Artificial Intelligence Engineer Masters Program Option 1
Collaborative Masters in Artificial Intelligence with IBM Developed in partnership with IBM, this Masters in Artificial Intelligence program uses a blended learning approach that transforms students into AI and Data Science specialists. IBM, headquartered in Armonk, New York, is a leader in cognitive services and cloud solutions, providing an array of technology and consulting services. As a pioneer in AI and Machine Learning, IBM’s collaboration with this program guarantees that you are equipped for a career in Artificial Intelligence and Data Analytics
Integrate the IBM Advantage into your education.Get access to IBM hackathons, masterclasses and ask-me-anything sessions.
3 Capstone and 12 ProjectsIndustry-specific projects from industry leaders.
Interactive Learning Experience8X more live engagement in live online courses taught by experienced trainers and industry professionals.
Learn from the BestLevelUp sessions
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About Masters in Artificial Intelligence
the Masters in Artificial Intelligence program introduces a blended learning approach that empowers students to become specialists in the domains of AI and Data Science. Based in Armonk, New York, IBM is a prominent player in cognitive services and integrated cloud solutions, offering an array of technology and consulting solutions. As a leading figure in AI and Machine Learning technology, IBM’s association with this program ensures students are prepared for a career in Artificial Intelligence and Data Analytics.
What to Expect from the Masters in Artificial Intelligence by IBM Upon successfully completing the program, you will receive certificates from both IBM and, validating your expertise in AI. Additionally, the program offers several valuable perks, including:
• Masterclasses conducted by IBM experts
• Interactive Ask Me Anything sessions featuring IBM leadership
• Innovative hackathons organized by IBM
• An industry-recognized certificate of course completion from
Upon completing this program, you will acquire the following skills: • Comprehend the concepts, purpose, scope, stages, implementations, and impacts of Artificial Intelligence. • Develop real-world projects, games, prediction models, constraint satisfaction problems, experiential systems, probabilistic models, and decision-making skills using artificial intelligence tools and models. • Learn fundamental programming aspects such as data types, tuples, lists, arrays, basic operators, and functions. • Build and deploy Python programs while utilizing Jupyter notebooks to analyze fundamental data. • Master data wrangling, data exploration, data visualization, hypothesis formulation, and testing procedures in Data Science. • Utilize NumPy and SciPy packages for advanced mathematical and technical computing, along with the Pandas package for data analysis. • Grasp supervised and unsupervised learning methods, including linear regression, logistic regression, clustering, dimensionality reduction, K-NN, recommendation engines, pipeline construction, and time series modeling. • Comprehend the core principles, operations, functions, and execution pipeline of TensorFlow. • Delve into advanced AI topics such as Convolutional Neural Networks (CNN), artificial neural networks, deep recurrent neural networks, and high-level Natural Language Processing (NLP) interfaces.
The effort by IBM has resulted in a comprehensive Masters in AI program that seamlessly integrates Artificial Intelligence, Data Science, Machine Learning, and Deep Learning. This program facilitates the practical application of advanced tools and models, equipping students with practical skills. You will learn essential statistical principles for machine learning, Python programming, data visualization, and feature engineering. Through courses covering Python libraries like TensorFlow, Matplotlib, and Scikit-learn, you will gain proficiency in Machine Learning techniques, ranging from supervised and unsupervised learning to intricate concepts like artificial neural networks, data layers, feature extraction, and TensorFlow.
The Impact of Artificial Intelligence and Machine Learning The emergence of Artificial Intelligence and machine learning is set to significantly transform various aspects of daily life, with wide-ranging applications in healthcare, aviation, finance, logistics, and customer support. Pursuing a career in AI aligns you with an ever-evolving and dynamic industry poised for substantial growth. The comprehensive training offered by this program positions you to harness the potential of this transformative field.
With a surging demand in both the present and the future, AI Engineers are poised for remarkable opportunities. According to Paysa, licensed AI Engineers in the United States command an average annual income of $172,000 (approximately Rs. 17 lakhs to Rs. 25 lakhs in India). Remarkably, there are currently fewer than 10,000 competent professionals globally to fill these roles.
The Masters in Artificial Intelligence (MIND) program, developed in partnership with IBM, includes more than 15 real-world projects across a wide range of areas. The projects are carefully crafted to help you gain a deep understanding of key AI concepts.
Masters in Artificial Intelligence is open to candidates from any background including: Aspiring AI/Machine Learning EngineersAnalytical Managers managing analyst teamsData Architects looking for expertise in AI systems/algorithmsData Analysts looking to enter the machine learning/AI domainProfessionals looking to build a career in AI or machine learningExperts looking to improve their domain knowledge using Artificial Intelligence
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Artificial Intelligence
Tools Covered
Masters in Artificial Intelligence Learning Path
Course 1
Introduction to Artificial Intelligence
Gain a basic understanding of AI and how it can be used in business. Learn about machine learning and deep learning, as well as performance metrics.
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Lesson A – Course Introduction
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Lesson B – Decoding Artificial Intelligence
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Lesson C – Fundamentals of Machine Learning and Deep Learning
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Lesson D – Machine Learning Workflow
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Lesson E – Performance Metrics
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Course 2
Applied Data Science with Python
Master Python’s data analytics tools and techniques in this comprehensive course. Learn data wrangling, mathematical computing, and more. Develop essential skills for a career as a data scientist.
