Machine Learning Training

Instructor-led Machine Learning Courses

Customized, role-based, expert-led Machine Learning Training

Machine Learning, a subfield of Artificial Intelligence (AI), is about training computers to find patterns in data in order to make predictions or decisions. Programming languages such as Python, Scala, R, Julia, and Java, as well as frameworks such as TensorFlow, Keras, and Scikit-learn are commonly used for Machine Learning tasks such as image recognition and natural language processing (NLP).

When they aren’t training for us, our machine learning instructors regularly author blog posts on the evolution of the field, speak at industry conferences and meetups, and contribute to open source projects.

A few of our most popular courses are the Introduction to Machine Learning and Advanced Machine Learning courses.

Developer Academy™

For Organizations

Executing a digital transformation or having trouble filling your tech talent pipeline?

Upskilling & Reskilling

For Tech Teams

Need to stay ahead of technology shifts and upskill your current workforce on the latest technologies?

New Hire Development

for Talent Acquisition

Is your engineering new hire experience encouraging retention or attrition?

Learning Strategy

For Tech Learning

Looking for in-the-trenches experiences to level-up your internal learning and development offerings?

AI Courses

AI for Product Managers -

This course will provide Product Managers with the knowledge and practical skills to plan, implement and manage an AI program.

Building Chatbots Using Google Dialogflow -

Build Chatbot on Google Cloud and deploy it standalone as well as on Facebook Messenger.

Introduction to AI and Machine Learning in Azure -

Learn to implement AI and ML techniques on Azure.

Introduction to Artificial Intelligence -

This course is designed to provide students with foundational and practical knowledge of artificial intelligence.

Apache Spark Courses

Advanced Spark -

Learn how to use Spark internals for working with NoSQL databases as well debugging and troubleshooting.

Introduction to Apache Spark -

Learn how to use Apache Spark as an alternative to traditional MapReduce processing.

Introduction to Apache Spark in Production -

Learn about the architecture and internals of Spark, a fast and general engine for big data processing with built-in modules for streaming, SQL, machine learning, and graph processing.

Scalable Machine Learning -

Understand and build end-to-end Scalable Machine Learning Pipelines for gaining actionable insights.

Spark Optimization -

Learn best practices and techniques to optimize Spark Core and Spark SQL code.

Azure Courses

Azure for DevOps Engineers -

Learn how to deploy, configure and manage Azure using Chef.

Azure for System Administrators -

Learn how to configure, maintain and monitor Azure Virtual Machines and Virtual Networks.

Azure in a Day -

Learn how to build, deploy, and maintain applications in Azure Cloud.

DevSecOps -

Describe the philosophy, models, goals, practices and tools being used to move to a DevSecOps culture.

Implementing Microsoft Azure Infrastructure Solutions Certification -

Learn how to pass the "Implementing Microsoft Azure Infrastructure Solutions" certification exam.

Introduction to AI and Machine Learning in Azure -

Learn to implement AI and ML techniques on Azure.

Introduction to Azure for Developers -

Learn how to build, deploy, secure, scale, monitor and maintain applications in Azure Cloud.

Introduction to Azure for Managers -

Learn how to plan and manage cloud team resources more effectively.

Introduction to the Azure Stack and VM Managers -

Learn how to deploy and manage Azure in private data centers.

Microsoft Azure Certification Fast Track -

Learn about the information necessary to pass the Developing Microsoft Azure Solutions Certification Exam

Working with Big Data on Azure -

Learn how to use Big Data technologies on the Azure cloud.

CUDA Courses

Advanced Programming of High-Performance GPU Architectures -

Examine advanced programming techniques for OpenCL programming on Intel, AMD, and Nvidia architectures

Introduction to Graphics Processing -

Develop a working knowledge through lecture and labs of graphics processing practices within Machine Learning using CUDA, PyCuda, OpenCL, Vulkan and Tensorflow.

Introduction to High-Performance GPU Architectures -

Introduce CUDA programming, profiling, and debugging techniques required to develop general purpose software applications for GPU hardware

Overview of High-Performance GPU Architectures -

Learn the approach and practices to using GPU architectures for high performance computing

Deep Learning Courses

Applied Computer Vision -

Study and implement deep learning models and learning algorithms for visual recognition.

Deep Learning with TensorFlow and Keras -

Examine Deep Learning concepts and popular tools.

Working with Deep Reinforcement Learning -

This course will provide an introduction to deep reinforcement learning, what it is, how it works and how you can apply it to real-world problems.

Google Cloud Courses

Google Cloud for Data Analysts -

Learn how to perform insightful and responsive data analysis at scale and delight the consumers of the analysis with effective data visualizations.

Google Cloud for Data Engineers -

Learn to build systems on Google Cloud to store and process batch or streaming data.

Google Cloud for Data Scientists -

Create and deploy high-performance data science and machine learning systems on Google Cloud for regression and classification use cases leveraging both structured and unstructured datasets.

Google Cloud for Developers -

Learn to create and deploy software on Google Cloud to have secure and stable applications.

Google Cloud for Managers -

Learn a technical overview of Google Cloud and its capabilities in the areas of information technology infrastructure, operations, big data, and machine learning.

Intermediate Google Cloud For Data Analysts -

Learn how to analyze large scale, distributed, and real-time datasets with MapReduce and Apache Beam based capabilities of Google Cloud.

