Post Graduate Program in Artificial Intelligence for Leaders

In the first week of the first module, you would learn about the business of Artificial Intelligence. By the end of the week, you would have gained various perspectives and warmed up gradually. Welcome to Career 2.0

• Introduction to Artificial Intelligence

Let’s go back to basics with this submodule,You would gain a basic understanding of AI and common keywords used in industries today.

• Explosion in AI

This module will help you appreciate the magnitude of Artificial Intelligence. You would understand how AI is exploding in several industries like Financial Services, Pharmaceuticals, and Customer Experience.

• Business application and its limitations

In this submodule, you would learn about the several business applications of AI & its limitations.

• Building AI project

Let’s increase our pace, shall we? You would now learn the techniques and methodologies involved in building AI projects.

• ROI Calculation

You will learn to appreciate AI from various angles like cost-benefit. As a leader in AI, you need to know the metrics involved. In this submodule, you would learn about the several methods and techniques involved in the calculation of return on investment.

• Case Study

The most awaited part of this course is already here. Your first case study is already here. You would also partake in a case study session guided by experts. Congrats on completing your first week.

Data Visualisation is an important technique that needs to be pursued by AI project managers and AI leaders. Data Visualisation refers to the tools and techniques employed to represent data in a graphical format. You would learn the data visualization techniques in this specific sub-module of The Artificial Intelligence for leaders program. By the end of this program, you will be able to understand the working of KNIME Studio and also will now be comfortable around various forms of data and representation of it visually. This module is highly interesting and you will enjoy learning the new tools.

• What is Data?

Since data is the primary source of AI, you would learn about data

• Numerical and Textual data

In this sub-module, you would learn about numerical and textual data.

• Graphs & Networks

You would master the methodologies and techniques involved in graphs and networks

• Time series data

Time series data is a collection of observations for a particular subject at varying time intervals

• Different types of data objects

In this sub-module, you would learn about different types of data objects

• Understanding Visual metrics – Mean, Median & Mode

Virtual metrics are incredibly used in the application of AI and they contribute so much to any business.

• Introduction to KNIME

You would gain a basic understanding of cloud platforms using KNIME.

• Visualizing data using KNIME studio

You would master KNIME to visualize the data.

• Data Manipulation using KNIME studio

You would also learn Data manipulation using KNIME.
 

In this module, you would work on a project and participate in case study analysis to enhance your understanding.

This week, you will meet some mathematical models like regression and learn more about statistics and numbers. Watch closely and ask questions, because this module is very important for what comes next. As an AIML Leader, you will be hearing these terms in data reports frequently. Regression employs mathematical methods that assist AI professionals to forecast accurate outcomes and make wise decisions considering the value of one or more predictor variables. You would master several regression techniques throughout this module of the Artificial Intelligence for leaders course. Below are the various concepts you would learn in this module.

• Introduction to Regression

You would gain a basic understanding of Regression.

• Linear Regression

Linear regression refers to the implementation of a linear function to a set of input-output pairs given a set of training examples where input and output features are numeric.

• Multivariate Linear regression

Multivariate regression is a procedure that evaluates a single regression model with more than one outcome variable.

• Categorical Independent variable in regression

A categorical variable is a statistical approach of AI. You would learn in this module.

• Root Mean square error and Mean Absolute error

Decoding the root Mean square error and Mean Absolute error.

• Linear Regression – Pros & Cons

Evaluate the pros and cons of Linear regression in AI.

• Hands-on using KNIME

Let’s put those skills to action in the lab. You would participate in hands-on lab sessions using KNIME.

• Case study session with experts

Each week closes with a case study to help you compile all your thoughts. You would partake in a case study session guided with experts. We suggest you ask as many questions as possible and leave no doubts unanswered.

You are now going to finish one-third of the program. By this time you have a stronghold on the basics and can understand what is happening. Brace yourself, we are going to power ahead now. Enjoy your hands-on lab sessions using KNIME.

