# 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

#### Notes

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