Intro to  AI & Data Science   

Artificial Intelligence (AI) is a field that has a long history but is still constantly and actively growing and changing.

The term artificial intelligence (AI) refers to a set of computer science techniques that enable systems to perform tasks normally requiring human intelligence, such as visual perception, speech recognition, decision-making and language translation. Machine learning and deep learning are branches of AI which, based on algorithms and powerful data analysis, enable computers to learn and adapt independently.

In this Program, you’ll learn the basics of modern AI from data collection all the way to solution design and make your final product online. This program provides you with:

• In-depth overview of Hadoop and MapReduce, the cornerstones of big data processing and data management systems.
• Principles of data Analytics techniques, building predictive models, design visualizing dashboards, creating reports and presenting results to audiences of all levels.
• Develop an understanding of the principles of machine learning and derive practical solutions using predictive analytics.
• Overview about Deep Learning, Computer vision and its applications in developing advanced Artificial Intelligence solution.

     BigData Management     

Today, organizations in every industry are being showered with imposing quantities of new information. Along with traditional sources, many more data channels and categories now exist. Collectively, these vastly larger information volumes and new assets are known as big data. 

Enterprises are using technologies such as MapReduce and Hadoop to extract value from big data. This course provides an in-depth overview of Hadoop and MapReduce, the cornerstones of big data processing. To crystalize the concepts behind Hadoop and MapReduce, write basic MapReduce programs, gain familiarity with advanced MapReduce programming practices, and utilize interfaces such as Pig and Hive to interact with Hadoop. You will also learn about real-world situations were MapReduce techniques can be used.

Module 1 Outline:

• Hadoop Introduction
• Hadoop Hands-on
• Introduction to ETL Tool (Pig)
• Introduction to Hive (DWH)
• Introduction to Map Reduce
• NoSQL Databases: MongoDB
•  Apache Spark Basics

 Data Analytics & Visualization  

This course introduces the basic goals and techniques in data science and analytics process with some theoretical foundations which include useful statistical concepts and data visualization to get insight about from the dataset. The course provides basic principles on important steps of the process which include data collecting, curating, analyzing, building predictive models and reporting and presenting results to audiences of all levels.

Python or R languages and statistical analysis techniques are introduced based on examples such as from marketing, business intelligence and decision support. Finally, you get introduced to state of the art Business Intelligence Data Visualization tools like PowerBI and Tableau and how to use it in storytelling.

Track 2 modules are:

• Introduction to Data Science 
• Stages of Data Science Project
• (R / Python) for Data Science
• Data Cleansing &Manipulation
• Exploring and Visualizing Data
• Data Visualization - Tableau
• Data Visualization - Power BI

  Machine Learning & Modeling  

In this course, you will develop an understanding of the principles of machine learning and derive practical solutions using predictive analytics. you'll learn about some of the most widely used and successful machine learning techniques. You'll have the opportunity to implement these algorithms yourself, and gain practice with them. You will also learn some of practical hands-on tricks and techniques (rarely discussed in textbooks) that help get learning algorithms to work well.

This is an "applied" machine learning Course, and we emphasize the intuitions and know-how needed to get learning algorithms to work in practice, rather than the mathematical derivations. At the end of course, you are going to work on large scale real-world project to emphasize all skills learned in all three courses.

Track 3 modules are:

• Classification
• Regression
• Tree and Ensemble Methods
• Clustering & Recommenders
• Artificial Neural Networks
• Predictive Modeling
• Applied Machine Learning
• Capstone Project

Deep Learning & Computer Vision

Machine learning were around us for some time and it help us to solve so many problems, but when it comes to use huge amount of data the tradition machine learning can’t help a lot. Due to the development in computation capabilities, statistical and mathematical algorithms to improve our prediction and modelling. Here, where Artificial Neural Networks (ANN) and it’s Deep Neural Networks (DNN) comes to picture.

Track 4 modules are:

• Introduction to Deep Learning
• TensorFlow & Keras for ANN
• Convolutional NN (CNN)
• Recurrent NN (RNN)
• Boltzmann & Autoencoders
• Intro. to Computer Vision
• Intro. to OpenCV, SSD & GANs
• Intro. to Object Detection
• Intro. to Facial Recognition
• Capstone Project