Practical Data Science and Machine Learning with Python Training

• Defining Data Science and Machine Learning
  • What is Data Science?
  • The Data Science Domain Diagram
  • The Scientific Method
  • Data Scientist vs Machine Learning Specialist
  • The Common Activities and Tasks
  • Making Predictions ...
  • A Testimony
  • “Classical” Machine Learning and Machine Learning using Neural Networks
  • Tools of the Trade
  • Data Analytics Gotchas
  • There is a Lot to Learn ...
  • Summary
• Core Data Science and Machine Learning Concepts and Terminology
  • Doing the Labs and Hands-on Exercises
  • The Shared Concepts and Terminology
  • Features
  • Targets (Labels)
  • Feature Importance
  • Observations (Examples/Cases)
  • Continuous Features
  • Categorical Features
  • Features Types Visually
  • Hands-on Exercises
  • The Machine Learning Process
  • What is a Machine Learning Model?
  • And Mathematically Speaking ...
  • The Desired Model Properties
  • Parametric and Non-parametric Models
  • Modeling Error Factors
  • Underfitting and Overfitting
  • One Way to Visualize Bias and Variance
  • Underfitting vs Overfitting Visualization
  • Balancing Off the Bias-Variance Ratio
  • Supervised and Unsupervised ML
  • Feature Engineering
  • Feature Blending (Creating Synthetic Features)
  • Scaling of the Features
  • The z-Score Normalization Formula
  • Hands-on Exercises
  • The Linear Algebra Functions in NumPy
  • Vectors
  • Visualizing the Vector
  • Vectors Geometrically
  • Vector Notation
  • Vector Scalar Operations
  • Vector Addition and Subtraction Operations
  • Matrices
  • Matrix Operations
  • Matrix Multiplication
  • Matrix Multiplication Visually
  • Common Representations
  • Matrix Transpose Operation
  • Hands-on Exercises
  • Common Distance Metrics
  • The L2 Norm
  • Cosine Similarity and Distance
  • Hands-on Exercises
  • Dimensionality Reduction (DR)
  • Common DR Algorithms and Techniques
  • The Advantages of DR
  • Hands-on Exercises
  • The Mean Squared Error (MSE)
  • Mean Absolute Error (MAE)
  • Hands-on Exercises
  • The Error Rate
  • Confusion Matrix
  • Confusion Matrix Components
  • The Binary Classification Confusion Matrix
  • Example of a Binary Confusion Matrix
  • Example of a Multi-Class Confusion Matrix
  • Precision and Recall
  • The F1 Score
  • Hands-on Exercises
  • Measuring the Impurity
  • The Shannon Formula
  • Gini Impurity
  • Example of Using Gini Impurity Formula
  • Visualization of a Decision Tree Algorithm For Binary Classification
  • Hands-on Exercises
  • After a Job Well Done ...
  • Summary
• Machine Learning Project Roadmap
  • The Data-Related Roles
  • The Typical Machine Learning Data Processing Pipeline
  • The Data Discovery Phase
  • The Data Harvesting Phase
  • The Data Priming Phase
  • Exploratory Data Analysis
  • Feature Importance
  • Feature Importance Metrics
  • The Model Planning Phase
  • The Typical Machine Learning Process Diagram
  • Which ML Algorithm to Choose?
  • The Model Building Process
  • Data Splitting into Training, Validation, and Test Datasets
  • Data Splitting Considerations
  • Avoiding Test Data Leakage
  • Cross-Validation Technique
  • Training Error vs Validation Error Diagram
  • The Grid Search Technique
  • Grid Search Caveats
  • Data Splitting, Cross-Validation, and Grid Search Flow Chart
  • Communicating the Results
  • Incorporate Feedback
  • The Operational Aspect
  • Data Processing Workflow Engines
  • Scikit-learn Pipelines
  • Production Roll-out
  • Summary
• Data Repairing Methods and Techniques
  • Quality is not an Act
  • Losing a Wheel
  • Data Irregularities
  • Dealing with the Missing Data
  • Missing Data Representations
  • Getting Information About the Missing Data (NaN)
  • Dropping Rows/Column with NaNs
  • Dropping Duplicate Rows
  • The dropna() Function
  • Examples of Using dropna()
  • Interpolating Missing Data in pandas
  • Examples of Interpolating Missing Data in pandas
  • Replacing the Missing Values with the User-defined Value
  • Examples of Using fillna()
  • Using NumPy to Mask Data
  • Example of Using a Masked Array
  • Summary
• Summarizing and Analyzing Data with Descriptive Statistics
  • What are Descriptive Statistics?
