2025 AI ENGINEER MASTERY GUIDE
YOUR STEP-BY-STEP PATH TO AI EXPERTISE
Prerequisites Step 0: Protect Yourself from Scams ❌
Required Skills: Tips:
Basic coding knowledge (Python preferred). Beware of promises for instant results; learning AI
Foundational math skills: algebra, calculus, and probability. requires consistent effort.
Verify the credibility of courses and mentors.
AI Engineer = Data Scientist + Software engineer Research industry trends and demands before starting.
Key Resource:
Core Skills for AI Engineers: Tool Skills: Snyk Blog: Scam Awareness in AI Education.
Computer Science Fundamentals. Python, SQL.
Data Analysis. Git, GitHub.
Machine Learning. Scikit-learn, TensorFlow, Step 1: Computer Science Fundamentals
NLP & Computer Vision. PyTorch. Topics:
MLOps. Data representation, binary systems.
Basics of algorithms and computer networks.
Problem-solving techniques.
Step 2: Learn Python Basics Learning Resources:
Topics: CS50 by Harvard.
Python Basics: Variables, loops, data structures (lists, dictionaries, sets). Brilliant.org: Fundamentals of Computer Science.
Functions and Lambda Expressions. Khan Academy: Introduction to Computer Programming.
Modules: NumPy, Pandas, Matplotlib for data handling and visualization.
Learning Resources:
Corey Schafer’s Python Tutorials (YouTube).
Step 4: Data Structures Step 5: SQL and
FreeCodeCamp Python Tutorials. and Algorithms (DSA) Databases
Automate the Boring Stuff with Python (Book).
Topics: Topics:
Arrays, Linked Lists, Stacks, Basic queries: SELECT,
Step 3: Version Control (Git, GitHub) Queues. WHERE, GROUP BY.
Topics: Sorting: Bubble, Merge, Quick Sort. Advanced concepts: Joins,
Setting up repositories and branches. Graphs and Trees. subqueries, stored
Git basics: add, commit, push, merge. Learning Resources: procedures.
Learning Resources: FreeCodeCamp’s DSA Playlist. Learning Resources:
Git & GitHub Crash Course. LeetCode for Practical Exercises. Khan Academy SQL.
Atlassian Git Tutorials. Data Structures by MIT Mode Analytics SQL Tutorial.
GitHub Crash Course by Traversy Media (YouTube). OpenCourseWare.
Step 6: Math & Statistics for AI Step 7: Machine Learning Additional Platforms
Topics:
Topics:
Preprocessing: Handling missing data, normalization, encoding. and Tools
Descriptive and inferential statistics.
Supervised Learning: Linear Regression, Decision Trees. YouTube Channels: Sentdex,
Linear Algebra: Matrices, Eigenvalues.
Unsupervised Learning: Clustering, PCA. StatQuest, 3Blue1Brown.
Probability distributions, Bayes Theorem.
Evaluation Metrics: Confusion Matrix, Precision, Recall. Online Courses: edX,
Learning Resources:
Learning Resources: Coursera, Udemy.
Khan Academy: Statistics and Probability.
Hands-On Machine Learning by Aurélien Géron (Book). Interactive Coding Platforms:
3Blue1Brown YouTube Channel.
Andrew Ng’s ML Course (Coursera). Kaggle, HackerRank,
Introduction to Statistical Learning (Book)
Kaggle Datasets for ML Projects. DataCamp.
Step 8: NLP and Computer Vision Step 9: MLOps Step 10: Deep Learning
Topics:
NLP Topics: Topics:
Neural Networks: Forward/Backward
Text processing, tokenization. CI/CD pipelines.
Propagation.
BERT, GPT, and Transformers. Model deployment (AWS, Docker,
Architectures: CNNs,
Libraries: Hugging Face, Spacy, NLTK. Kubernetes).
RNNs, Transformers.
Computer Vision Topics: Monitoring model performance.
Learning Resources:
Image processing, object detection (YOLO, SSD). Learning Resources:
Deep Learning
Neural Networks: CNNs. Full Stack Deep Learning Tutorials.
Specialization by Andrew
Learning Resources: FastAPI and Docker for Deployment.
Ng (Coursera).
NLP Playlist by The AI Epiphany.
TensorFlow/Keras
Stanford CS231n (Computer Vision).
Tutorials.
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