Data Science & Machine Learning

Data science extracts insights from data using statistics, visualization, and analysis. Machine learning enables computers to learn from data and make predictions without explicit programming.

TL;DR

• Python dominates the data science ecosystem
• Start with statistics and data analysis before deep learning
• Choose the right algorithm for your problem
• Data quality and feature engineering matter more than model choice

Data Science Fundamentals

Tools & Libraries

• Python: NumPy, Pandas, Matplotlib, Seaborn
• R: tidyverse, ggplot2
• Jupyter Notebooks: Interactive development
• SQL: Data extraction and manipulation

Data Analysis

• Exploratory data analysis (EDA)
• Statistical inference
• Hypothesis testing
• Data visualization
• Feature engineering

Machine Learning

Supervised Learning

• Classification (decision trees, random forests, SVM)
• Regression (linear, polynomial)
• Ensemble methods (XGBoost, LightGBM)

Unsupervised Learning

• Clustering (K-means, DBSCAN)
• Dimensionality reduction (PCA, t-SNE)
• Anomaly detection

Deep Learning

• Neural networks fundamentals
• Convolutional Neural Networks (CNNs)
• Recurrent Neural Networks (RNNs)
• Transformers and attention mechanisms

Frameworks

• TensorFlow: Google's ML platform
• PyTorch: Research-friendly, dynamic computation
• scikit-learn: Classical ML algorithms
• Keras: High-level neural network API

MLOps & Deployment

• Model training and evaluation
• Hyperparameter tuning
• Model versioning and registry
• Deployment and serving
• Monitoring and retraining

Related Topics

• Databases - Data storage and engineering
• Python - Primary data science language
• Cloud Computing - Scalable ML infrastructure