  • Geometric Visualization of Central Tendency Measures
  • Calculating Descriptive Statistics and Summary Measures in pandas
  • Calculations Along axes
  • Examples of Axis-Specific DataFrame Operations
  • The nlargest() and nsmallest() Methods
  • Using NumPy for Calculating Descriptive Statistics Measures
  • Finding Min and Max in NumPy
  • Boxplots
  • The Boxplot Visually
  • Correlation
  • Multicollinearity
  • Non-uniformity of a Probability Distribution
  • The Skew Measure
  • Dealing with Skewed Data
  • Summary
• Data Visualization and EDA in Python
  • The Quote of the Day
  • Data Visualization
  • What is Exploratory Data Analysis?
  • Data Visualization in Python
  • Using the Graphics Libraries in Python
  • Matplotlib
  • Getting Started with matplotlib
  • Visualization with pandas
  • Using matplotlib.pyplot.plot() Example
  • The matplotlib.pyplot.bar() Function
  • The matplotlib.pyplot.pie () Function
  • X and Y Axis Labeling
  • Labeling Plotted Elements
  • Displaying a Part of the Graph
  • Style Themes
  • Setting the Global Runtime Configuration Parameters
  • Annotating Plots with Text
  • Showing Images
  • Figures
  • The matplotlib.pyplot.subplot() Function
  • A Subplot Example
  • The Axes Object
  • The Subplotting Idiom with Axes
  • Boxplots in Matplotlib
  • 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
  • A Seaborn Scatterplot with Varying Point Sizes and Hues
  • Plotly
  • ggplot
  • Summary
• Supervised Learning: Regression Models
  • What is Regression?
  • Linear Regression
  • The (Linear) Regression Mathematical Formulation
  • p-values in Linear Regression (LR)
  • Non-linear Regression Models
  • Polynomial Regression
  • Spline Regression
  • The Locally-Weighted Linear Regression
  • Machine Learning Algorithms for Regression
  • Fitting the Regression Model
  • Gradient Descent (GD)
  • The GD Algorithm in a Nutshell
  • Stochastic Gradient Descent (SGD)
  • Decision Trees
  • Decision Tree Regressor
  • Random Forest Regression
  • Gradient Boosting
  • XGBRegressor
  • Evaluating Regression Model Accuracy
  • The Coefficient of Determination (the R2 Score)
  • The Mean Squared Error Score
  • Problems with the Input Data (1 of 2)
  • Problems with the Input Data (2 of 2)
  • Summary
• Supervised Machine Learning: Classification Models
  • Doing the Labs and Hands-on Exercises
  • Classification Defined
  • Predicting that a Creature is a Duck
  • Machine Learning Classification Tasks Examples
  • How is Classification Performed?
  • Two Categories of Classification Algorithms
  • Instance-Based Learning
  • Model-Based Learning
  • Classification Algorithms
  • k-Nearest Neighbors and Radius Neighbors Algorithms
  • kNN's Characteristics
  • The k-Nearest Neighbors Algorithm Visually
  • Hands-on Exercise
  • Support-Vector Machines (SVMs)
  • SVM Limitations
  • SVM Classification Visually
  • SMV Mathematical Formulation
  • Dealing with Non-Linear Class Boundaries
  • Examples of SVM Decision Boundaries
  • Hands-on Exercise
  • Decision Trees
  • An Annotated Decision Tree
  • Decision Trees in a Nutshell
  • Decision Trees: What the Customer Really Needed ...
