Essential Machine Learning and AI with Python and Jupyter Notebook

Video description

8+ Hours of Video Instruction

Learn just the essentials of Python-based Machine Learning on AWS and Google Cloud Platform with Jupyter Notebook.


This 8-hour LiveLesson video course shows how AWS and Google Cloud Platform can be used to solve real-world business problems in Machine Learning and AI. Noah Gift covers how to get started with Python via Jupyter Notebook, and then proceeds to dive into nuts and bolts of Data Science libraries in Python, including Pandas, Seaborn, scikit-learn, and TensorFlow.

EDA, or exploratory data analysis, is at the heart of the Machine Learning; therefore, this series also highlights how to perform EDA in Python and Jupyter Notebook. Software engineering fundamentals tie the series together, with key instruction on linting, testing, command-line tools, data engineering APIs, and more.

The supporting code for this LiveLesson is located at

About the Instructor

Noah Gift is lecturer and consultant at UC Davis Graduate School of Management in the MSBA program. He is teaching graduate machine learning and consulting on Machine Learning and Cloud Architecture for students and faculty. He has published close to 100 technical publications, including two books on subjects ranging from Cloud Machine Learning to DevOps. He is also a certified AWS Solutions Architect and an SME (Subject Matter Expert for Machine Learning for AWS). He has an MBA from UC Davis, an MS in Computer Information Systems from Cal State Los Angeles, and a BS in Nutritional Science from Cal Poly San Luis Obispo.

Professionally, Noah has approximately 20 years of experience of programming in Python and is a member of the Python Software Foundation. He has worked in roles ranging from CTO, General Manager, Consulting CTO and Cloud Architect. This experience has been with a wide variety of companies including ABC, Caltech, Sony Imageworks, Disney Feature Animation, Weta Digital, AT&T, Turner Studios, and Linden Lab. In the past ten years, he has been responsible for shipping many new products at multiple companies that generated millions of dollars of revenue and had global scale. He is the founder of Pragmatic AI Labs, a training, consulting, and AI/ML product company that specializes in cloud native Machine Learning and AI Solutions.

Skill Level

  • Beginner
What You Will Learn
  • Introduces Data Science concepts and Python fundamentals for Machine Learning
  • Teaches how to develop a Data Engineering API with Flask and Pandas
  • Walks through EDA (exploratory data analysis)
  • Explains Python and AWS
  • Covers Python and Google Cloud Platform

Who Should Take This Course
  • Business and analytics professionals with some SQL experience looking to move to the next generation of Data Science
  • Junior Data Scientists looking to expand into cloud-based Machine Learning concepts on AWS and GCP
  • Software developers who want to understand how to get more deeply involved in the Data Science movement
  • Technical leaders who want to understand Machine Learning and AI in Python to effectively manage teams that perform these actions
Course Requirements
  • Beginner programming skills in any language
  • Beginner command-line skills on Unix or Linux
  • Beginner understanding of Cloud Technology
About Pearson Video Training

