Machine Learning using Python Deep Dive Training

Introduction

• Review of Core Python Concepts
• Anaconda Computing Environment
• Importing and manipulating Data with Pandas
• Exploratory Data Analysis with Pandas and Seaborn
• NumPy ndarrays versus Pandas Dataframes

An Overview of Machine Learning

• Machine Learning Theory
• Data pre-processing
  Missing Data
• Dummy Coding
• Standardization
• Data Validation Strategies
• Supervised Versus Unsupervised Learning

Modeling for explanation (descriptive models)

• Understanding the linear model
• Describing model fit
• Adding complexity to the model
• Explaining the relationship between model inputs and the outcome
• Making predictions from the model

Supervised Learning: Regression

• Linear Regression
• Penalized Linear Regression
• Stochastic Gradient Descent
• Decision Tree Regressor
• Random Forest Regression
• Gradient Boosting Regressor
• Scoring New Data Sets
• Cross Validation
• Variance-Bias Tradeoff
• Feature Importance

Supervised Learning: Classification

• Logistic Regression
• LASSO
• Support Vector Machine
• Random Forest
• Ensemble Methods
• Feature Importance
• Scoring New Data Sets
• Cross Validation

Unsupervised Learning: Clustering

• Preparing Data for Ingestion
• K-Means Clustering
• Visualizing Clusters
• Comparison of Clustering Methods
• Agglomerative Clustering and DBSCAN
• Evaluating Cluster Performance with Silhouette Scores
• Scaling
• Mean Shift, Affinity Propagation and Birch
• Scaling Clustering with mini-batch approaches

Clustering for Treatment Effect Heterogeneity

• Understand average versus conditional treatment effects
• Estimating conditional average treatment effects for a sample
• Summarizing and Interpreting

Data Munging and Machine Learning Via H20

• Intro to H20
• Launching the cluster, checking status
• Data Import, manipulation in H20
• Fitting models in H20
• Generalized Linear Models
• naïve bayes
• Random forest
• Gradient boosting machine (GBM)
• Ensemble model building
• automl
• data preparation
• leaderboards
• Methods for explaining modeling output

Introduction to Natural Language Processing (NLP)

• Transforming Raw Text Data into a Corpus of Documents
• Identifying Methods for Representing Text Data
• Transformations of Text Data
• Summarizing a Corpus into a TF—IDF Matrix
• Visualizing Word Frequencies

NLP Normalization, Parts-of-speech and Topic Modeling

• Installing And Accessing Sample Text Corpora
• Tokenizing Text
• Cleaning/Processing Tokens
• Segmentation
• Tagging And Categorizing Tokens
• Stopwords
• Vectorization Schemes for Representing Text
• Parts-of-speech (POS) Tagging
• Sentiment Analysis
• Topic Modeling with Latent Semantic Analysis

NLP and Machine Learning

• Unsupervised Machine Learning and Text Data
• Topic Modeling via Clustering
• Supervised Machine Learning Applications in NLP

Conclusion