AI Engineer Roadmap for Beginners

AI Engineer

Following is the roadmap to learning AI Engineer (also known as ML Engineer) skills for a total beginner. It includes learning resources for technical skills (or tool skills) and soft (or core) skills Prerequisites: You must have skills or interests to build skills in Coding and Math. Without these two you cannot become an AI engineer

AI Engineer = Data Scientist + Software Engineer

AI Engineer Tool skills
  • Python
  • SQL
  • DSA&Git and Github
  • Pandas&EDA
  • Machine Learning
  • Deep Learning
  • NLP or computer vision
  • ML Ops
AI Engineer core skills
  • Computer Science Fundamentals
  • Math and statistics
  • Communication
  • Business understanding

Computer Science Fundamentals

  • Data representation: Bits and Bytes, Storing text and numbers, Binary number system.
  • Basics of computer networks, IP addresses, Internet routing protocol.
  • UDP, TCP, HTTP, and The World Wide Web o
  • Programming basics: variables, strings, and numbers, if condition, loops
  • Algorithm basics

Beginners Python

  • Variables, Numbers, Strings
  • Lists, Dictionaries, Sets, Tuples
  • If condition, for loop
  • Functions, Lambda Functions
  • Modules (pip install)
  • Read, Write files
  • Exception handling
  • Classes, Objects

Data Structures and Algorithms in Python

  • Data structures basics, Big Onotation
  • Data structures: Arrays, Linked List, Hash Table, Stack, Queue
  • Data structures: Tree, Graph
  • Algorithms: Binary search, Bubble sort, quick sort, merge sort
  • Recursion

Advance Python

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

Version Control (Git, Github)

  • What is the version control system? What is Git and GitHub?
  • Basic commands: add, commit, push
  • Branches, reverting change, HEAD, Diff and Merge
  • Pull requests.

SQL

  • Basics of relational databases
  • Basic Queries: SELECT, WHERE LIKE, DISTINCT, BETWEEN, GROUP BY, ORDER BY
  • Advanced Queries: CTE, Subqueries, Window Functions
  • Joins: Left, Right, Inner, Full
  • Database creation, indexes, stored procedures.

Data Visualization

  • Numpy
  • Pandas
  • Data Visualization
  • Matplotlib
  • Seaborn

Math & Statistics for AI

  • Basics: Descriptive vs inferential statistics, continuous vs discrete data, nominal vs ordinal data
  • Linear Algebra: Vectors, Metrices, Eigenvalues and Eigenvectors
  • Calculus: Basics of integral and differential calculus
  • Basic plots: Histograms, pie charts, bar charts, scatter plot etc.
  • Measures of central tendency: mean, median, mode
  • Measures of dispersion: variance, standard deviation
  • Probability basics
  • Distributions: Normal distribution
  • Correlation and covariance
  • Central limit theorem
  • Hypothesis testing: p value, confidence interval, type 1 vs type 2 error, Z test

Exploratory Data Analysis (EDA)

Machine Learning

Machine Learning: Preprocessing

  • Handling NA values, outlier treatment, data normalization
  • One hot encoding, label encoding
  • Feature engineering
  • Train test split
  • Cross validation

Machine Learning: Model Building

  • Types of ML: Supervised, Unsupervised
  • Supervised: Regression vs Classification

Linear models

  • Linear regression, logistic
    regression
  • Gradient descent

Nonlinear models (tree-based models)

  • Decision tree
  • Random forest
  • XGBoost

Model evaluation

  • Regression: Mean Squared Error, Mean Absolute Error, MAPE
  • Classification: Accuracy, Precision-Recall, F1 Score, ROC Curve, Confusion matrix
  • Hyperparameter tuning: GridSearchCV, Random SearchCV
  • Unsupervised: K means, Hierarchical clustering, Dimensionality reduction (PCA)
  •  

ML Ops

  • What is API, FastAPI for Python server development
  • DevOps Fundamentals: CI/CD pipelines, containerization (Docker, Kubernetes)
  • Familiarity with at least one cloud platform (AWS, Azure etc.)

Machine Learning Projects with Deployment

You need to finish two end to end ML projects. One on Regression, the other on Classification •

Regression Project: Bangalore property price prediction

YouTube playlist link:
https://bit.ly/3ivycWr

Project covers following

  • Data cleaning
  • Feature engineering
  • Model building and hyper parameter tuning
  • Write flask server as a web backend
  • Building website for price prediction
  • Deployment to AWS

Classification Project: Sports celebrity image classification
YouTube playlist link:
https://bit.ly/3ioaMSU

Project covers following

  • Data collection and data cleaning
  • Feature engineering and model training
  • Flask server as a web backend
  • Building website and deployment

Deep Learning

Topics

  • What is a neural network? Forward propagation, back propagation
  • Building multilayer perceptron Special neural network architecture
  • Convolutional neural network (CNN)
  • Sequence models: RNN, LSTM

NLP or Computer Vision & GenAI

Many AI engineers choose a specialized track which is either NLP or Computer vision. You don’t need to learn both.

Natural Language Processing (NLP)

  • Regex
  • Text presentation: Count vectorizer, TF-IDF, BOW, Word2Vec, Embeddings
  • Text classification: Naïve Bayes
  • Fundamentals of Spacy & NLTP library
  • One end to end project

Computer Vision (CV)

  • Basic image processing techniques: Filtering, Edge Detection, Image Scaling, Rotation
  • Library to use: OpenCV
  • Convolutional Neural Networks (CNN) – Already covered in deep learning.
  • Data preprocessing, augmentation – Already covered in deep learning.
  •  

LLM & Langchain

Topics

  • What is LLM, Vector database, Embeddings
  • RAG (Retrieval Augmented Generation)
  • Langchain framework

Core Skills and Job Preparation

Create a professional-looking LinkedIn profile

Linkedin ▪ Start following prominent AI influencers

Increase engagement

  • Start commenting meaningfully on AI and career-related posts
  • Helps network with others working in the industry build connections
  • Learning and brainstorming opportunity
  • Remember online presence is a new form of resume

Business Fundamentals - Soft Skill

Learn business concepts from ThinkSchool and other YT Case Studies

Discord
Start asking questions and get help from the community.

This post shows how to ask questions the right way: https://bit.ly/3I70EbI

Core/Soft Skills

ATS Resume Preparation

ATS Resume Preparation

  • Resumes are dying but not dead yet. Focus more on online presence.
  • Here is the resume tips video along with some templates you can use for your data analyst resume:
    https://www.youtube.com/watch?v=buQSI8NLOMw
  • Use this checklist to ensure you have the right ATS Resume

Portfolio Building Resources:

You need a portfolio website in 2024. You can build your portfolio by using these free resources.

GitHub
Upload your projects with code on github and using github.io create a portfolio website

Sample portfolio website:

Linktree
Helpful to add multiple links in one page.

  • ATS friendly resume preparation
  • Linkedin optimization
  • Certificate of course completion
  • Online community access where jobs are posted
  • Interview Questions and answers
  • Bootcamp project
  • Resume & Project Related Documents