From Data Science to GenAI: A Roadmap Every Aspiring ML/GenAI Engineer Should Follow
Most freshers jump straight into ChatGPT and LangChain tutorials. That’s the biggest mistake.
If you want to build a real career in AI, start with the core engineering foundations — and climb your way up to Generative AI systematically.
Starting TIP: Don't use sklearn, only use pandas and numpy
Here’s how:
1. Start with Core Programming Concepts
Learn OOPs properly — classes, inheritance, encapsulation, interfaces.
Understand data structures — lists, dicts, heaps, graphs, and when to use each.
Write clean, modular, testable code. Every ML system you build later will rely on this discipline.
2. Master Data Handling with NumPy and pandas
Create data preprocessing pipelines using only these two libraries.
Handle missing values, outliers, and normalization manually — no scikit-learn shortcuts.
Learn vectorization and broadcasting; it’ll make you faster and efficient when data scales.
3. Move to Statistical Thinking & Machine Learning
Learn basic probability, sampling, and hypothesis testing.
Build regression, classification, and clustering models from scratch.
Understand evaluation metrics — accuracy, precision, recall, AUC, RMSE — and when to use each.
Study model bias-variance trade-offs, feature selection, and regularization.
Get comfortable with how training, validation, and test splits affect performance.
4. Advance into Generative AI
Once you can explain why a linear model works, you’re ready to understand how a transformer thinks.
Key areas to study:
Tokenization: Learn Byte Pair Encoding (BPE) — how words are broken into subwords for model efficiency.
Embeddings: How meaning is represented numerically and used for similarity and retrieval.
Attention Mechanism: How models decide which words to focus on when generating text.
Transformer Architecture: Multi-head attention, feed-forward layers, layer normalization, residual connections.
Pretraining & Fine-tuning: Understand masked language modeling, causal modeling, and instruction tuning.
Evaluation of LLMs: Perplexity, factual consistency, hallucination rate, and reasoning accuracy.
Retrieval-Augmented Generation (RAG): How to connect external knowledge to improve contextual accuracy.
You don’t need to “learn everything” — you need to build from fundamentals upward.
When you can connect statistics to systems to semantics, you’re no longer a learner — you’re an engineer who can reason with models.
0
Показать ещё
