 weeks

Your Step-by-Step Roadmap

Follow these phases in order. Don't skip ahead—each phase builds on the previous one.

ML Foundations (Weeks 1-6)
Machine Learning Fundamentals
Master supervised and unsupervised learning, understand key algorithms, and build your first models.

Action Steps:

  • 1.

    Complete 'Machine Learning Specialization' course (Andrew Ng)

  • 2.

    Implement linear regression, logistic regression, and decision trees from scratch

  • 3.

    Learn scikit-learn for practical ML model building

  • 4.

    Build 3 projects: house price prediction, spam classifier, customer segmentation

✅ Complete all action steps before moving to Deep Learning (Weeks 7-12)

Deep Learning (Weeks 7-12)
Neural Networks and Deep Learning
Understand neural networks, CNNs, RNNs, and transformers. Build deep learning models with TensorFlow/PyTorch.

Action Steps:

  • 1.

    Complete 'Deep Learning Specialization' course

  • 2.

    Learn TensorFlow or PyTorch framework

  • 3.

    Build image classification models (CNNs) and text models (RNNs, transformers)

  • 4.

    Understand transfer learning and fine-tuning pre-trained models

✅ Complete all action steps before moving to Advanced AI (Weeks 13-18)

Advanced AI (Weeks 13-18)
LLMs, Reinforcement Learning, and MLOps
Work with large language models, learn reinforcement learning, and deploy models to production.

Action Steps:

  • 1.

    Complete 'Machine Learning Path' and 'AI Programming with Python Nanodegree'

  • 2.

    Fine-tune LLMs for specific tasks using Hugging Face

  • 3.

    Learn MLOps: model versioning, monitoring, A/B testing

  • 4.

    Deploy models to cloud platforms (AWS SageMaker, Google Vertex AI)

✅ Complete all action steps before moving to Portfolio & Career (Weeks 19-24)

Portfolio & Career (Weeks 19-24)
Building Your AI Engineering Portfolio
Create impressive projects, contribute to open source, and prepare for AI engineering roles.

Action Steps:

  • 1.

    Build 3 end-to-end AI projects with real-world applications

  • 2.

    Contribute to open-source AI libraries (Hugging Face, LangChain, etc.)

  • 3.

    Write technical blog posts explaining your projects

  • 4.

    Prepare for AI engineering interviews (LeetCode, system design, ML theory)

What to Do After Completing This Path

Build a portfolio project showcasing your new skills

Apply for jobs or freelance projects in this field

Explore advanced courses to deepen your expertise

Join communities and network with other professionals

Required Courses
Take these courses in order
Step 1AI & Machine Learning

Machine Learning Specialization

Step 2AI & Machine Learning

Deep Learning Specialization

Step 3AI

AI Programming with Python Nanodegree

Step 4Machine Learning

Machine Learning Path

Required Tools
Tools you'll need for this path

Manus

AI

Notion

SaaS
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