Lesson A: Course Introduction
Lesson B: Introduction to Data Science
Lesson C: Python Libraries for Data Science
Lesson D: Statistics
Lesson E: Data Wrangling
Lesson F: Feature Engineering
Lesson G: Exploratory Data Analysis
Lesson H: Feature Selection
Free Course
Math Refresher
Lesson A: Course Introduction
Lesson B: Probability and Statistics
Lesson C: Coordinate Geometry
Lesson D: Linear Algebra
Lesson E: Eigenvalues Eigenvectors and Eigendecomposition
Lesson F: Introduction to Calculus
Free Course
Statistics Essential for Data Science
Lesson A: Course Introduction
Lesson B: Introduction to Statistics
Lesson C: Understanding the Data
Lesson D: Descriptive Statistics
Lesson E: Data Visualization
Lesson F: Probability
Lesson G: Probability Distributions
Lesson H: Sampling and Sampling Techniques
Lesson I: Inferential Statistics
Lesson J: Application of Inferential Statistics
Lesson K: Relation between Variables
Lesson L: Application of Statistics in Business
Lesson M: Assisted Practice
Course 3
Machine Learning
Acquire in-depth knowledge of Machine Learning through hands-on projects, interactive labs, and mentoring. Master Machine Learning concepts and techniques, gaining the skills required for a successful career.
Lesson A: Course Introduction
Lesson B: Introduction to Machine Learning
Lesson C: Supervised Learning Regression and Classification
Lesson D: Decision Trees and Random Forest
Lesson E: Unsupervised Learning
Lesson F: Time Series Modelling
Lesson G: Ensemble Learning
Lesson H: Recommender Systems
Lesson I: Level Up Sessions
Practice Project
Free Course
Math Refresher
Lesson A: Course Introduction
Lesson B: Probability and Statistics
Lesson C: Coordinate Geometry
Lesson E: Linear Algebra
Lesson F: Eigenvalues Eigenvectors and Eigendecomposition
Lesson G: Introduction to Calculus
Free Course
Statistics Essential for Data Science
Lesson A: Course Introduction
Lesson B: Introduction to Statistics
Lesson C: Understanding the Data
Lesson D: Descriptive Statistics
Lesson E: Data Visualization
Lesson F: Probability
Lesson G: Probability Distributions
Lesson H: Sampling and Sampling Techniques
Lesson I: Inferential Statistics
Lesson J: Application of Inferential Statistics
Lesson K: Relation between Variables
Lesson L: Application of Statistics in Business
Lesson M: Assisted PracticeAD
Course 4
Deep Learning with Keras and TensorFlow
Learn how to use Keras or TensorFlow to explore the concepts and models of Deep Learning. Learn how to build deep learning algorithms and get ready to become a Deep Learning Engineer.
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Section 1 – Deep Learning with Tensor Flow (Self Learning)
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Lesson A – Welcome!
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Lesson B – Introduction to Tensorflow
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Lesson C – Convolutional Networks
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Lesson D – Recurrent Neural Network
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Lesson E – Restricted Boltzmann Machines (RBM)
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Lesson F – Autoencoders
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Lesson G – Course Summary
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Section 2 – Deep Learning with Keras and Tensor Flow (Live Classes)
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Lesson A – Course introduction
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Lesson B- AI and Deep learning introduction
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Lesson C – Artificial Neural Network
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Lesson D – Deep Neural Network & Tools
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Lesson E- Deep Neural Net optimization, tuning, interpretability
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Lesson F – Convolutional Neural Network
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Lesson G – Recurrent Neural Networks
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Lesson H – Autoencoders
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Section 3 – Practice Projects
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Practice Projects
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PUBG Players Finishing Placement Prediction
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Course 5
AI Capstone Project
Apply your program’s knowledge to a real-world, industry-related challenge.
Complete a full capstone project that shows off your skills to employers.
Exploratory Data Analysis
Model Building and fitting
Unsupervised learning
Representing results
Master's Program Certificate
Electives
Python for Data Science
Electives
Advanced Deep Learning and Computer Vision
Electives
Natural Language Processing (NLP)
Electives
Use cases of ChatGPT
Electives
Industry Master Class – Artificial Intelligence
Get Ahead with Master Certificate
Earn Your Artificial Intelligence Course Certificate
Stand Out With a Masters Certificate
Share Your Artificial Intelligence certificate
Earn Your Artificial Intelligence Course Certificate
Stand Out With a Masters Certificate
Share Your Artificial Intelligence certificate
Why Online Bootcamp
Develop skills for real career growth
A state-of-the-art curriculum developed in collaboration with industry and education to equip you with the skills you need to succeed in today’s world.
Don’t listen to trainers who aren’t in the game. Learn from the experts who are in the game.
Leading Practitioners who deliver current best practice and case studies in sessions that fit within your workflow.
Learn by working on real-world problems
Capstone projects combining real-world data sets with virtual laboratories for hands-on experience.
Structured guidance ensuring learning never stops
24×7 Learning support from mentors and a community of like-minded peers to resolve any conceptual doubts
Artificial Intelligence Course FAQs
If you want to pursue a career in AI, you will need to have a good understanding of mathematical concepts such as Statistics, Probability, Linear Algebra, Calculus, and Bayesian Algorithms. You will need to have knowledge of Statistics, Learning Theory, Critical Thinking, Classic Mechanics, and Natural Language Processing. You will also need to be familiar with Programming Language, Data Structures, and Logical Reasoning.
With a deep understanding of AI, professionals are open to many opportunities. They can work as an AI Specialist, a Machine Learning Engineer, an NLP Scientist, an AI Research Analyst, or a Data Scientist.
Top ten use cases for AI are:
Education
Health & Medicine
Retail & e-commerce Food Technology
Banking & Finance
Logistics & Transportation
Travel
Real Estate
Entertainment
Sports Manufacturing
The following courses will be eligible for your IBM credentials:
Python for Deep Learning in Data Science with Keras & Tensorflow
