Data AI Guide

Data Science & AI Guide

Master data science, machine learning, generative AI, and modern AI engineering practices.

Quick Start

Python Data Science Stack

import pandas as pd
import numpy as np
from sklearn.model_selection import train_test_split
from sklearn.ensemble import RandomForestClassifier

# Load and prepare data
df = pd.read_csv('data.csv')
X = df.drop('target', axis=1)
y = df['target']

# Split data
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2)

# Train model
model = RandomForestClassifier(n_estimators=100)
model.fit(X_train, y_train)

# Evaluate
accuracy = model.score(X_test, y_test)

Deep Learning with PyTorch

import torch
import torch.nn as nn

class SimpleNN(nn.Module):
    def __init__(self):
        super().__init__()
        self.linear1 = nn.Linear(784, 128)
        self.linear2 = nn.Linear(128, 10)

    def forward(self, x):
        x = torch.relu(self.linear1(x))
        return self.linear2(x)

# Training loop
model = SimpleNN()
optimizer = torch.optim.Adam(model.parameters())
criterion = nn.CrossEntropyLoss()

LLM Prompt Engineering

from openai import OpenAI

client = OpenAI()

response = client.chat.completions.create(
  model="gpt-4",
  messages=[
    {"role": "system", "content": "You are a helpful assistant."},
    {"role": "user", "content": "What is machine learning?"}
  ],
  temperature=0.7
)

Data Science Path

Fundamentals

• Mathematics: Statistics, linear algebra, calculus
• Python: Libraries (Pandas, NumPy, Scikit-learn)
• Data Analysis: Exploratory analysis, visualization
• SQL: Querying and data manipulation

Machine Learning

• Supervised Learning: Regression, classification
• Unsupervised Learning: Clustering, dimensionality reduction
• Model Evaluation: Cross-validation, metrics
• Hyperparameter Tuning: Grid search, Bayesian optimization

Deep Learning

• Neural Networks: Architecture, training
• CNNs: Computer vision tasks
• RNNs: Sequence modeling
• Transformers: Modern architecture for NLP/Vision

Natural Language Processing

• Text Processing: Tokenization, embeddings
• Word Embeddings: Word2Vec, GloVe, FastText
• BERT: Contextual embeddings
• Transformers: GPT, BERT for various NLP tasks

Generative AI & LLMs

Large Language Models

• GPT Family: GPT-3.5, GPT-4 for text generation
• Claude: Constitutional AI models
• Open Source: Llama, Mistral, Zephyr
• Fine-tuning: Adapting models for specific tasks

Prompt Engineering

• Role-based Prompting: Setting context and expertise
• Few-shot Learning: Examples in prompt
• Chain-of-Thought: Step-by-step reasoning
• Retrieval Augmented Generation (RAG): Knowledge augmentation

# RAG Example
from langchain.vectorstores import Chroma
from langchain.embeddings import OpenAIEmbeddings
from langchain.chains import RetrievalQA

embeddings = OpenAIEmbeddings()
vectorstore = Chroma(embedding_function=embeddings)

qa = RetrievalQA.from_chain_type(
  llm=llm,
  chain_type="stuff",
  retriever=vectorstore.as_retriever()
)

AI Agents

• Tool Use: Agents calling external tools
• Planning: Multi-step task execution
• Memory: Conversation history, context
• Evaluation: Assessing agent performance

Data Engineering

ETL Pipelines

• Apache Airflow: Workflow orchestration
• dbt: Data transformation
• Kafka: Stream processing
• Spark: Distributed processing

Big Data

• Hadoop: Distributed storage and processing
• Spark: In-memory processing framework
• Scala: Spark's native language
• Distributed Systems: Understanding CAP theorem

Data Warehousing

• Snowflake: Cloud data warehouse
• BigQuery: Google's data warehouse
• Redshift: AWS data warehouse
• Star Schema: Dimensional modeling

MLOps

Model Management

• Model Versioning: Tracking model versions
• Model Registry: MLflow, Weights & Biases
• Experiment Tracking: Monitoring training runs
• Model Cards: Documenting model capabilities

Deployment

• Model Serving: FastAPI, TFServing
• Containerization: Docker for models
• Kubernetes: Production ML deployment
• API Monitoring: Performance and data drift

Monitoring

• Data Drift: Detecting distribution changes
• Model Drift: Performance degradation
• Feature Store: Consistent feature serving
• Observability: Logging and metrics

Technology Stack

Core Libraries

• Pandas: Data manipulation
• NumPy: Numerical computing
• Scikit-learn: Machine learning
• Matplotlib/Seaborn: Visualization
• Plotly: Interactive plots

Deep Learning

• TensorFlow: Keras API, distributed training
• PyTorch: Dynamic graphs, research-friendly
• JAX: Functional programming for ML

LLM Frameworks

• LangChain: Building LLM applications
• LlamaIndex: RAG and indexing
• OpenAI API: GPT models access
• Hugging Face: Model hub and transformers

Learning Path

1. Fundamentals (3 months)

   • Python programming
   • Statistics and mathematics
   • Data manipulation with Pandas

2. Machine Learning (3 months)

   • Supervised learning
   • Model evaluation
   • Feature engineering

3. Deep Learning (2 months)

   • Neural networks
   • CNNs and RNNs
   • Transformers

4. Specialization (ongoing)

   • NLP / Computer Vision / Tabular Data
   • LLMs and generative AI
   • MLOps and production

Projects

1. Iris Classification - Classic ML project
2. Housing Price Prediction - Regression
3. Sentiment Analysis - NLP with transformers
4. Image Classification - CNN with deep learning
5. LLM Chatbot - Using prompt engineering
6. RAG System - Knowledge-augmented AI
7. Time Series Forecasting - Stock predictions

Resources

Learning Platforms

• Coursera: Andrew Ng's ML course
• Fast.ai: Practical deep learning
• DataCamp: Interactive data science
• Kaggle: Competitions and datasets

Documentation

• Scikit-learn
• PyTorch
• Hugging Face
• OpenAI API

Roadmap.sh Reference: https://roadmap.sh/ai-engineer

Status: ✅ Production Ready | SASMP: v1.3.0 | Bonded Agent: 04-data-ai-specialist