Artificial Intelligence Engineer (AI) - Master's Program in collaboration with IBM

Artificial Intelligence - Master's Program

The AI Engineer Master’s Program, in collaboration with IBM, covers the crucial skills you need for a successful career in Artificial Intelligence (AI). As you undertake this Artificial Intelligence program, you’ll master the concepts of Machine Learning and Deep Learning and the internationally-acclaimed programming language Python needed to excel in AI. You will also learn how to design intelligent models and advanced artificial neural networks and leverage predictive analytics to solve real-time problems to take your career in Artificial Intelligence to the next level.

Key Features

• 11 months long live online bootcamp classroom and eLearning (self-paced)
• 1 year access to self-paced learning content
• Certificate of Completion of AI Engineer Program and IBM certificates for IBM courses.
• Core courses delivered in live online classes with 8X higher interaction delivered by experienced trainers and industry experts.
• 3 Capstones (final projects) and 25+ practical projects from different industry domains like Amazon, Walmart, Mercedes Benz, and Uber
• Session about the latest AI trends, such as ChatGPT, generative AI, prompt engineering and much more.
• Exposure to TensorFlow, Keras, ChatGPT, OpenAI, Dall-E and other prominent tools.
• Masterclasses, Exclusive hackathons, and Ask Me Anything sessions held by experts from IBM.
• Complete program helps you get noticed by the best recruitment companies.

Program Outcomes

• Learn about the major applications of Artificial Intelligence across various use cases across various fields like customer service, financial services, healthcare, etc.
• Implement classical Artificial Intelligence techniques such as search algorithms, neural networks, and tracking.
• Gain the ability to apply Artificial Intelligence techniques for problem solving and explain the limitations of current Artificial Intelligence techniques.
• Master the skills and tools used by the most innovative Artificial Intelligence teams across the globe as you delve into specializations, and gain experience solving real-world challenges.
• Design and build your intelligent agents and apply them to create practical Artificial Intelligence projects, including games, Machine Learning models, logic constraint satisfaction problems, knowledge-based systems, probabilistic models, agent decision-making functions, and more.
• Understand the concepts of TensorFlow, its main functions, operations, and the execution pipeline.
• Understand and master the concepts and principles of Machine Learning, including its mathematical and heuristic aspects.
• Master and comprehend advanced topics such as convolutional neural networks, recurrent neural networks, training deep networks, and high level interfaces.
• Learn to deploy deep learning models on Docker, Kubernetes, and serverless environments (cloud).
• Understand the fundamentals of Natural Language Processing using the most popular library, Python’s Natural Language Toolkit (NLTK).

Target Audience

This program caters to professionals from a variety of industries and backgrounds. The diversity of our students adds richness to class discussions and interactions. Roles in this space require a combination of experience and an understanding of tools and technologies. This program is ideal for professionals looking for a career transition into the field of AI and ML, who have knowledge or prior experience in programming and mathematics, and an analytical frame of mind.

With the demand for Artificial Intelligence in a broad range of industries such as banking and finance, manufacturing, transport and logistics, healthcare, home maintenance, and customer service, the Artificial Intelligence course is well suited for a variety of profiles like:

• Developers aspiring to be an ‘Artificial Intelligence Engineers’ or Machine Learning engineers
• Analytics managers who are leading a team of analysts
• Information architects who want to gain expertise in Artificial Intelligence algorithms
• Graduates looking to build a career in Artificial Intelligence and Machine Learning

Professionals eager to develop AI and ML expertise with the objective of:

• Enhancing effectiveness in their current role
• Transitioning to AI and ML roles in their organization
• Seeking to advance their career in the industry Giving shape to entrepreneurial aspirations

Learning Path

1. Essentials of Generative AI, Prompt Engineering & ChatGPT (Live Online Classroom)
2. Programming Essentials (Live Online Classroom & eLearning)
3. Python for Data Science (IBM) (eLearning)
4. Applied Data Science with Python (Live Online Classroom & eLearning)
5. Machine Learning using Python (Live Online Classroom & eLearning)
6. Deep Learning Specialization (Live Online Classroom & eLearning)

