Roadmap to Become an AI Engineer – Complete Skill Guide
AI Engineering is one of the fastest-growing career paths today. To master it, you need strong foundations in programming, mathematics, machine learning, deep learning, NLP, and Generative AI. This content is designed to take you step by step through everything you need to learn to become a successful AI Engineer.
1. Python for AI & Gen AI
A strong grasp of Python is the foundation of AI.
Core Python Concepts: Variables (Global & Local), Data Types (String, List, Tuple, Set, Dictionary, Frozenset), Statements & Operators, Loops (For, While).
Functions: User Defined, Arguments, Lambda Functions, Modules & Packages.
Advanced: File Handling, OS Module, Datetime, Exception Handling, OOP (Class, Object, Inheritance, Polymorphism, Encapsulation, Abstraction).
Other: Regular Expressions, Web Scraping, NumPy, Pandas.
2. Mathematics for Data Science
Mathematics builds the logic behind AI models.
Linear Algebra: Matrices, Multiplication, Addition.
Probability & Statistics: Bayes Theorem, Normal, Poisson & Binomial Distributions, Conditional Probability, Hypothesis Testing, P-Values, Confidence Intervals, Type I & II Errors.
Calculus: Differential Equations, Integrals.
Descriptive Statistics: Mean, Median, Mode, Variance, Standard Deviation.
3. Machine Learning (ML)
Learn how machines learn from data.
Types of ML: Supervised, Unsupervised, Reinforcement Learning.
Core Algorithms: Regression (Linear, Polynomial), Classification (Logistic, Decision Trees, SVM), Clustering (K-Means, Hierarchical, DBSCAN).
Ensemble Learning: Bagging, Boosting (XGBoost, AdaBoost).
Feature Engineering: Handling Missing Data, Feature Selection (Filtering, Wrapper, PCA), Encoding (One-Hot, Label, Ordinal), Scaling (Min-Max, Standard).
Optimization: Hyperparameter Tuning (Grid, Random, Bayesian), Learning Rate, Optimization Strategies.
4. Deep Learning (DL)
Dive into neural networks and advanced architectures.
Core Concepts: Perceptrons, MLPs, Activation Functions (ReLU, Sigmoid, Tanh, Softmax), Backpropagation, Gradient Descent.
CNNs: Convolutions, Filters, Pooling (Max, Average), ResNet, EfficientNet.
RNNs: LSTM, GRU, Vanishing Gradient Problem.
Generative Models: Autoencoders, GANs, Conditional GANs.
Advanced: Attention Mechanisms, Transfer Learning, Dropout, BatchNorm, Regularization.
5. Natural Language Processing (NLP) & Generative AI
Introduction: What is NLP? What is Generative AI? Real-World Use Cases.
Fundamentals: Tokenization, Lemmatization, Stop Words, POS Tagging, NER.
Feature Representation: BoW, TF-IDF, Word2Vec, GloVe.
Models: RNNs, LSTMs, GRUs, Applications in NLP.
5.5 Transformers & Attention
Why Transformers? Limitations of RNNs & LSTMs, Self-Attention, Multi-Head Attention.
Variants: BERT, GPT, T5, Advanced Attention (MQA, GQA, Flash Attention).
5.6 Generative AI
Models: Autoregressive (GPT) vs Autoencoding (BERT), LLMs (GPT-4, PaLM, Claude, LLaMA).
Pretrained Models: BERT, RoBERTa, GPT, T5, XLNet.
Tools: Hugging Face, spaCy, NLTK.
Multi-Modal: DALL·E, CLIP, Flamingo.
Techniques: RLHF, RAG.
6. Prompt Engineering
What is Prompt Engineering? Importance in LLMs.
Techniques: Few-Shot, Zero-Shot, Chain-of-Thought Prompting, Iterative Refinement, Dynamic Prompting with LangChain.
7. Agentic AI
What is Agentic AI? Task-Oriented vs Autonomous AI.
Core Components: Decision-Making, Feedback Loops, Dynamic Adaptation.
Advanced: ReAct Framework, Multi-Agent AI Systems, CrewAI, LangGraph.
Applications: Task Automation, Gaming, Healthcare, Logistics, Collaborative AI Workflows.
