Complete Machine Learning Package Outline

Introduction to Python and Working with Data

• Python for Machine Learning
• Data Computations with NumPy
• Data Manipulation with Pandas
• Data Visualization with Matplotlib
• Data Visualization with Seaborn
• Data Visualization with Pandas

Data Analysis and Preparation

• Exploratory Data Analysis
• Data Preparation
• Categorical Feature Encoding
• Feature Scaling
• Handling Missing Values

Classical Machine Learning with Scikit-Learn

• Machine Learning Fundamentals
• Intro to Scikit-Learn
• Linear Models for Regression
• Linear Models for Classification
• SVMs for Regression
• SVM for Classification
• Decision Trees for Regression
• Decision Trees for Classification
• Random Forests for Regression
• Random Forests for Classification
• Ensemble Models
• Unsupervised learning with K-Means Clustering
• A Pracrical Introduction to Principal Component Analysis

Introduction to Deep Learning

• Intro to Articial Neural Networks
• Intro to TensorFlow for Artificial Neural Networks
• Neural Networks for Regression with TensorFlow
• Neural Networks for Classification with TensorFlow

Deep Computer Vision with TensorFlow

• Intro to Computer Vision with ConvNets
• ConvNets for Real World Data and Image Augmentation
• Transfer Learning with Pretrained Convolutional Neural Networks*

Natural Language Processing with TensorFlow

• Intro to NLP and Text Processing with TensorFlow
• Using Word Embeddings to Represent Texts
• Recurrent Neural Networks (RNNs)
• Using Convolutional Neural Networks for Texts Classification
• Using Pretrained BERT for Text Classification

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Notes

*: The latest version of transfer learning and pretraining with ConvNets is available here.