Intermediate Google Cloud for Data Engineers -

Learn how to use data engineering on Google Cloud to build specialized data pipelines for large scale streaming data analytics, machine learning, and recommendation systems.

Intermediate Google Cloud For Data Scientists -

Implement statistical and machine learning models using TensorFlow, for example for recommendation engines, and how to improve their performance based on the students’ understanding of underlying mathematical principles.

Intermediate Google Cloud for Developers -

Utilize advanced processes and practices for the platform to improve function and security.

Introduction to BigQuery -

Empower developers to use Google's BigQuery to explore and gain insights from large datasets.

Introduction to Cloud Computing -

This course provides an overview of the different cloud providers and what they offer.

Introduction to the Google App Engine -

Learn how to use Java to set up a project with the Google App Engine and integrate it with other Google Cloud services.

Moving to a Hybrid Cloud for Managers Adding Google Cloud -

This course explores the development of a platform-based infrastructure using Google Cloud.

Kafka Courses

Introduction to Apache Kafka -

Learn how to use the high-throughput, distributed, publish-subscribe messaging system Apache Kafka.

Introduction to Kafka Streams -

Upon completion, students will be able to utilize Kafka Streams and identify where Kafka can be further incorporated into their practice.

Real-Time Ingestion & Processing Using Kafka & Spark -

This course will provide the knowledge and skills needed to utilize Data Ingestion and Processing using Kafka and Spark Streaming.

Machine Learning Courses

Advanced Machine Learning -

Learn advanced techniques for managing data and tuning models.

Advanced Tableau -

Improve analytical ability and the ability to provide data insights.

Applied Data Science & Machine Learning -

Explore intermediate data analysis techniques and how programming languages can be used to further analyze data.

Building Chatbots Using Google Dialogflow -

Build Chatbot on Google Cloud and deploy it standalone as well as on Facebook Messenger.

Data Storytelling -

Identify the right context for analysis, perform the analysis and tell a story to drive action.

Google Cloud for Data Engineers -

Learn to build systems on Google Cloud to store and process batch or streaming data.

Google Cloud for Data Scientists -

Create and deploy high-performance data science and machine learning systems on Google Cloud for regression and classification use cases leveraging both structured and unstructured datasets.

Intermediate Google Cloud For Data Analysts -

Learn how to analyze large scale, distributed, and real-time datasets with MapReduce and Apache Beam based capabilities of Google Cloud.

Intermediate Google Cloud for Data Engineers -

Learn how to use data engineering on Google Cloud to build specialized data pipelines for large scale streaming data analytics, machine learning, and recommendation systems.

Intermediate Google Cloud For Data Scientists -

Intermediate Tableau -

Provide students with advanced Tableau knowledge and skills.

Introduction to AI and Machine Learning in Azure -

Learn to implement AI and ML techniques on Azure.

Introduction to Data Science & Machine Learning -

Examine how data can be gathered to improve the overall needs of the business.

Introduction to Graphics Processing -

Develop a working knowledge through lecture and labs of graphics processing practices within Machine Learning using CUDA, PyCuda, OpenCL, Vulkan and Tensorflow.

Introduction to Machine Learning -

Learn various Machine Learning algorithms to evaluate and productize models.

Introduction to R & Python -

Introduce developers to both programming languages providing comparisons and recommendations between the two languages.

Introduction to SageMaker for Data Analysts -

Teach developers how to choose the right questions to ask and how to answer them with Machine Learning.

Introduction to Tableau -

Promote familiarity with Tableau

Machine Learning and Natural Language -

Learn to implement ML techniques for natural language comprehension, sentiment analysis, topic discovery, etc.

Machine Learning for Search -

Learn the knowledge and use cases for software engineers to transition to Machine Learning for Search.

Real-Time Ingestion & Processing Using Kafka & Spark -

This course will provide the knowledge and skills needed to utilize Data Ingestion and Processing using Kafka and Spark Streaming.

Scalable Machine Learning -

Understand and build end-to-end Scalable Machine Learning Pipelines for gaining actionable insights.

NLP Courses

Machine Learning and Natural Language -

Learn to implement ML techniques for natural language comprehension, sentiment analysis, topic discovery, etc.

Python Courses

Advanced Machine Learning -

Learn advanced techniques for managing data and tuning models.

Advanced Python -

Learn how to become an expert at creating high-throughput, multithreaded, network oriented programs written in Python.

Building a Recommendation Engine Using Python -

The purpose is to understand, design, implement and evaluate various recommendation engines.

Intermediate Python -

Learn how to train new Python developers to mature their programming knowledge.

Intro to Programming -

Learn how to create basic programs using Python.

Introduction to Python -

Learn how to develop simple programs using fundamental Python concepts.

Introduction to R & Python -

Introduce developers to both programming languages providing comparisons and recommendations between the two languages.

Machine Learning for Search -

Learn the knowledge and use cases for software engineers to transition to Machine Learning for Search.

MongoDB for Python Developers -

Learn how to use MongoDB with Python.

Presenting Visual Data -

By the end of this course, students should be able to evaluate a visuals request, choose the most appropriate visualization tool, and create effective visuals to suit the needs of the request.

Python for Data Scientists -

Obtain a working knowledge of how to use Python to explore and analyze data, run basic regression models, visualize data, and apply some basic machine learning models to data.