Providing Technology Training and Mentoring For Modern Technology Adoption
To stay competitive, organizations have started adopting new approaches to data processing and analysis. For example, data scientists are turning to Apache Spark for processing massive amounts of data using Apache Spark’s distributed compute capability and its built-in machine learning library.
This intensive Apache Spark training course provides an overview of data science algorithms as well as the theoretical and technical aspects of using the Apache Spark platform for Machine Learning. This training course is supplemented by a variety of hands-on labs that help attendees reinforce their theoretical knowledge of the learned material.
Many data-centric organizations rely on manufacturing processes to build their products. They’ll use a deterministic process where you “plan the work and then work the plan.” This mindset works great for products with linear development. But artificial intelligence products seldom follow this direct path. Teams aren’t manufacturing AI products as much as they’re discovering them. To effectively create new AI products your team needs to understand the technology and be comfortable running small experiments. This course will go over artificial intelligence concepts and then show you specific practices such as machine learning and neural networks. Then you’ll see roles and processes that encourage your team to use a less deterministic approach to discovering great AI products.
Machine Learning is the process of discovering interesting knowledge from large amounts of data. It is an interdisciplinary field with contributions from many areas, such as statistics, artificial intelligence, information retrieval, pattern recognition and bioinformatics. Machine learning for predictive analytics is widely used in many domains, such as retail, finance, telecommunication and social media.
This course provides an overview of various machine learning techniques with examples of how they are used in various organizations such as retail, finance, biotechnology and social media. Case studies are used to allow participants to work through several machine learning issues using the techniques described and to recognize opportunities within their organization.
Note: This course uses a visually oriented, open source software package to process the data. The class is not intended to be a programming class. Instead, the software is used to examine the impact of different data mining decisions.
Practical Machine Learning with Apache Spark
Build a customer support chat Bot that use artificial intelligence from the Microsoft Azure platform including language understanding and pre-built AI functionality in the Azure Cognitive Services.
This course will provide you with a blueprint for how to build an application that generates predictions using a deep learning model. From there, you can continue to improve our example model—either by adding more data, computing more features, or changing its architecture—continuously increasing its prediction accuracy, or create a completely new model, changing the core components of the application as you see fit.
This is an in depth hands on workshop based course that teaches the fundamentals of Machine Learning (ML) and Neural Networks (NN). The course is designed for professionals who will be asked to solve various business problems using ML. Workshop projects are designed to be realistic. They follow a complete lifecycle:- Collect and clean up data.- Design a model- Train the model with data- Start doing prediction
We primarily use Keras for the workshops. In some cases we dive lower and code using Tensorflow API. This done to gain better understanding of the concepts.
In response to Google’s Kotlin-first policy for Android, every team doing Android development needs to consider Kotlin. This three-day course is an in-depth introduction to the Kotlin language. This course is appropriate for developers who already know Java and Android or those wishing to learn the Kotlin Language. This course does not teach Android development fundamentals focusing rather on the Kotlin Language.
Data engineering is a software engineering practice with focus on design, development, and the productionizing of data processing systems. It includes all the practical aspects of data acquisition, transfer, transformation, and storage on-prem or in the cloud.This intensive hands-on training course teaches the students how to apply Python to the practical aspects of data engineering and introduces the students to the popular Python libraries used in the field, including NumPy, pandas, Matplotlib, scikit-learn, and Apache Spark.