Classification in Machine Learning refers to the identification of the category of the given problem. You would master several techniques of classification in this specific sub-module of AI for business leaders course. As a leader in AI, you will be expected to understand and evaluate business models. We will be taking you through some of them to help you build your own!

• Introduction to classification

You would gain a basic understanding of classification.

• Logistic regression

Linear Regression is applied to predict the outcome that is continuous and has a constant slope within a continuous range.

• Setting up threshold

In this module, you would learn to set up a threshold.

• Performance measures – Precision & Recall

In this module, you would master several performance measures such as Precision and Recall.

• Evaluation of models

One of the most important sessions. You would learn several techniques to calculate the accuracy of the machine learning model.

• Hands-on using KNIME

Enjoy your hands-on lab sessions using KNIME.

• Case study session with experts

By this time, you would be just as excited about the case studies as we are. These case studies are a unique opportunity to understand real life scenarios and the industry.

One-third of the program is already done. Be proud of yourself. This week will be about leadership decisions, documentations, and managerial concepts. Time to awaken the business acumen.

This module focuses on building the Proof of Concept ( PoC) for AI projects. Below are the various concepts you would learn in this module.

• Building POC – Outline

This module focuses on building the proof of concept ( PoC) for AI projects.

• Solution at a glance

In step 2, you would master the techniques employed to derive the solution at a glance.

• Market potential

Understanding the Market potential is a vital step and we help you identify the various ways.

• Threats & Opportunities

Learn to do a SWOT analysis of your projects in this module.

• Requirements – Data & People

In this submodule, you would learn about evaluating the requirements of data and people to build an AI project.

• Product Development Roadmap

A product development roadmap defines the summary of the proposed work and events included throughout the delivery of the output.

• Expansion plan

After set-up comes expansion. With this module, you would master the techniques employed to expand the AI plan.

• AI techniques & their relevance to domains

In this module, you would master the techniques of AI and their relevance to other domains.

• Identifying AI use cases

In this submodule, you would master several techniques of Identifying AI use cases.

• Tips for building successful AI product

In this bonus module, you would master some great techniques and tips employed for building a successful AI product.

In this module, you would work on a project and participate in 3 hands-on lab sessions besides working on 5 case studies to enhance your understanding.

Neural networks are a series of algorithms developed inspired by the biological neural networks designed to mimic the human brain in performing quick actions. Below are the various concepts you would learn in this module of the Artificial Intelligence Course.

• Introduction to Neural Networks

In this submodule, you would gain a basic understanding of Neural Networks.

• Activation function

Activation functions are applied to an artificial neuron to deliver outputs considering the given inputs.

• Feed forward neural network

In this submodule, you would learn the techniques of feed-forward neural networks.

• The topology of a neural network

In this submodule, you would learn about the topology of a neural network.

• Error & Loss function

In this submodule, you would learn about the error and loss functions of a neural network.

• Training a neural network

In this submodule, you would learn to train a neural network.

• Optimizing a neural network

In this submodule, you would learn several techniques used to optimize a neural network.

• Hands-on using KNIME

You would participate in hands-on lab sessions using KNIME and gain Hands-on expertise.

You are now almost halfway across the program and the pace has now increased. You will now be dealing with detailed concepts and more practical-based learning. Ensemble techniques are applied to obtain enhanced predictive performance than the models obtained by using any other learning algorithms.

Below are the various concepts of Ensemble techniques you would learn in this module of AI for business leaders course.

• Introduction to Decision trees

Decision trees are used to analyze the models as they facilitate effective decision-making.

• CART

The CART algorithm is designed as a sequence of problems, the answers to which defines what would be the next question, if there exists any.

• Pruning

Pruning is applied to checkout each note of a game tree which facilitates computing the correct minimax decision.

• Ensemble techniques

You would learn several ensemble techniques in this submodule.

• Random Forest

Random Forests are a group of decision trees used for classification, regression and more. Random Forests are ensemble learning methodologies.

