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Expert practitioners facilitate our Machine Learning training
Instructor-Led | Role-Based | Tailored To Your Project
Today, Machine Learning training isn’t optional. Organizations are leveraging Machine Learning to gain a competitive advantage. A wide range of applications—from self-driving cars to e-commerce sites—rely on ML to look at past data in an effort to predict the future.
The goals? To serve customers better, drive efficiency and outflank competitors.
Stay ahead of the Machine Learning curve with help from DevelopIntelligence.
Our Machine Learning Training Team
Our experts are well-known industry practitioners. They have hands-on, in-the-trenches experience with the specific technologies they teach. When not training for DevelopIntelligence, they regularly author technical papers on the evolution of tools and languages in the field, speak at industry conferences, and contribute to open-source projects.
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Boost your competitiveness with Machine Learning training from DevelopIntelligence.
Learn the practical skills to plan, implement and manage an AI program.
Learn the foundational and practical knowledge of artificial intelligence.
Learn how to implement AI and ML techniques on Azure.
Learn to implement ML techniques for Natural Language comprehension, sentiment analysis, topic discovery, etc.
Learn how to use Spark internals for working with NoSQL databases as well debugging and troubleshooting.
Learn how to use Apache Spark as an alternative to traditional MapReduce processing.
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.
Learn about and build end-to-end SML pipelines for gaining actionable insights.
Learn best practices and techniques to optimize Spark Core and Spark SQL code.
Learn to build end-to-end data applications using Microsoft Azure and understand which tools are best suited to certain problems and use-cases.
Learn how to deploy, configure and manage Azure using Chef.
Learn how to configure, maintain and monitor Azure Virtual Machines and Virtual Networks.
Learn how to build, deploy, and maintain applications in Azure Cloud.
Learn how to develop a globally distributed application using Azure and serverless concepts
Learn how to pass the "Implementing Microsoft Azure Infrastructure Solutions" certification exam.
Learn how to implement AI and ML techniques on Azure.
Learn how to build, deploy, secure, scale, monitor and maintain applications in Azure Cloud.
Learn how to plan and manage cloud team resources more effectively.
Learn how to deploy and manage Azure in private data centers.
Learn the information necessary to pass the Developing Microsoft Azure Solutions Certification Exam.
Learn how to use Big Data technologies on the Azure cloud.
Learn about advanced programming techniques for OpenCL programming on Intel, AMD, and Nvidia architectures.
Learn about CUDA programming, profiling, and debugging techniques required to develop general purpose software applications for GPU hardware.
Learn the approach and practices to using GPU architectures for high performance computing
Learn to understand, design, implement and assess the impact of deep learning techniques for a diverse range of visual recognition tasks.
Learn Deep Learning concepts and popular tools.
Learn about deep reinforcement learning, what it is, how it works, and how you can apply it to real-world problems.
Learn how to build a Chatbot on Google Cloud and deploy it standalone as well as on Facebook Messenger.
Learn about Data Warehousing from an AWS perspective examining tools specifically underneath AWS.
Learn how to perform insightful and responsive data analysis at scale and delight the consumers of the analysis with effective data visualizations.
Learn to build systems on Google Cloud to store and process batch or streaming data.
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.
Learn to create and deploy software on Google Cloud to have secure and stable applications.
Learn about Google Cloud and its technical capabilities in the areas of IT infrastructure, operations, Big Data, and Machine Learning.
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.
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.
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.
Learn how to utilize advanced processes and practices for the platform to improve function and security.
Learn to use Google's BigQuery to explore and gain insights from large datasets.
Learn about the different cloud providers and what they offer.
Learn how to use Java to set up a project with the Google App Engine and integrate it with other Google Cloud services.
Learn how to develop a platform-based infrastructure using Google Cloud.
Learn the fundamental skills needed to manage large scale infrastructure with SaltStack on major cloud providers (AWS, Google Cloud and Azure).
Learn how to use the high-throughput, distributed, publish-subscribe messaging system Apache Kafka.
Learn to utilize Kafka Streams and identify where Kafka can be further incorporated into practice.
Learn to use Apache Kafka as a distributed messaging system.
Learn the knowledge and skills needed to utilize Data Ingestion and Processing using Kafka and Spark Streaming.
Learn advanced techniques for managing data and tuning models.
Learn how to become an expert at creating high-throughput, multithreaded, network oriented programs written in Python.
Learn intermediate data analysis techniques and how programming languages can be used to further analyze data.
Learn to design, implement and evaluate various recommendation engines.
Learn how to mature programming knowledge.
Learn how data can be gathered to improve the overall needs of the business.
Learn a working knowledge of graphics processing practices within Machine Learning using CUDA, PyCuda, OpenCL, Vulkan, and Tensorflow.
Learn various Machine Learning algorithms to evaluate and productize models.
Learn how to create basic programs using Python.
Learn how to develop simple programs using fundamental Python concepts.
Learn both R and Python programming languages by providing comparisons and recommendations between both.
Learn how to use MongoDB with Python.
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.
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.
Learn software engineering techniques using Python.
Learn how to improve analytical ability and the ability to provide data insights.
Learn how to identify the right context for analysis, perform the analysis and tell a story to drive action.
Learn advanced Tableau knowledge and skills.
Learn the fundamentals of Tableau.