Elective Courses

• Deep Learning with TensorFlow (IBM)
• Advanced Deep Learning and Computer Vision
• Natural Language Processing
• Reinforcement Learning
• Advanced Generative AI
• AI Engineer Industry Masterclass

1. Essentials of Generative AI, Prompt Engineering & ChatGPT

This course thoroughly explores Generative AI models, specifically emphasizing ChatGPT. Participants will understand the fundamentals of Generative AI and its scope, prompt engineering, explainable AI, conversational AI, ChatGPT, other large language models, and more.

Key Learning Objectives

• Acquire a solid foundation in Generative AI models, encompassing their core principles and various models.
• Grasp the concept of explainable AI, understand its importance, and distinguish between different approaches for achieving explainability in AI systems.
• Utilize effective prompt engineering techniques to enhance performance and regulate the behavior of Generative AI models.
• Develop a comprehensive understanding of ChatGPT, including its operational mechanisms, notable features, and limitations.
• Explore a range of applications and scenarios where ChatGPT can be effectively utilized.
• Master fine-tuning techniques to personalize and optimize ChatGPT models.
• Recognize the ethical challenges of Generative AI models to ensure responsible data usage, mitigate bias, and prevent misuse.
• Comprehend the transformative potential of Generative AI across industries and explore prominent tools
• Gain insights into the future of Generative AI, its challenges, and the necessary steps to unlock its full potential.

Topics Covered

• Generative AI and its Landscape
• ChatGPT and its Applications
• Explainable AI
• Conversational AI
• Prompt Engineering
• Designing and Generating Effective Prompts
• Large Language Models
• Fine-tuning ChatGPT
• Ethical Considerations in Generative AI
• Responsible Data Usage and Privacy
• The Future of Generative AI
• AI Technologies for Innovation

2. Programming Essentials

In this course, you will acquire essential Python skills that will serve as one of the building blocks for your journey throughout the program.

Key Learning Objectives

• Gain knowledge of procedural and object-oriented programming
• Understand the benefits and advantages of using Python as a programming language.
• Install Python and its integrated development environment.
• Familiarize yourself with Jupyter Notebook and its usage.
• Implement Python identifiers, indentations, and comments effectively.
• Understand Python’s data types, operators, and string functions.
• Learn about different types of loops in Python.
• Explore variable scope within functions.
• Explain the concepts of objectoriented programming and its characteristics.
• Describe methods, attributes, and access modifiers in Python.
• Gain an understanding of multithreading.

Topics covered

• Fundamentals of Programming
• Introduction to Python Programming
• Python Data Types and Operators
• Conditional Statements and Loops in Python
• Python Functions
• Object-Oriented Programming Concepts with Python
• Threading

3. Python for Data Science (IBM)

Designed by IBM, this course teaches students how to leverage Python for data science. Upon completion, you will be able to write Python scripts and conduct critical hands-on data analysis using a Jupyter-based lab environment.

Key Learning Objectives

• Use variables, strings, functions, loops, and conditions to create your first Python program.
• Gain an understanding of lists, sets, dictionaries, conditions, branching, objects, and classes
• Leverage pandas to load, manipulate, and save data, as well as read and write files in Python

Topics covered

• Python Basics
• Python Data Structures
• Python Programming Fundamentals
• Working with Data in Python
• Working with NumPy Arrays

4. Applied Data Science with Python

This course provides a comprehensive understanding of data science essentials, including data preparation, model building, and evaluation. Participants will learn concepts like strings, Lambda functions, and lists.