  • Properties of the Decision Tree Algorithm
  • Controlling the Overfitting of Decision Trees
  • The Measure-and-Divide Policy
  • Information Gain
  • Gini Impurity
  • Example of Using Gini Impurity Formula
  • Random Forests
  • Example of Random Forests Model's Rules
  • Hands-on Exercise
  • Logistic Regression
  • Logistic Regression's Problem Domain
  • Logistic Regression vs Ordinary Linear Regression
  • The Probabilistic Dimension of Logistic Regression
  • The Math Behind
  • Multi-class Logistic Regression
  • A Classification Example with Logistic Regression
  • Hands-On Exercise
  • The Naive Bayes Classifier
  • The Naive Bayes Classifier Implementations
  • The Bayes' Theorem in a Nutshell
  • The Classification Algorithm
  • The Final Brick in the Wall ...
  • Classification of Documents with Naive Bayes
  • Summary
• Unsupervised Machine Learning - Clustering
  • Doing the Labs and Hands-on Exercises
  • Unsupervised Machine Learning
  • Unsupervised Machine Learning Algorithms
  • Unsupervised Learning Type: Clustering
  • Clustering Examples
  • k-means Clustering
  • k-means Clustering in a Nutshell
  • k-means Clustering Visually
  • k-means Characteristics
  • The k-means Objective Function
  • Global Minimum vs Local Minimum Explained
  • Hands-on Exercise
  • The Silhouette Coefficient (Score)
  • Interpreting the Silhouette Score Values
  • The Silhouette Scores Visually
  • Hands-on Exercise
  • Hierarchical Clustering
  • Hierarchical Clustering Strategies
  • Common Cluster Linkage Criterion
  • An Agglomerative Clustering Example
  • How Many Clusters Is There?
  • The Mean Shift Clustering
  • Mean Shift Algorithm's Properties
  • Affinity Propagation
  • Example of Clustering using Affinity Propagation
  • Min-Batch k-means
  • Min-Batch k-means in scikit-learn
  • BIRCH
  • What's in a Name?
  • Trees and Nodes
  • The BIRTH Hyperparameters
  • The BIRCH Algorithm in scikit-learn
  • Hands-on Exercise
  • DBSCAN
  • DBSCAN Characteristics
  • DBSCAN Concepts
  • The DBSCAN Algorithm in a Nutshell
  • A DBSCAN Clustering Example
  • A Comparison of The Clustering Algorithms in scikit-learn
  • Summary
• Unsupervised ML - Dimensionality Reduction
  • PCA
  • Where PCA Can Help
  • PCA and Data Variance
  • PCA Components Visually
  • PCA Variants
  • Importance of Feature Scaling in PCA Visually
  • Handling Large Datasets
  • Dimensionality Reduction in Action
  • The t-SNE Embeddings
  • t-SNE Visually
  • Summary
• Introduction to Natural Language Processing
  • What is Natural Language Processing (NLP)?
  • Typical NLP Use Cases
  • How Do Machines Understand Text?
  • Popular NLP and Text Mining Libraries
  • Getting the Text Data (Data Collection)
  • Text Formats
  • Common Text Preprocessing Activities
  • Text Normalization
  • The Stop Words
  • Stemming
  • Lemmatization
  • The POS Tagging
  • Name-Entity Recognition
  • Text Corpus Vocabulary
  • Documents as Vectors
  • OOV Tokens
  • The Bag of Words
  • N-Grams
  • TF-IDF
  • The Feature Hashing Trick
  • Cosine Similarity and Distance
  • Limitations of BoW and TF-IDF Representation Schemes
  • Word Embedding
  • Creating Word Embeddings
  • The Word2vec Model
  • Gensim in Action (Bring in Your Own Protractor)
  • Summary
• Introduction to Inferential Analytics (Supplementary Chapter)
  • Descriptive Statistics vs. Inferential Statistics
  • Population and Sample Visually
  • Population Parameters vs Sample Statistics
  • Examples
  • Estimating (Population) Parameters
  • The Null and Alternative Hypotheses
  • Making Hypotheses ...
  • The Classical Hypothesis-Testing Methodology
  • A Court Proceeding Example
  • Hypothesis Testing in Production Example
  • The Great Tragedy of Science ...
