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MS In Full Stack Artificial Intelligence And Machine Learning

Golden Gate University

MS In Full Stack Artificial Intelligence And Machine Learning

IT & Technology , Machine Learning

Admission Open

Enrolment closes on 22 Mar, 2023

Want to know more? enquire now

or call us on 9355801556

Duration: 24 Months

2 years in US (GGU)
01 Sep, 2023

Start Date

Admission Open

Enrolment closes on 22 Mar, 2023

Want to know more? enquire now

or call us on 9355801556

Enquire now

About this course

The MS in Full Stack Artificial Intelligence and Machine Learning is ideal for professionals who are interested in building on their existing skills to become leaders in the field of AI and ML. This program offers students an opportunity to learn a variety of foundational, computational, and applied topics in artificial intelligence and machine learning. The MS in Full Stack Artificial Intelligence and Machine Learning provides students with core knowledge in applied statistics, computational tools, DevOps, and ML Ops that can be applied immediately to real-world problems or used as a launching point for more in-depth and expansive knowledge.

With this course the learners will have the flexibility to do the entire MS program in India and get all the benefits of an MS degree from the USA.

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Get a deeper understanding of

Data Visualizations and Machine Learning
AI and Deep Learning Applications for NLP
Data Structures and Algorithms
Applying ML to Big Data Using Hadoop and Spark Ecosystem

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Key skills you’ll learn

Data Mining using SQL & Hadoop
Data Analytics
Statistical Analysis using R and Python
Data Visualization using Tableau and Power BI

Program Starts in 163 Days

Most suited for

IT Professionals: To have a better knowledge of analytics in addition to strong leadership qualities, Non-IT Professionals: Learn to convert business problems in analytics problems and then solve it.

Job Opportunities

Lead Data Scientist, AI Specialist, Data Scientist, Machine Learning Engineer, Algorithm Engineer, Computer Vision Engineer, Research Scientist

Benefits for All Students

On successful completion of this course earn the same U.S. degree as awarded on-campus

Opportunity to participate in global case study discussions

Participants can make use of Golden Gate University Digital Library whenever needed

Chance to become a part of GGU’s 70,000+ alumni network

Acquire skills to design, develop, and deploy an AI-based blueprint to solve domain-specific business problems

On successful completion of this course earn the same U.S. degree as awarded on-campus

Opportunity to participate in global case study discussions

Participants can make use of Golden Gate University Digital Library whenever needed

Chance to become a part of GGU’s 70,000+ alumni network

Acquire skills to design, develop, and deploy an AI-based blueprint to solve domain-specific business problems

Are You Eligible?

Education

Participants must have 16 years of Formal Education with Minimum 60% scores.
They must have completed their education with English as a medium of instruction from an Accredited university.
For working professionals with 2+ years of experience.

Syllabus at a Glance

• Understand the building blocks driving the global AI and ML revolution.

• Learn descriptive statistics, probability and probability distributions as applicable to Data Science/AI/ML

• Use sample data to infer about larger populations using Confidence Intervals and Hypothesis Testing.

• Learn Python programming to analyze and process data prior to building AI and ML models.

• Predict outcomes of various business problems using multiple input variables and data.

• Discover the relationships between variables and identify the variables that impact the outcome the most.

• Learn to transform data and engineer new features for improving predictive accuracy.

• Learn to transform the data and build powerful visuals, charts, reports, dashboards and storyboards using Python and Tableau.

• Gain insights from data and effectively communicate them to both technical and non-technical audiences for enabling business decision-making.

• Extract patterns from data using various supervised and unsupervised Machine Learning techniques.

• Evaluate errors in predictions and improve model performance.

• Find optimal solutions to business problems given a set of constraints using linear programming techniques and heuristic models.

• Learn to implement artificial neural networks and deep neural networks on both structured and textual data.

• Implement state-of-the-art techniques like transformer models to solve business problems in text classification, information extraction, sentiment analysis, text generation, entity extraction from documents, etc.

• Learn to store, manipulate and access data in repositories like relational and non-relational databases, data warehouses, data marts and data lakes to enable efficient data extraction and transformation for business needs and analytical modeling.

• Learn advanced programming and networking concepts using Python to solve enterprise-grade business problems.

• Learn relationships among data values and functions and operations that can be applied to them to efficiently organize, process, retrieve and store data for building different types of sophisticated applications.

• Apply programming best practices to write clear and unambiguous steps with well-defined inputs and outputs for solving problems.

• Learn to process images such as in medical imaging, defect classification, satellite imagery, object identification and classification, etc., and extract spatial patterns and features using Convolutional Neural Networks and other architectures.

• Learn advanced concepts of Transfer Learning, Data Augmentation, Object Detection and Image Segmentation, etc.

• Apply knowledge and concepts learned during the program to critical think about the problem, identify potential solutions, implement them using Python, and present results effectively using data visualizations and storytelling.

• Solve challenges posed by large volumes and variety of data that includes structured data, text, images, audio, video, etc., using Hadoop and Spark technology frameworks.

• Implement AI and ML models on such big data using Spark on the Hadoop cluster for solving large-scale enterprise problems.

• Learn concepts and tools for continuous software development, continuous integration, versioning, monitoring, system dependencies, etc.

• Understand the need to collaborate with various teams such as operations, software developers, IT and others to build and deploy ML and AI models in production environments.

• Understand various types of cloud computing services enterprises use to manage their data on cloud

• Learn Microsoft Azure global infrastructure and deploy AI and ML projects using Azure’s compute, basic services, storage, data factory, data bricks and synapse analytics.