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      • Machine Learning
        • Week 1
          • Introduction
        • Week 2
          • Linear Regression with Multiple Variables
    • fast.ai
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      • Deep Learning Part 1: Practical Deep Learning for Coders
        • Deep Learning Part 1: 2018 Edition (v2)
          • Lesson 1
        • Deep Learning Part 1: 2019 Edition (v3)
          • Lesson 1 - Image Recognition
          • Lesson 2 - Computer Vision: Deeper Applications
          • Lesson 3 - Multi-label, Segmentation, Image Regression, and More
          • Lesson 4 - NLP, Tabular, and Collaborative Filtering
          • Lesson 5 - Foundations of Neural Networks
          • Lesson 6 - Foundations of Convolutional Neural Networks
          • Lesson 7 - ResNets, U-Nets, GANs and RNNs
      • Deep Learning Part 2: Cutting Edge Deep Learning for Coders
        • Deep Learning Part 2: 2017 Edition (v1)
          • Lesson 8 - Artistic Style
          • Lesson 9 - Generative Models
          • Lesson 10 - Multi-modal & GANs
          • Lesson 11 - Memory Networks
          • Lesson 12 - Attentional Models
          • Lesson 13 - Neural Translation
          • Lesson 14 - Time Series & Segmentation
        • Deep Learning Part 2: 2018 Edition (v2)
          • Lesson 8 - Object Detection
          • Lesson 9 - Single Shot Multibox Detector (SSD)
          • Lesson 10 - Transfer Learning for NLP and NLP Classification
          • Lesson 11 - Neural Translation; Multi-modal Learning
          • Lesson 12 - DarkNet; Generative Adversarial Networks (GANs)
          • Lesson 13 - Image Enhancement; Style Transfer; Data Ethics
          • Lesson 14 - Super Resolution; Image Segmentation with U-Net
      • Machine Learning: Intro to Machine Learning for Coders
        • Machine Learning: 2017 Edition
          • Lesson 1 - Introduction to Random Forests
          • Lesson 2 - Random Forest Deep Dive
          • Lesson 3 - Feature Engineering
          • Lesson 4 - Random Forest Interpretation
          • Lesson 5 - Train vs Test
          • Lesson 6 - What is Machine Learning and Why Do We Use It
          • Lesson 7 - Decision Trees Ensemble
          • Lesson 8 - Basic Neural Networks
          • Lesson 9 - SGD; Neural Network Training; Broadcasting
          • Lesson 10 - Logistic Regression; NLP; Naive Bayes
          • Lesson 11 - Structured and Time-Series Data
          • Lesson 12 - Entity Embeddings; Data Science and Ethics
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Machine Learning

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