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Table of contents

  1. Introduction
    1. Essential Machine Learning and AI with Python and Jupyter Notebook: Introduction
  2. Lesson 1: Introducing Data Science Coding with Python Fundamentals
    1. Learning objectives
    2. 1.1 Use IPython, Jupyter, and Python REPL
    3. 1.2 Write procedural statements
    4. 1.3 Use strings and string formatting
    5. 1.4 Use numbers and arithmetic operations
    6. 1.5 Interact with data structures
    7. 1.6 Write and run scripts
    8. 1.7 Summary
  3. Lesson 2: Writing and Applying Functions
    1. Learning objectives
    2. 2.1 Write functions
    3. 2.2 Utilize functional programming concepts
    4. 2.3 Utilize lazy evaluated functions
    5. 2.4 Utilize decorators
    6. 2.5 Make classes behave like functions
    7. 2.6 Apply a function to a Pandas DataFrame
    8. 2.7 Use Python lambdas
    9. 2.8 Summary
  4. Lesson 3: Using Python Control Structures
    1. Learning objectives
    2. 3.1 Create loops
    3. 3.2 Use if/else/break/continue/pass statements
    4. 3.3 Understand try/except
    5. 3.4 Understand generator expressions
    6. 3.5 Understand list comprehensions
    7. 3.6 Understand sorting
    8. 3.7 Understand Python regular expressions
    9. 3.8 Summary
  5. Lesson 4: Writing, Using, and Deploying Libraries in Python
    1. Learning objectives
    2. 4.1 Write and use libraries in Python
    3. 4.2 Use pipenv, pip, virtualenv and conda
    4. 4.3 Deploy Python code to production
    5. 4.4 Summary
  6. Lesson 5: Understanding Python Classes
    1. Learning objectives
    2. 5.1 Understand differences between classes and functions
    3. 5.2 Make and interact with simple objects
    4. 5.3 Understand class inheritance
    5. 5.4 Interact with special class methods
    6. 5.5 Create metaclasses
    7. 5.6 Summary
  7. Lesson 6: IO Operations in Python and Pandas
    1. Learning objectives
    2. 6.1 Use write file operations
    3. 6.2 Use read file operations
    4. 6.3 Use serialization techniques
    5. 6.4 Use Pandas DataFrames
    6. 6.5 Use Google Sheets with Pandas DataFrames
    7. 6.6 Use concurrency methods in Python
    8. 6.7 Summary
  8. Lesson 7: Learning Software Carpentry
    1. Learning objectives
    2. 7.1 Build a new Data Science Github project layout
    3. 7.2 Use git and Github to manage changes
    4. 7.3 Use CircleCI and AWS Code Build to build and test a project sourced from Github
    5. 7.4 Use static analysis and testing tools: pylint, pytest, and coverage
    6. 7.5 Test Jupyter Notebooks
    7. 7.6 Summary
  9. Lesson 8: Creating a Data Engineering API with Flask and Pandas
    1. Learning objectives
    2. 8.1 Make a project layout
    3. 8.2 Lay out a Makefile for a project
    4. 8.3 Create a command-line tool for Pandas aggregation
    5. 8.4 Make plugins to pass to Pandas
    6. 8.5 Write the Flask API
    7. 8.6 Integrate Swagger documentation
    8. 8.7 Benchmark Python projects
    9. 8.8 Integrate testing and linting
    10. 8.9 Summary
  10. Lesson 9: Walking through Social Power NBA EDA and ML Project
    1. Learning objectives
    2. 9.1 Data Collection of Social Media Data
    3. 9.2 Import and merge DataFrames in Pandas
    4. 9.3 Understand correlation heatmaps and pairplots
    5. 9.4 Use linear regression in Python
    6. 9.5 Use ggplot in Python
    7. 9.6 Use k-means clustering
    8. 9.7 Use PCA with scikit-learn
    9. 9.8 Use ML classification prediction with scikit-learn
    10. 9.9 Use ML regression prediction with scikit-learn
    11. 9.10 Use Plotly for interactive data visualization
    12. 9.11 Summary
  11. Lesson 10: Understanding Intermediate Machine Learning
    1. Learning objectives
    2. 10.1 Overview of AI, Machine Learning and Deep Learning
    3. 10.2 Big Data
    4. 10.3 Working with recommendation systems
    5. 10.4 Summary
  12. Lesson 11: Python based AWS Cloud ML and AI Pipelines
    1. Learning objectives
    2. 11.1 Use AWS Web Services
    3. 11.2 Use Boto
    4. 11.3 Use AWS Lambda development with Chalice
    5. 11.4 Use AWS DynamoDB
    6. 11.5 Use AWS Step functions
    7. 11.6 Use AWS Batch for ML jobs
    8. 11.7 Use AWS Sagemaker
    9. 11.8 Use AWS Comprehend for NLP
    10. 11.9 Use AWS Rekognition API
    11. 11.10 Summary
  13. Lesson 12: Python based Google Compute Platform ML and AI Pipelines
    1. Learning objectives
    2. 12.1 Perform Colaboratory basics
    3. 12.2 Use Advanced Colab Features
    4. 12.3 Perform Datalab basics
    5. 12.4 Use TPUS for deep learning
    6. 12.5 Use Google Big Query
    7. 12.6 Use Google Machine Learning Services
    8. 12.7 Use Google Sentiment Analysis API
    9. 12.8 Use Google Computer Vision API
    10. 12.9 Summary
  14. Lesson 13: Creating Command-line Machine Learning Tools
    1. Learning objectives
    2. 13.1 Walk through Spot Price Machine Learning
    3. 13.2 Walk through DevML
    4. 13.3 Summary
  15. Lesson 14: Datascience: Case Study Social Power in the NBA
    1. 14.1 Datascience: Case Study Social Power in the NBA
  16. Summary
    1. Essential Machine Learning and AI with Python and Jupyter Notebook: Summary

Product information

  • Title: Essential Machine Learning and AI with Python and Jupyter Notebook
  • Author(s): Noah Gift
  • Release date: August 2018
  • Publisher(s): Pearson
  • ISBN: 0135261112