  • Types of Errors
  • Normal Distribution
  • The Normal Distribution and the 68-95-99.7 Empirical Rule
  • Normal Distribution in Statistics
  • The Standard Normal Distribution
  • The Normal Distribution PDF Simplification
  • The Box Plot and PDF Relationship Visually
  • A Confidence Interval
  • The Population Percentiles
  • Percentiles for Samples
  • The Z-Score
  • Getting from Z-Score Back to X
  • Finding Z for the Sampling Distribution of the Mean
  • Quiz
  • Z-Score Tables
  • Z-Score Table Variations
  • The Cumulative Z-Score Table
  • Finding a Z-Score Example
  • A Two-Tail Z-Score Example
  • A Quiz
  • Defining the Confidence Interval
  • The Level of Confidence
  • The Power of Test
  • Putting it All Together
  • The Algorithm
  • The Calculations
  • The p-Value
  • Putting the p-Value to Test
  • T-Tests
  • T-Score
  • Chi-Square Tests
  • The Chi-Square Statistic
  • Chi-Square Test Types
  • ANOVA
• Introduction to Python (Supplementary Chapter)
  • What is Python?
  • The Friendly Python
  • The Zen of Python (Abbreviated), by Tim Peters
  • Python Documentation
  • Where Can I Use Python?
  • Python Development Environments
  • PEP8
  • Variables
  • Types
  • Variable Names
  • More on Variables
  • More on Built-In Types
  • The Layout of Python Programs
  • Comments and Triple-Delimited String Literals
  • Sample Python Code
  • Getting Help on Python Objects
  • None
  • Strings
  • String Public Method Names
  • Useful String Methods: split()
  • String Formatting
  • The Boolean Type
  • Boolean Operators
  • Relational Operators
  • Numbers
  • "Easy Numbers"
  • Looking Up the Runtime Type of a Variable
  • Divisions
  • Assignment-with-Operation
  • Control Flow With The if-elif-else Triad
  • Conditional Expressions (a.k.a. Ternary Operator)
  • The While-Break-Continue Triad
  • The for Loop
  • The range() Function
  • Examples of Using range()
  • Exceptions and Errors
  • The try-except-finally Construct
  • Lists
  • Slicing Lists (Cont'd from Previous Slide)
  • Main List Methods
  • A Preamble to List Comprehension
  • List Comprehension
  • Zipping Lists
  • The enumerate() Function
  • Dictionaries
  • Creating Dictionaries
  • Getting and Setting Dictionary Values
  • Iterating Over Dictionaries
  • Sets
  • Creating Sets
  • Set Methods
  • Set Operations
  • Examples of Set Operations
  • Finding Unique Elements in a List
  • Common Collection Functions and Operators
  • Tuples
  • Unpacking Tuples
  • Built-in Functions
  • Functions
  • The "Call by Sharing" Parameter Passing
  • Global and Local Variable Scopes
  • Using Global Variables in Functions
  • Default Parameters
  • Named Parameters
  • Dealing with an Arbitrary Number of Parameters
  • Keyword Function Parameters
  • Python Modules
  • Importing Modules
  • Working with Files
  • Reading and Writing Files
  • Summary

Lab Exercises

• Lab 1. Learning the Colab Jupyter Notebook Environment
• Lab 2. Core Data Science and ML Concepts
• Lab 3. Data Repairing and Priming
• Lab 4. Obtaining Descriptive Statistics
• Lab 5. Understanding Statistical Concepts (Optional)
• Lab 6. Data Visualization and EDA in Python
• Lab 7. Linear Regression Models
• Lab 8. Regression Models
• Lab 9. Classification with kNN
• Lab 10. Classification with SVM and Using Grid Search
• Lab 11. Classification with Random Forest Project (Optional)
• Lab 12. Logistic Regression Models
• Lab 13. Decision Tree Classifier Visualization
• Lab 14. Assessing Feature Importance
• Lab 15. Understanding the k-means Algorithm
• Lab 16. Understanding the Silhouette Score
• Lab 17. BIRCH (Optional)
• Lab 18. Understanding PCA
• Lab 19. Understanding t-SNE
• Lab 20. Introduction to NLP
• Lab 21. Using Naive Bayes Classifier for Sentiment Analysis
• Lab 22. Getting Started with spaCy
• Lab 23. Word Embeddings (Optional)