Created in partnership with IBM, this course introduces students to blended learning and prepares them to be AI and Data Science specialists. In Armonk, New York, IBM is a significant cognitive service and integrated cloud solution firm that provides many technology and consulting solutions.
Every year, IBM invests $6 billion in research & development and has won five Nobel prizes, nine US National Medals of Technology, five US National Medals of Science, six Turing Awards, and ten US Inventors Hall of Fame inductions.
IBM is a leader in AI and Machine Learning technology verticals for 2021. This AI masters course will prepare students for Artificial Intelligence and Data Analytics careers.
You will be able to demonstrate the following abilities after completing this Master in Artificial Intelligence:
Gain insights into the latest AI trends like Generative AI, prompt engineering, ChatGPT, and more.
Learn how to apply effective prompt engineering techniques to improve the performance and control the behavior of Generative AI models.
Master AI and ML comprehensively, understanding their meaning, purpose, scope, stages, applications, and effects.
Navigate data science intricacies with expertise, encompassing processes, wrangling, exploration, visualization, hypothesis building, and testing.
Conduct scientific and technical computing seamlessly using the SciPy package, including subpackages like Integrate, Optimize, Statistics, IO, and Weave.
Excel in mathematical computing using the NumPy and scikit-learn package.
Gain expertise in supervised and unsupervised learning, recommendation engines, and time series modeling.
Validate machine learning models effectively, decoding various accuracy metrics.
Understand and apply deep learning across various applications.
Navigate the layers of data abstraction in neural networks, gaining unparalleled insights into data.
Utilize tools like Keras to build computer vision applications.
Become well-versed with Generative Adversarial Networks (GANs).
Execute distributed and parallel computing efficiently, leveraging high-performance GPUs.
Comprehend natural language understanding and natural language generation
Master natural language understanding and generation, delving into the fundamentals of NLP using Python’s Natural Language Toolkit (NLTK).
Know how to apply machine learning and deep learning seamlessly with NLP.
Conduct text-to-speech conversion with automated speech recognition.
Learn how to apply reinforcement learning theory using Python and TensorFlow.
Master ways to solve reinforcement learning problems through various industry-standard strategies.
Simplilearn and IBM collaborated on a Master's in AI that combines AI, Data Science, Machine Learning, and Deep Learning, allowing for the real-world application of sophisticated tools and models. This AI Masters Program will teach you the principles of statistics for machine learning, python programming, data visualization, and feature engineering. These courses will teach you how to use Python libraries like TensorFlow, Matplotlib, and sci-kit-learn, as well as essential Machine Learning techniques like supervised and unsupervised learning and advanced concepts like artificial neural networks, layers of data
and feature extraction and TensorFlow.
Artificial intelligence and machine learning will substantially impact all aspects of daily life in the near future, with applications in healthcare, aviation, finance, logistics, and customer support. A job in AI puts you on the fast road to a dynamic, ever-changing industry that is predicted to grow significantly and beyond once you complete the training.
Artificial intelligence (AI) and AI engineering have been witnessing significant growth, and numerous statistical indicators support the attractiveness of becoming an AI engineer.
According to the World Economic Forum, the demand for AI and machine learning specialists is expected to increase by 60% by 2025.
In the U.S., the Bureau of Labor Statistics projected a 15% growth in employment for computer and information research scientists (which includes AI engineers) from 2019 to 2029, much faster than the average for all occupations.
AI engineers typically command higher-than-average salaries due to their specialized skill set and high demand. In the U.S., according to Glassdoor, the average base pay for AI engineers exceeded $100,000 per year, and senior AI engineers often earned considerably more.
Numerous industries have been embracing AI technologies. This adoption spans sectors like healthcare, finance, automotive, retail, and more, signifying many opportunities for AI engineers to apply their skills across various domains.
The Global Generative AI market has huge potential with the current market trends. It is expected to grow to $667.9 billion by 2030.
ver 25 real-life projects in various areas are included in this AI Masters. These projects are intended to help you grasp essential AI topics in areas like ML, deep learning, NLP, computer vision, reinforcement learning, generative AI, prompt engineering, ChatGPT, and many more.
This training includes a capstone assignment allowing you to review the principles you've learned. You'll take specialized guided classes to develop a high-quality industrial project addressing real-world problems.
Ecommerce
Develop a shopping app for an ecommerce company using Python.
Food Service
Using data science techniques, such as time series forecasting, to help a data analytics company forecast demand for different restaurant items.
Retail
Use exploratory data analysis and statistical techniques to understand the factors contributing to a retail firm's customer acquisition.
Production
To understand their overall quality and sustainability, perform a feature analysis of water bottles using EDA and statistical techniques.
Real Estate
Use feature engineering to identify the top factors that influence price negotiations in the homebuying process.
Entertainment
Perform cluster analysis to create a recommended playlist of songs for users based on user behavior.
Human Resources
Build a machine learning model that predicts employee attrition rate at a company by identifying patterns in their work habits and desires to stay with the company.
Shipping
Use deep learning concepts, such as Convolutional Neural Networks (CNN), to automate a system that detects and prevents faulty situations resulting from human error and identifies the type of ship entering the port.
BFSI
Use deep learning to construct a model that predicts potential loan defaulters and ensures secure and trustworthy lending opportunities for a financial institution.