Key Learning Objectives

• Explain the fundamentals of data science and its practical applications
• Explore the processes of data preparation, model building, and evaluation.
• Apply Python concepts like strings and understand Lambda functions and lists.
• Develop a solid understanding of the fundamentals of NumPy.
• Explore array indexing and slicing techniques.
• Apply principles of linear algebra in data analysis.
• Understand the application of calculus in linear algebra
• Calculate measures of central tendency and dispersion.
• Gain understanding of statistical concepts such as skewness, covariance, and correlation.
• Describe the null hypothesis and alternative hypothesis.
• Examine different hypothesis tests, including Z-test and T-test
• Understand the concept of ANOVA.
• Work with pandas’ two primary data structures: Series and DataFrame.
• Utilize pandas for tasks such as data loading, indexing, reindexing, and data merging.
• Prepare, format, normalize, and standardize data using data binning techniques.
• Create visualizations with Matplotlib, Seaborn, Plotly, and Bokeh.

Topics Covered

• Introduction to Data Science
• Essentials of Python Programming
• NumPy
• Linear Algebra
• Statistics Fundamentals
• Probability Distributions
• Advanced Statistics
• Working with pandas
• Data Analysis
• Data Wrangling
• Data Visualization
• End-to-End Statistics Applications in Python

5. Artificial Intelligence Capstone Project

This course provides a comprehensive overview of various machine learning types and their practical applications. You will explore the machine learning pipeline and gain insights into supervised learning, regression models, and classification algorithms. Additionally, you will study unsupervised learning, clustering techniques, and ensemble modeling. Evaluate popular machine learning frameworks such as TensorFlow and Keras, and build a recommendation engine using PyTorch.

Key Learning Objectives

• Examine the different types of machine learning and their respective characteristics
• Analyze the machine learning pipeline and understand the key operations involved in Machine Learning Operations (MLOps).
• Learn about supervised learning and its wide range of applications.
• Understand the concepts of overfitting and underfitting and employ techniques to detect and prevent them.
• Analyze various regression models and their suitability for different scenarios.
• Identify linearity between variables and create correlation maps.
• List different types of classification algorithms and understand their specific applications.
• Master various types of unsupervised learning methods and when to use them.
• Gain a deep understanding of different clustering techniques within unsupervised learning.
• Examine different ensemble modeling techniques such as bagging, boosting, and stacking.
• Evaluate and compare different machine learning frameworks, including TensorFlow and Keras.
• Build a recommendation engine using PyTorch.

Topics covered

• Machine Learning
• Supervised Learning
• Regression and its Applications
• Classification and its Applications
• Unsupervised Learning
• Ensemble Learning
• Recommendation Systems

6. Deep Learning Specialization

This comprehensive course provides the knowledge and skills to deploy deep learning tools using AI/ML frameworks effectively. You will explore the fundamental concepts and practical applications of deep learning while gaining a clear understanding of the distinctions between deep learning and machine learning.

Key Learning Objectives

• Differentiate between deep learning and machine learning and understand their respective applications.
• Gain a thorough understanding of various types of neural networks.
• Master the concepts of forward propagation and backward propagation in Deep Neural Networks (DNNs).
• Gain insight into modeling techniques and performance improvement in deep learning.
• Understand the principles of hyperparameter tuning and model interpretability.
• Learn about essential techniques such as dropout and early stopping and implement them effectively.
• Develop expertise in Convolutional Neural Networks (CNNs) and object detection.
• Acquire a solid understanding of Recurrent Neural Networks (RNNs).
• Gain familiarity with PyTorch and learn how to create neural networks using this framework.

Topics covered

• Introduction to Deep Learning
• Artificial Neural Networks
• Deep Neural Networks
• TensorFlow
• Model Optimization and Performance Improvement
• Convolutional Neural Networks (CNNs)
• Transfer Learning
• Object Detection
• Recurrent Neural Networks (RNNs)
• Transformer Models for Natural Language Processing (NLP)
• Getting Started with Autoencoders
• PyTorch

7. Artificial Intelligence Capstone Project

AVC's Artificial Intelligence Capstone project will allow you to implement the skills you learned in the Masters of Artificial Intelligence. With dedicated mentoring sessions, you’ll know how to solve a real industry-aligned problem. You’ll learn various Artificial Intelligence-based supervised and unsupervised techniques like Regression, SVM, Tree-based algorithms, NLP, etc. The project is the final step in the learning path and will help you to showcase your expertise to employers.

