Videos

Introduction to Jenkins for DevOps LiveLessons (Video Training)

Publisher: Pearson

Release Date: 2020

Introduction to Jenkins for DevOps LiveLessons (Video Training)

Abstract

Cloud technology advancement has changed the face of the tech world, with more emphasis on continuous integration and delivery. Learn how to deploy, configure, and take advantage of Jenkins for Continuous Integration and Continuous Delivery (CI/CD) and pipeline-like workflows.

Data Engineering with Python and AWS Lambda LiveLessons

Publisher: Pearson

Release Date: 2019

Data Engineering with Python and AWS Lambda LiveLessons

Abstract

Data Engineering with Python and AWS Lambda LiveLessons shows users how to build complete and powerful data engineering pipelines in the same language that Data Scientists use to build Machine Learning models. By embracing serverless data engineering in Python, you can build highly scalable distributed systems on the back of the AWS backplane. Users learn to think in the new paradigm of serverless, which means to embrace events and event-driven programs that replace expensive and complicated servers.

Pragmatic AI and Machine Learning Core Principles

Publisher: Pearson

Release Date: 2019

Pragmatic AI and Machine Learning Core Principles

Abstract

Machine Learning is the scientific study of models and algorithms that train a computer to make predictions without explicit instruction. Machine Learning is a subset of Artificial Intelligence, which can be defined as computers that mimic human problem-solving. This video demonstrates the core principles of Machine Learning and AI, including supervised Machine Learning, unsupervised Machine Learning, neural networks, and social network theory.

Building AI Applications on Google Cloud Platform

Publisher: Pearson

Release Date: 2019

Building AI Applications on Google Cloud Platform

Abstract

Building AI Applications on Google Cloud Platform LiveLessons covers programming components essential to the development of AI and Analytics applications. The focus is on building real-world software engineering applications on the Google Cloud Platform. Several emerging technologies are used to demonstrate the process, including AutoML and Google BigQuery. The Python language is used throughout the course, as Python is becoming the de facto standard language for AI application development in the cloud.

AWS Certified Big Data - Specialty Complete Video Course and Practice Test Video Training

Publisher: Pearson

Release Date: 2019

AWS Certified Big Data

Abstract

AWS leads the world in cloud computing and big data. This course offers the complete package to help practitioners master the core skills and competencies needed to build successful, high-value big data applications, with a clear path toward passing the certification exam AWS Certified Big Data - Specialty. This course provides a solid foundation in all areas required to pass the AWS Certified Big Data Specialty Exam–including Collection, Storage, Processing, Analysis, Visualization, and Data Security. In addition, multiple quizzes and a practice exam prepare the student for the formal Certification Exam administered by AWS.

Python for Data Science Complete Video Course

Publisher: Pearson

Release Date: 2019

Python for Data Science

Abstract

Notebook-based Data Science programming in Python is the emerging standard but there is a dearth of quality training material available for beginners. This 9-hour video, complete with interactive quizzes, provides foundational training on the Python language for the novice or beginner programmer looking to start in the Data Science field. The video serves as the 100-level course for a Data Science undergraduate or graduate program.

The course has been designed around Colab notebook-based learning. Students would be able to run every exercise shown in the videos. The material focuses on a smaller, easier subset of Python that is needed just for Data Science coding.

AWS Certified Machine Learning-Specialty (ML-S)

Publisher: Pearson

Release Date: 2019

AWS Certified Machine Learning-Specialty (ML-S)

Abstract

This course covers the essentials of Machine Learning on AWS and prepares a candidate to sit for the AWS Machine Learning-Specialty (ML-S) Certification exam. Four main categories are covered: Data Engineering, EDA (Exploratory Data Analysis), Modeling, and Operations. Description This 7+ hour Complete Video Course is fully geared toward the AWS Machine Learning-Specialty (ML-S) Certification exam. The course offers a modular lesson and sublesson approach, with a mix of screencasting and headshot treatment.

  • Data Engineering instruction covers the ingestion, cleaning, and maintenance of data on AWS.
  • Exploratory Data Analysis covers topics including data visualization, descriptive statistics, and dimension reduction and includes information on relevant AWS services.
  • Machine Learning Modeling covers topics including feature engineering, performance metrics, overfitting, and algorithm selection.
  • Operations covers deploying models, A/B testing, using AI services versus training your own model, and proper cost utilization.

Essential Machine Learning and A.I. with Python and Jupyter Notebook LiveLessons

Publisher: Pearson

Release Date: 2018

AWS Certified Cloud Practitioner 2020-Real World & Pragmatic

Abstract

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

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.

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