v2.0

If you haven’t already, check out our Pre-Requisites guide to get your local macbook setup and build familiarity with your code editor (Cursor)

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Overview

This roadmap is designed for complete beginners who want to become AI/ML Engineers. It focuses on the most essential skills and technologies required to land a job or build AI-powered products in 9-12 months of dedicated study.

We’ve simplified the learning path into three focused stages to help you progress efficiently:

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1. Learning Python and applied Data Science

Estimated time to complete: 2-3 months

1

Associate Data Scientist Full Course

For complete beginners.

DataCamp: Data Science with Python

Price: $39/month (Discounts available)

Python Programming Basics and Intermediate
NumPy for Numerical Computing
Pandas for Data Analysis
Matplotlib and Seaborn for Data Visualization
Scikit-learn basics

Alternatives (Free):

2

Project Building

Practice your Python and data skills by building:

Data cleaning and preprocessing pipeline
Exploratory data analysis (EDA) on real datasets
Interactive data dashboard

Datasets for Practice:

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2. MLExpert: Comprehensive Machine Learning & Interview Prep

Estimated time to complete: 1-2 months

MLExpert is a comprehensive platform designed to teach machine learning fundamentals and prepare you for ML interviews. Created by AlgoExpert and taught by Ryan Doan, an ex-Amazon ML Infrastructure Engineer with extensive industry experience.

1

ML Crash Course

MLExpert offers an intelligently organized ML crash course with 18 modules covering key concepts in:

Supervised Learning (Naive Bayes, Linear Regression, Support Vector Machines)
Unsupervised Learning (K-Means Clustering)
Deep Learning (Neural Networks)
Recommendation Systems (Ranking, Content-Based Filtering)

Each module builds on the previous one, creating a guided, comprehensive education that equips you with all the building blocks needed for machine learning interviews.

2

ML Coding Questions

Practice applied machine learning with coding questions that test your ability to implement ML concepts. These questions go beyond theory and focus on practical implementation, ensuring you’re prepared for the coding portion of ML interviews.

3

Large-Scale ML

Learn how to design large-scale machine learning systems through 16 modules that build on each other. This section goes beyond ML fundamentals and covers specialized topics required for building and scaling production ML systems.

4

ML Design Questions

Prepare for open-ended systems design questions that appear in ML interviews, such as:

Designing recommendation engines
Building fraud-detection systems
Creating voice assistants

MLExpert provides a curated list of design questions and a specialized workspace to practice these challenging problems.

5

ML Quiz & Recruiting Profile

Test your knowledge with a 75-question ML quiz covering essential concepts.

After earning the MLExpert Certificate, you can be referred to tech companies, helping you bypass traditional application channels and directly enter their interview process.

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3. ML School: Building Production-Ready Systems

Estimated time to complete: 1-2 months

ML School is a live, interactive program focused on teaching you how to design, build, and deploy production-ready machine learning systems—without the academic fluff. Taught by Santiago, an ML engineer with 30+ years of experience building systems for companies like Disney, Boston Dynamics, IBM, and others.

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Hands-On Program Structure

This program includes:

20+ hours of live, interactive sessions
Best practices for building, evaluating, and maintaining ML systems in production
Complete walkthrough of an end-to-end ML system built from scratch
Techniques for deploying anywhere using state-of-the-art tools
Lifetime access to future cohorts and a private community

Price: 300(normally300 (normally 500)

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Key Learning Modules

The program consists of six main sessions:

Day 1: How to start ML projects - pitching, selling, and launching new projects
Day 2: Building effective models - data cleaning, feature engineering, model selection
Day 3: Model evaluation - ensuring models work in real-world scenarios
Day 4: Serving predictions - versioning, deploying, and optimizing models
Day 5: Monitoring models - handling edge cases, detecting drift, building resilient systems
Day 6: Continual learning systems - automating the entire ML lifecycle
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Additional Benefits

Code walkthroughs: Access to production-ready template systems
Office hours: Weekly sessions to answer questions and connect with others
Real-world focus: Practical strategies that work in production, not just theory
Lifetime access: Pay once and join any future cohort at no additional cost

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Career Paths in AI/ML

After completing this roadmap, you can pursue various roles:

  1. Data Scientist: Building models and systems for data analysis
  2. Machine Learning Engineer: Building and deploying ML systems in production
  3. AI Application Developer: Creating applications that utilize AI capabilities
  4. Computer Vision Engineer: Specializing in image and video analysis
  5. NLP Engineer: Focusing on text and language understanding
  6. MLOps Engineer: Managing ML systems throughout their lifecycle
  7. Research Engineer: Implementing cutting-edge research in practical applications

Most entry-level positions require:

  • Strong Python programming skills
  • Understanding of ML fundamentals
  • Experience with at least one deep learning framework (TensorFlow, PyTorch, etc.)
  • Projects demonstrating real-world problem-solving

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Support System

You’re not alone in this journey:

  • Discord Community: 24/7 access to help and support
  • Project Reviews: Get feedback on your projects
  • Job Search Support: Resume reviews and interview prep
  • Mentorship Tiers: Access different levels of support based on your needs

Remember, consistent practice and building real projects are the keys to success in AI/ML. Focus on understanding the fundamentals deeply and applying them to real-world problems.