The capstone project will enhance your understanding of the artificial intelligence decision cycle, including performing exploratory data analysis, building and fine-tuning a model with cutting-edge AI-based algorithms, and representing results

Key Learning Objectives

AVC's online Artificial Intelligence Capstone course will bring you through the Artificial Intelligence decision cycle, including Exploratory Data Analysis, building and fine-tuning a model with cutting-edge Artificial Intelligence-based algorithms, and representing results. The project milestones are as follows:

• Exploratory Data Analysis - In this step, you will apply various data processing techniques to determine the features and correlation between them, transformations required to make the data sense,new features, construction, etc.
• Model Building and fitting - This will be performed using Machine Learning algorithms like regression, multinomial Naïve Bayes, SVM, tree-based algorithms, etc.
• Unsupervised learning - Clustering to group similar transactions/reviews using NLP and related techniques to devise meaningful conclusions.

The Electives:

Deep Learning with Tensorflow (IBM)

This course will take your machine learning skills to the next level by providing a comprehensive understanding of Deep Learning with TensorFlow and Keras. Become proficient in deep learning concepts, enabling you to construct artificial neural networks and navigate through layers of data abstraction. By unlocking the potential of data, this course prepares you for new frontiers in Artificial Intelligence.

Advanced Deep Learning and Computer Vision

This comprehensive course provides in-depth knowledge and practical skills in the field of computer vision and advanced deep learning techniques. You will delve into various topics, including image formation and processing, Convolutional Neural Networks (CNNs, object detection, image segmentation, generative models, optical character recognition, distributed and parallel computing, and deploying deep learning models. By the end of the course, you will have the expertise to tackle complex computer vision challenges and successfully deploy deep learning models in various applications.

Natural Language Processing

In this course, you will gain a detailed understanding of the science behind applying machine learning algorithms to process vast amounts of natural language data. The course focuses on natural language understanding, feature engineering, natural language generation, automated speech recognition, speech-to-text conversion, text-to-speech conversion, and voice assistance devices.

Reinforcement Learning

This course offers a comprehensive exploration of the core concepts of reinforcement learning. You will learn how to solve reinforcement learning problems using various strategies through practical examples and hands-on exercises using Python and TensorFlow. The course covers the theory behind RL algorithms and equips you with the skills to effectively utilize reinforcement learning as a problem-solving strategy. By the end of the course, you will be proficient in using RL algorithms to tackle a wide range of real-world challenges.

Advanced Generative AI

Dive deep into innovative Generative AI principles with this advanced course. During the program, you’ll thoroughly explore neural networks, LLMs, their architectures, and diverse generative models such as VAEs, GANs, autoencoders, and transformer-based models. Delve into renowned Generative AI models like GPT, BERT, and T5, mastering the art of effectively assessing their performance. Participate in hands-on learning activities, acquiring practical expertise in building and deploying a conversational chatbot that engages in meaningful dialogue interactions.

AI Engineer Industry Masterclass

Attend this live interactive industry masterclass to gain insights about the latest advancements in artificial intelligence

FAQ

What is the format of the program?

The programmes are entirely distance learning. Parts are practical e-learning courses that you can complete when you have time and at your own pace which you can also learn from your mobile phone (our app).

There are also online classroom sessions via our advanced professional distance learning system. We have a range of time slots to choose from and we always record the sessions so you can listen to them if you miss anything or want to review information. There is always someone on hand to help and support you if you have any questions about the skills you are learning.

When can I take the live online courses?

The timing of each course varies for different groups. You will be given access to a dashboard with several different time slots for the same session/topic. You decide which date and time works best for you. Some are scheduled for weekday afternoons, while others are scheduled for weekend mornings or evenings. Scheduling is based on factors such as the number of interested participants and the availability of trainers. If you miss a session, you can always watch recordings of that session. You will never miss out!