Practical Machine Learning with Apache Spark Training

Practical Machine Learning with Apache Spark Training

Course Length:
Delivery Methods: Available for private class only
Course Topics
  • Python essentials
  • Capabilities of the Apache Spark platform and its machine learning module
  • Terminology, concepts, and algorithms used in machine learning
Course Overview

This intensive Practical Machine Learning with Apache Spark training class introduces the audience to the core aspects of scalable data processing using Python on the Apache Spark platform.

The audience for this class is data scientists, business analysts, software developers, and IT architects.

Course Outline
  1. Defining Data Science
    1. Data Science, Machine Learning, AI?
    2. The Data-Related Roles
    3. Data Science Ecosystem
    4. Business Analytics vs. Data Science
    5. Who is a Data Scientist?
    6. The Break-Down of Data Science Project Activities
    7. Data Scientists at Work
    8. The Data Engineer Role
    9. What is Data Wrangling (Munging)?
    10. Examples of Data Science Projects
    11. Data Science Gotchas
    12. Summary
  2. Machine Learning Life-cycle Phases
    1. Data Analytics Pipeline
    2. Data Discovery Phase
    3. Data Harvesting Phase
    4. Data Priming Phase
    5. Data Cleansing
    6. Feature Engineering
    7. Data Logistics and Data Governance
    8. Exploratory Data Analysis
    9. Model Planning Phase
    10. Model Building Phase
    11. Communicating the Results
    12. Production Roll-out
    13. Summary
  3. Quick Introduction to Python Programming
    1. Module Overview
    2. Some Basic Facts about Python
    3. Dynamic Typing Examples
    4. Code Blocks and Indentation
    5. Importing Modules
    6. Lists and Tuples
    7. Dictionaries
    8. List Comprehension
    9. What is Functional Programming (FP)?
    10. Terminology: Higher-Order Functions
    11. A Short List of Languages that Support FP
    12. Lambda
    13. Common High-Order Functions in Python 3
    14. Summary
  4. Introduction to Apache Spark
    1. What is Apache Spark
    2. Where to Get Spark?
    3. The Spark Platform
    4. Spark Logo
    5. Common Spark Use Cases
    6. Languages Supported by Spark
    7. Running Spark on a Cluster
    8. The Driver Process
    9. Spark Applications
    10. Spark Shell
    11. The spark-submit Tool
    12. The spark-submit Tool Configuration
    13. The Executor and Worker Processes
    14. The Spark Application Architecture
    15. Interfaces with Data Storage Systems
    16. Limitations of Hadoop's MapReduce
    17. Spark vs MapReduce
    18. Spark as an Alternative to Apache Tez
    19. The Resilient Distributed Dataset (RDD)
    20. Datasets and DataFrames
    21. Spark SQL
    22. Spark Machine Learning Library
    23. GraphX
    24. Summary
  5. The Spark Shell
    1. The Spark Shell
    2. The Spark v.2 + Shells
    3. The Spark Shell UI
    4. Spark Shell Options
    5. Getting Help
    6. The Spark Context (sc) and Spark Session (spark)
    7. The Shell Spark Context Object (sc)
    8. The Shell Spark Session Object (spark)
    9. Loading Files
    10. Saving Files
    11. Summary
  6. Quick Intro to Jupyter Notebooks
    1. Python Dev Tools and REPLs
    2. IPython
    3. Jupyter
    4. Jupyter Operation Modes
    5. Basic Edit Mode Shortcuts
    6. Basic Command Mode Shortcuts
    7. Summary
  7. Data Visualization in Python using matplotlib
    1. Data Visualization
    2. What is matplotlib?
    3. Getting Started with matplotlib
    4. The matplotlib.pyplot.plot() Function
    5. The matplotlib.pyplot.scatter() Function
    6. Labels and Titles
    7. Styles
    8. The matplotlib.pyplot.bar() Function
    9. The matplotlib.pyplot.hist () Function
    10. The matplotlib.pyplot.pie () Function
    11. The Figure Object
    12. The matplotlib.pyplot.subplot() Function
    13. Selecting a Grid Cell
    14. Saving Figures to a File
    15. Summary
  8. Data Science and ML Algorithms with PySpark
