Machine Learning Training
What if Machine Learning training is your key to remaining competitive?
What if Machine Learning training is your key to remaining competitive?
Get your team started on a custom learning journey today!
Our Boulder, CO-based learning experts are ready to help!
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 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.
Chat with one of our tech experts to create a custom on-site or online training program.
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.
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.
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.
Azure for System Administrators -
Learn how to configure, maintain and monitor Azure Virtual Machines and Virtual Networks.
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 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.
Learn the knowledge and use cases for software engineers to transition to Machine Learning for Search.
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.
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.
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.
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.
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.
Learn the fundamental skills needed to manage large scale infrastructure with SaltStack on major cloud providers (AWS, Google Cloud and Azure).
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.
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.
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.
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.
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.
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.
MongoDB for Python Developers -
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.
Software Engineering in Python -
Learn software engineering techniques using Python.
Learn how to use R as a tool to perform data science, machine learning or statistics on large data sets.
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.
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Fortune 500 companies trust us to deliver high-performance, hyper-focused learning programs for onboarding, upskilling and reskilling tech talent.
We work with L&D experts across many industries and domains. You get the benefit of this ever-expanding knowledge base when you partner with DI.
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