Healthcare
Use distributed training to construct a CNN model capable of detecting diabetic retinopathy and deploy it using TensorFlow Serving for an accurate diagnosis.
Leverage deep learning algorithms to develop a facial recognition feature that helps diagnose patients for genetic disorders and their variations.
Automobile
Examine accident data involving Tesla’s auto-pilot feature to assess the correlation between road safety and the use of auto-pilot technology.
Tourism
Uses AI to categorize images of historical structures and conduct exploratory data analysis (EDA) to build a recommendation engine that improves marketing initiatives for historic loca
You will obtain certificates from IBM and Simplilearn upon completing these courses. These certificates will attest to your abilities as an expert in AI. In addition, you will receive the following:
Masterclass by IBM experts
Ask-Me-Anything sessions with IBM leadership
Hackathons conducted by IBM
IBM Certificates for IBM courses
Industry-recognized Program completion certificate from Simplilearn
Delve into AI basics and generative AI principles. Grasp the importance of explainable AI. Employ prompt engineering to enhance generative AI performance. Understand ChatGPT's mechanisms, features, and constraints. Explore varied ChatGPT applications. Gain foresight into generative AI's future and challenges.
Discover procedural and object-oriented programming. Uncover Python's benefits. Set up Python and its IDE. Master Jupyter Notebook. Apply Python basics like identifiers, indentation, and comments. Understand data types, operators, and string functions. Explore Python loops and variable scopes. Learn about OOP, its features, and elements like methods, attributes, and access modifiers.
Kickstart your learning of Python for Data Science with this Data Scientist course and familiarize yourself with programming, tastefully crafted by IBM.
The Data Science with Python course provides a comprehensive overview of Python's tools and techniques for data analytics. Learning Python is a vital skill for numerous data science roles, and you can cultivate it through this course. With a blended learning approach, you can grasp Python for data science alongside concepts such as data wrangling, mathematical computing, and more and propel your career as a data scientist.
Ensure career success with this Machine Learning course. Learn this exciting branch of Artificial Intelligence with a program featuring Applied Learning, interactive labs, 4 hands-on projects, and mentoring. With our Machine Learning training, master Machine Learning concepts are required for a Machine learning certification. This Machine Learning online course will provide you with the skills needed to become a successful Machine Learning Engineer today.
Differentiate deep learning from machine learning. Explore various neural network types. Excel at forward and backward propagation in deep neural networks. Introduce modeling and performance enhancement in deep learning. Understand hyperparameter tuning and model interpretability. Learn dropout and early stopping implementation. Master CNNs, object detection, and RNN fundamentals. Grasp PyTorch basics and neural network creation.
The capstone project allows you to implement the skills you learned throughout this bootcamp. You will solve industry-specific challenges by leveraging various AI and ML techniques. The capstone project will help you showcase your expertise to employers.
Our Masters in Artificial Intelligence is exhaustive and this certificate is proof that you have taken a big leap in mastering the domain.
The knowledge and AI skills you've gained working on projects, simulations, and case studies will set you ahead of the competition.
Talk about your AI masters certificate on LinkedIn, Twitter, and Facebook, boost your resume, or frame it - tell your friends and colleagues about it.
Master's in Artificial Intelligence helps you gain a competitive edge over your peers and build job-ready skills. The instruction is offered by top-notch industry experts who have rich domain experience. By enrolling in this master's program, you will clearly understand various AI concepts like machine learning, natural language processing, computer vision, deep learning, neural networks, etc.
No, knowledge of coding is not mandatory for taking up this master's in artificial intelligence. It would, however, be beneficial to grasp the concepts faster.
A Master in Artificial Intelligence is a rigorous training program that helps students learn about this powerful technology from scratch and develop work-ready AI skills.
Professionals with a thorough knowledge of AI ideas have many opportunities. They can apply for AI Specialist, Machine Learning Engineer, NLP Scientist, AI Research Analyst, and Data Scientist.
You will be entitled to acquire the Masters in AI certificate, which will attest to your AI engineer abilities, provided you meet the following minimum requirements.
Course | Course completion certificate | Criteria |
Introduction to Artificial Intelligence Course |
Required | 85% of Online Self-paced completion and Pass Assessment test at 80% |
Data Science with Python | Required | 85% of Online Self-paced completion or attendance of 1 Live Virtual Classroom, a score above 75% in course-end assessment, and successful evaluation in at least 1 project |
Machine Learning | Required | 85% of Online Self-paced completion or attendance of 1 Live Virtual Classroom and successful evaluation in at least 1 project |
Deep Learning with Keras and TensorFlow |
Required | Attend 1 Live Virtual Classroom and successful evaluation in at least 1 project and score 70% for course-end assessment. |
Advanced Deep Learning and Computer Vision | Required | Attend 1 LVC batch, Pass a Project, Pass an Assessment test 70% |
AI Capstone Project | Required | Attendance of 1 Live Virtual Classroom and successful completion of the capstone project |
Math principles such as statistics, probability, linear algebra, calculus, and Bayesian algorithms should be understood by professionals who want to start AI careers. Statistics, learning theory, problem-solving, classical mechanics, and language processing are all skills they'll need. It is also suggested that you know at least one programming language, data structure, and logic.