    1. In-Class Discussion
    2. Types of Machine Learning
    3. Supervised vs Unsupervised Machine Learning
    4. Supervised Machine Learning Algorithms
    5. Classification (Supervised ML) Examples
    6. Unsupervised Machine Learning Algorithms
    7. Clustering (Unsupervised ML) Examples
    8. Choosing the Right Algorithm
    9. Terminology: Observations, Features, and Targets
    10. Representing Observations
    11. Terminology: Labels
    12. Terminology: Continuous and Categorical Features
    13. Continuous Features
    14. Categorical Features
    15. Common Distance Metrics
    16. The Euclidean Distance
    17. What is a Model
    18. Model Evaluation
    19. The Classification Error Rate
    20. Data Split for Training and Test Data Sets
    21. Data Splitting in PySpark
    22. Hold-Out Data
    23. Cross-Validation Technique
    24. Spark ML Overview
    25. DataFrame-based API is the Primary Spark ML API
    26. Estimators, Models, and Predictors
    27. Descriptive Statistics
    28. Data Visualization and EDA
    29. Correlations
    30. Hands-on Exercise
    31. Feature Engineering
    32. Scaling of the Features
    33. Feature Blending (Creating Synthetic Features)
    34. Hands-on Exercise
    35. The 'One-Hot' Encoding Scheme
    36. Example of 'One-Hot' Encoding Scheme
    37. Bias-Variance (Underfitting vs Overfitting) Trade-off
    38. The Modeling Error Factors
    39. One Way to Visualize Bias and Variance
    40. Underfitting vs Overfitting Visualization
    41. Balancing Off the Bias-Variance Ratio
    42. Linear Model Regularization
    43. ML Model Tuning Visually
    44. Linear Model Regularization in Spark
    45. Regularization, Take Two
    46. Dimensionality Reduction
    47. PCA and isomap
    48. The Advantages of Dimensionality Reduction
    49. Spark Dense and Sparse Vectors
    50. Labeled Point
    51. Python Example of Using the LabeledPoint Class
    52. The LIBSVM format
    53. LIBSVM in PySpark
    54. Example of Reading a File In LIBSVM Format
    55. Life-cycles of Machine Learning Development
    56. Regression Analysis
    57. Regression vs Correlation
    58. Regression vs Classification
    59. Simple Linear Regression Model
    60. Linear Regression Illustration
    61. Least-Squares Method (LSM)
    62. Gradient Descent Optimization
    63. Locally Weighted Linear Regression
    64. Regression Models in Excel
    65. Multiple Regression Analysis
    66. Evaluating Regression Model Accuracy
    67. The R>2
    68. Model Score
    69. The MSE Model Score
    70. Hands-on Exercise
    71. Linear Logistic (Logit) Regression
    72. Interpreting Logistic Regression Results
    73. Hands-on Exercise
    74. Naive Bayes Classifier (SL)
    75. Naive Bayesian Probabilistic Model in a Nutshell
    76. Bayes Formula
    77. Classification of Documents with Naive Bayes
    78. Hands-on Exercise
    79. Decision Trees
    80. Decision Tree Terminology
    81. Properties of Decision Trees
    82. Decision Tree Classification in the Context of Information Theory
    83. The Simplified Decision Tree Algorithm
    84. Using Decision Trees
    85. Random Forests
    86. Hands-On Exercise
    87. Support Vector Machines (SVMs)
    88. Hands-On Exercise
    89. Unsupervised Learning Type: Clustering
    90. k-Means Clustering (UL)
    91. k-Means Clustering in a Nutshell
    92. k-Means Characteristics
    93. Global vs Local Minimum Explained
    94. Hands-On Exercise
    95. Time-Series Analysis
    96. Decomposing Time-Series
    97. A Better Algorithm or More Data?
    98. Summary
Class Materials

Each student in our Live Online and our Onsite classes receives a comprehensive set of materials, including course notes and all the class examples.

Class Prerequisites

Experience in the following is required for this Spark class:

  • General knowledge of statistics and programming.
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  • Expert Instructors
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