Machine Learning Training

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 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.

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 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.

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.

Advanced Machine Learning -

Learn advanced techniques for managing data and tuning models.

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.

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.

Machine Learning and Natural Language -

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

Scalable Machine Learning -

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

Machine Learning and Natural Language -

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

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.

MongoDB for Python Developers -

Learn how to use MongoDB with Python.

Python Boot Camp -

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.

Introduction to R -

Learn how to leverage R to perform data analytics.

Introduction to R & Python -

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

Working with R -

Learn how to leverage R to perform data analytics.