The Artificial Intelligence and Machine Learning Specialist (AIMLS) program introduces students to the theoretical basis and practical applications of machine learning and data science. The use of machine learning technologies in data science is seeing exploding growth across many industries, performing a wide range of functions such as forecasting, optimization, and complex decision-making. Students will learn how to apply supervised and supervised learning in processing and analyzing data in their professional settings, as well as topics such as statistics, data visualization, and deep learning. This program is suitable for business professionals and managers who work directly or indirectly with any type of data and wish to stay competitive by leveraging the power of machine learning and data science.

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NEXT START DATES:
- September 20th, 2021

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Introduction to Python for Data Science

The objective of this course is to introduce the course participant to the Python programming language for data science applications. By the end of this course, participants will be able to set up Python and have learnt the fundamental concepts required to derive actionable insights from data. Concepts such as data types, data analysis, data visualizations, database connectivity and file manipulation will be covered in detail. In addition, Python development using Jupyter Notebooks and Google Colab will also be explored. There will be lots of opportunities to apply the concepts learned in the course in the form of assignments and a group project.

Statistics for Data Science

This course covers what you need to know about probability and statistics to succeed in business and the data science field. This is a very practical course and will go over both theory and implementations of statistics to real-world problems. Each section has multiple example problems and some sections have homework at the end.

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Storytelling Using Data

The objective of this course is to enable the participant to tell more effective stories using data. The foundational principles of narrating impactful stories, supported with the appropriate visualizations, will be discussed in detail. This course will cover the core phases of a data analytics project including clarification of the business outcome, data understanding, visualization, deployment, and presentation. Practical examples and mock-ups will be provided using some of the popular tools such as Tableau, Power BI, Matplotlib (Python) and Ggplot2 (R). There will be lots of opportunities to apply the concepts learned in the course in the form of assignments and a group project.

Artificial Intelligence in Business

This course presents the current landscape of AI technologies, their core branches, and their applications in business. It provides an overview of the history and evolution of AI and the multiple paradigm shifts making AI today.  An overview of the contribution of AI to markets and industries. The current advancements of AI in business domains and the spectrum of AI as a Service available in the cloud today.

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Introduction to Machine Learning

This course introduces the fundamentals of machine learning and data science. The theory part of the course is designed to explain the lifecycle of real-life machine learning projects with the help of established CRISM-DM (Cross-industry standard process for data mining) framework along with the underlying concepts that are fundamental to machine learning and deep learning such as types of machine learning, model training and selection, and evaluation. The lab section is designed to give you an opportunity to implement machine learning techniques learned throughout the course on some real-world datasets with an emphasis on practical application in business.

Unsupervised Learning: The Power of Unlabeled Data

This module introduces the fundamentals of unsupervised learning and discusses its applications in clustering, dimensionality reduction, and anomaly detection.

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Introduction to Deep Learning

This course introduces the fundamentals of artificial neural networks and deep learning. The course consists of two parts: theory of deep learning using slides as well as hands-on lab section for deep learning using Jupyter notebooks. The theory focuses on the conceptual understanding of deep learning as opposed to complex mathematical equations. The lab section covers several areas of deep learning that a data scientist is supposed to be familiar with. Upon completion of this course, one should be ready to start building predictive models at basic and fairly advanced levels using the Tensorflow/Keras framework in Python.

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