Machine Learning for Developers

5.

Python Machine Learning Ecosystem

5.1.

Core Scientific Computing

5.1.1.

NumPy Fundamentals

5.1.1.1.

Array Operations

5.1.1.2.

Broadcasting Rules

5.1.1.3.

Linear Algebra Operations

5.1.1.4.

Random Number Generation

5.1.2.

Pandas Data Manipulation

5.1.2.1.

DataFrame Operations

5.1.2.2.

Data Cleaning Techniques

5.1.2.3.

Grouping and Aggregation

5.1.2.4.

Time Series Handling

5.1.3.

Data Visualization

5.1.3.1.

Matplotlib Basics

5.1.3.2.

Seaborn Statistical Plots

5.1.3.3.

Interactive Visualizations

5.1.3.4.

Plot Customization

5.2.

Scikit-learn Framework

5.2.1.

API Design Principles

5.2.1.1.

Estimator Interface

5.2.1.2.

Transformer Interface

5.2.1.3.

Predictor Interface

5.2.2.

Model Selection Tools

5.2.2.1.

Cross-Validation

5.2.2.2.

5.2.2.3.

5.2.2.4.

Pipeline Construction

5.2.3.

Preprocessing Utilities

5.2.3.1.

Scaling and Normalization

5.2.3.2.

Encoding Categorical Variables

5.2.3.3.

Feature Selection Tools

5.2.4.

Model Evaluation Tools

5.2.4.1.

Metrics and Scoring

5.2.4.2.

Validation Curves

5.2.4.3.

Learning Curves

5.3.

Deep Learning Frameworks

5.3.1.

TensorFlow Ecosystem

5.3.1.1.

Core TensorFlow

5.3.1.2.

Keras High-Level API

5.3.1.3.

TensorFlow Extended

5.3.1.4.

TensorFlow Lite

5.3.2.

PyTorch Framework

5.3.2.1.

Tensor Operations

5.3.2.2.

Automatic Differentiation

5.3.2.3.

Neural Network Modules

5.3.2.4.

5.3.3.

Framework Comparison

5.3.3.1.

Use Case Suitability

5.3.3.2.

Performance Considerations

5.3.3.3.

Community and Ecosystem

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6. Data Engineering for Machine Learning