Machine Learning with Python Training

Chapter 1. Python for Data Science

• In-Class Discussion
• Python Data Science-Centric Libraries
• NumPy
• NumPy Arrays
• Select NumPy Operations
• SciPy
• pandas
• Creating a pandas DataFrame
• Fetching and Sorting Data
• Scikit-learn
• Matplotlib
• Seaborn
• Python Dev Tools and REPLs
• IPython
• Jupyter
• Jupyter Operation Modes
• Jupyter Common Commands
• Anaconda
• Summary

Chapter 2. Defining Data Science

• What is Data Science?
• Data Science, Machine Learning, AI?
• The Data-Related Roles
• The Data Science Ecosystem
• Tools of the Trade
• Who is a Data Scientist?
• Data Scientists at Work
• Examples of Data Science Projects
• An Example of a Data Product
• Applied Data Science at Google
• Data Science Gotchas
• Summary

Chapter 3. Data Processing Phases

• Typical Data Processing Pipeline
• Data Discovery Phase
• Data Harvesting Phase
• Data Priming Phase
• Exploratory Data Analysis
• Model Planning Phase
• Model Building Phase
• Communicating the Results
• Production Roll-out
• Data Logistics and Data Governance
• Data Processing Workflow Engines
• Apache Airflow
• Data Lineage and Provenance
• Apache NiFi
• Summary

Chapter 4. Descriptive Statistics Computing Features in Python

• Descriptive Statistics
• Non-uniformity of a Probability Distribution
• Using NumPy for Calculating Descriptive Statistics Measures
• Finding Min and Max in NumPy
• Using pandas for Calculating Descriptive Statistics Measures
• Correlation
• Regression and Correlation
• Covariance
• Getting Pairwise Correlation and Covariance Measures
• Finding Min and Max in pandas DataFrame
• Summary

Chapter 5. Repairing and Normalizing Data

• Repairing and Normalizing Data
• Dealing with the Missing Data
• Sample Data Set
• Getting Info on Null Data
• Dropping a Column
• Interpolating Missing Data in pandas
• Replacing the Missing Values with the Mean Value
• Scaling (Normalizing) the Data
• Data Preprocessing with scikit-learn
• Scaling with the scale() Function
• The MinMaxScaler Object
• Summary

Chapter 6. Data Visualization in Python

• Data Visualization
• Data Visualization in Python
• Matplotlib
• Getting Started with matplotlib
• The matplotlib.pyplot.plot() Function
• The matplotlib.pyplot.bar() Function
• The matplotlib.pyplot.pie () Function
• Subplots
• Using the matplotlib.gridspec.GridSpec Object
• The matplotlib.pyplot.subplot() Function
• Figures
• Saving Figures to a File
• Seaborn
• Getting Started with seaborn
• Histograms and KDE
• Plotting Bivariate Distributions
• Scatter plots in seaborn
• Pair plots in seaborn
• Heatmaps
• ggplot
• Summary

Chapter 7. Data Science and ML Algorithms in scikit-learn

• In-Class Discussion
• Types of Machine Learning
• Terminology: Features and Observations
• Representing Observations
• Terminology: Labels
• Terminology: Continuous and Categorical Features
• Continuous Features
• Categorical Features
• Common Distance Metrics
• The Euclidean Distance
• What is a Model
• Supervised vs Unsupervised Machine Learning
• Supervised Machine Learning Algorithms
• Unsupervised Machine Learning Algorithms
• Choosing the Right Algorithm
• The scikit-learn Package
• scikit-learn Estimators, Models, and Predictors
• Model Evaluation
• The Error Rate
• Confusion Matrix
• The Binary Classification Confusion Matrix
• Multi-class Classification Confusion Matrix Example
• ROC Curve
• Example of an ROC Curve
• The AUC Metric
• Feature Engineering
• Scaling of the Features
• Feature Blending (Creating Synthetic Features)
• The 'One-Hot' Encoding Scheme
• Example of 'One-Hot' Encoding Scheme
• Bias-Variance (Underfitting vs Overfitting) Trade-off
• The Modeling Error Factors
• One Way to Visualize Bias and Variance
• Underfitting vs Overfitting Visualization
• Balancing Off the Bias-Variance Ratio
• Regularization in scikit-learn
• Regularization, Take Two
• Dimensionality Reduction
• PCA and isomap
• The Advantages of Dimensionality Reduction
• The LIBSVM format
• Life-cycles of Machine Learning Development
• Data Splitting into Training and Test Datasets
• ML Model Tuning Visually
• Data Splitting in scikit-learn
• Cross-Validation Technique
• Hands-on Exercise
• Classification (Supervised ML) Examples
• Classifying with k-Nearest Neighbors
• k-Nearest Neighbors Algorithm
• k-Nearest Neighbors Algorithm
• Hands-on Exercise
• Regression Analysis
• Regression vs Correlation
• Regression vs Classification
• Simple Linear Regression Model
• Linear Regression Illustration
• Least-Squares Method (LSM)
• Gradient Descent Optimization
• Multiple Regression Analysis
• Evaluating Regression Model Accuracy
• The R
• 2
• Model Score
• The MSE Model Score
• Logistic Regression (Logit)
• Interpreting Linear Logistic Regression Results
• Decision Trees
• Decision Tree Terminology
• Properties of Decision Trees
• Decision Tree Classification in the Context of Information Theory
• The Simplified Decision Tree Algorithm
• Using Decision Trees
• Random Forests
• Hands-On Exercise
• Hands-on Exercise
• Support Vector Machines (SVMs)
• Naive Bayes Classifier (SL)
• Naive Bayesian Probabilistic Model in a Nutshell
• Bayes Formula
• Classification of Documents with Naive Bayes
• Unsupervised Learning Type: Clustering
• Clustering Examples
• k-Means Clustering (UL)
• k-Means Clustering in a Nutshell
• k-Means Characteristics
• Global vs Local Minimum Explained
• Hands-On Exercise
• XGBoost
• Gradient Boosting
• Hands-On Exercise
• A Better Algorithm or More Data?
• Summary

Chapter 8. AI Systems and Platforms Overview

• Heuristics and Expert Systems
• What is AI?
• AI, Machine Learning (ML), and Deep Learning
• Neural Networks in AI
• Deep Learning Neural Networks
• TensorFlow
• Keras
• Colab Notebooks
• PyTorch
• ML at Scale: Python on Spark - PySpark
• AWS IoT Service
• AWS ML Services
• SageMaker
• DeepLens
• The DeepLens Device (an IoT Device)
• A DeepLens Use Case
• Rekognition
• Rekognition's Object and Scene Detection Demo
• AWS ML Algorithm Marketplace

Chapter 9. Text Mining and NLP Overview

• What is Text Mining?
• The Common Text Mining Tasks
• What is Natural Language Processing (NLP)?
• Some of the NLP Use Cases
• Machine Learning in Text Mining and NLP
• Machine Learning in NLP
• TF-IDF
• The Feature Hashing Trick
• Stemming
• Example of Stemming
• Stop Words
• Popular Text Mining and NLP Libraries and Packages
• Google Natural Language Cloud Service
• Trying it Out
• How Google NL Service Works
• Google Translate Service
• Comprehend
• How Comprehend Works
• Comprehend in the AWS Management Console
• Use Cases for Comprehend
• Lex
• Polly
• Polly's Text-to-Speech Dashboard
• Example of Using Polly's AWS CLI
• Transcribe
• Translate