Machine Learning Training

Introduction to Apache Kafka -

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

Introduction to Kafka Streams -

Learn to utilize Kafka Streams and identify where Kafka can be further incorporated into practice.

Kafka Internals -

Learn to use Apache Kafka as a distributed messaging system.

Real-Time Ingestion & Processing Using Kafka & Spark -

Learn the knowledge and skills needed to utilize Data Ingestion and Processing using Kafka and Spark Streaming.

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 -

Learn about and build end-to-end SML pipelines for gaining actionable insights.

Spark Optimization -

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

Artificial Intelligence for Product Managers -

Learn the practical skills to plan, implement and manage an AI program.

Introduction to Artificial Intelligence -

Learn the foundational and practical knowledge of artificial intelligence.

Introduction to Artificial Intelligence and Machine Learning in Azure -

Learn how to implement AI and ML techniques on Azure.

Machine Learning and Natural Language Processing -

Learn to implement ML techniques for Natural Language comprehension, sentiment analysis, topic discovery, etc.

Advanced Analytics in Azure -

Learn to build end-to-end data applications using Microsoft Azure and understand which tools are best suited to certain problems and use-cases.

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.

Developing Scalable Applications on Azure -

Learn how to develop a globally distributed application using Azure and serverless concepts

Implementing Microsoft Azure Infrastructure Solutions Certification -

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

Introduction to Artificial Intelligence and Machine Learning in Azure -

Learn how to implement AI and ML techniques on Azure.

Introduction to Azure for Architects -

This course introduces you to everything a cloud solutions architect needs to know to plan and design solutions for Microsoft Azure cloud platform.

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 Boot Camp -

Learn 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.

Advanced Programming of High-Performance GPU Architectures -

Learn about advanced programming techniques for OpenCL programming on Intel, AMD, and Nvidia architectures.

Introduction to High-Performance GPU Architectures -

Learn about 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

Introduction to SageMaker for Data Analysts -

Learn how to choose the right questions to ask and how to answer them with ML.

Machine Learning for Search -

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

Applied Computer Vision -

Learn to understand, design, implement and assess the impact of deep learning techniques for a diverse range of visual recognition tasks.

Deep Learning with TensorFlow and Keras -

Learn Deep Learning concepts and popular tools.

Working with Deep Reinforcement Learning -

Learn about deep reinforcement learning, what it is, how it works, and how you can apply it to real-world problems.

Building Chatbots Using Google Dialogflow -

Learn how to build a Chatbot on Google Cloud and deploy it standalone as well as on Facebook Messenger.

Data Warehousing on AWS -

Learn about Data Warehousing from an AWS perspective examining tools specifically underneath AWS.

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 -

Learn how to 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 about Google Cloud and its technical capabilities in the areas of IT 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 and practice identification and analysis of effective data features for predictive analytics with BigQuery ML and TensorFlow.

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 -

Learn how to 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 -

Learn how to utilize advanced processes and practices for the platform to improve function and security.

Introduction to BigQuery -

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

Introduction to Cloud Computing -

Learn about 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 -

Learn how to develop a platform-based infrastructure using Google Cloud.

SaltStack -

Learn the fundamental skills needed to manage large scale infrastructure with SaltStack on major cloud providers (AWS, Google Cloud and Azure).

Artificial Intelligence for Product Managers -

Learn the practical skills to plan, implement and manage an AI program.

Machine Learning and Natural Language Processing -

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.

Applied Data Science & Machine Learning -

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

Building a Recommendation Engine Using Python -

Learn to design, implement and evaluate various recommendation engines.

Intermediate Python -

Learn how to mature programming knowledge.

Introduction to Data Science & Machine Learning -

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

Introduction to Graphics Processing -

Learn a working knowledge 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 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 -

Learn both R and Python programming languages by providing comparisons and recommendations between both.

MongoDB for Python Developers -

Learn how to use MongoDB with Python.

Python for Data Scientists -

Learn how to use Python to explore and analyze data, run basic regression models, visualize data, and apply some basic machine learning models to data.

PyTorch -

Learn how to implement statistical and machine learning models using PyTorch and how to improve their performance based on an understanding of underlying mathematical principles.

Software Engineering in Python -

Learn software engineering techniques using Python.

PyTorch -

Learn how to implement statistical and machine learning models using PyTorch and how to improve their performance based on an understanding of underlying mathematical principles.

Working with R -

Learn how to use R as a tool to perform data science, machine learning or statistics on large data sets.

Introduction to SageMaker for Data Analysts -

Learn how to choose the right questions to ask and how to answer them with ML.

Advanced Tableau -

Learn how to improve analytical ability and the ability to provide data insights.

Data Storytelling -

Learn how to identify the right context for analysis, perform the analysis and tell a story to drive action.

Intermediate Tableau -

Learn advanced Tableau knowledge and skills.

Introduction to Tableau -

Learn the fundamentals of Tableau.

Deep Learning with TensorFlow and Keras -

Learn Deep Learning concepts and popular tools.

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 -

Learn how to 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 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 -

Learn how to 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.

Machine Learning and Natural Language Processing -

Learn to implement ML techniques for Natural Language comprehension, sentiment analysis, topic discovery, etc.