• Hands-on using KNIME

You would also gain Hands-on expertise working with KNIME.

• Case study session with experts

You would also partake in an interesting case study session guided by experts.

Throughout this module, you would work on one project and 3 hands-on lab sessions that help you gain hands-on expertise on the tools and techniques learned. Your mentors and program managers will be right next to you, ensuring you keep powering ahead.

Clustering is a technique applied to group a set of data points and classify them accordingly.

• Introduction to Clustering

Clustering is a term you would have heard often in AI, let’s decode it.

• Types of clustering

You would learn the different types of clustering techniques.

• K Means clustering

K clustering is an unsupervised algorithm employed to assign each data point to any of the K groups considering the features specified.

• Importance of scaling
• Applications of clustering

In this submodule, you would learn the several applications of clustering techniques.

• Advantages & Disadvantages of clustering

In this submodule, you would learn the advantages & disadvantages of clustering.

• Visual analysis

In this submodule, you would learn about the techniques involved in visual analysis.

• Hands-on using KNIME

You would participate in hands-on lab sessions using KNIME.

This module of the AI for managers program focuses on teaching the tools and techniques used to set up AI teams and drive AI culture. This module assists you to regulate the effective application of artificial intelligence in management. You will find this module extremely useful and interesting as it discusses managerial concepts and leadership. Below are the various concepts you would learn in this module - AI for management.

• Service Vs product companies

In this submodule, you would learn about service and product companies, the differences between them and more.

• AI Team composition

In this submodule, you would learn the specifications of building an AI team.

• Centralized vs Distributed AI teams

You would learn the differences between centralized vs distributed AI teams.

• How to keep your team motivated?

You would master the techniques to keep the team motivated.

• Handling resistance from senior management

In this submodule, you would learn the techniques to handle resistance from senior management.

• Coaching others

You would also gain knowledge used to coach others in an AI team.

• Managing portfolio of projects

In this submodule, you would learn the techniques to manage a portfolio of various AI projects.

• Scaling AI teams

In this submodule, you would learn the techniques employed in Scaling the AI teams.

As one of the most common applications of AI, we strongly recommend you take recommendation systems seriously. Most projects across all domains use recommendation systems to study the user's past actions and propose the most accurate recommendations considering their preferences. These recommendations enhance the user's experience. Below are the various concepts of recommendation systems you would learn in this module.

• Introduction to Recommendation systems

You would gain a basic understanding of Recommendation Systems.

• Content based filtering

You would learn the techniques of content-based filtering of the recommendations.

• Collaborative filtering

You would learn the techniques of collaborative-based filtering of the recommendations.

• Similarity measures

Similarity measures define the real-valued function that quantifies the similarity between two objects.

• Case study

You would also partake in a case study session guided by experts.

• Hybrid Systems

Hybrid systems combine neural networks and AI to detect the patterns in a given dataset. They are rapidly employed in solving the problems of deep learning.

• Hands-on using KNIME

In this submodule, you would gain hands on expertise on KNIME.

• Case study session with experts

You would also partake in a case study session guided with experts on subjects like Netflix and Amazon which use recommendation systems to produce customer satisfaction.

Throughout this module, you would work on a project and 3 hands on labs besides working on a case study.

Natural language processing employs computational linguistics to develop real-world applications that run with languages of fluctuating structures. NLP deals with training a system to learn a language and understand it. Below are the various concepts of NLP you would learn in this module.

• Introduction to NLP

You would gain a basic understanding of Natural Language Processing.

• Different tasks in NLP

In this submodule, you would learn about the different tasks executed in NLP.

• How are NLP problems solved?

In this submodule, you would learn how to solve the various NLP problems.

• Text extraction/ web scraping

You would learn the techniques of text extraction and web scraping.

• Building a model

You would learn to build an NLP model.

• Case study – Sentiment analysis

You would also partake in a case study performed on Sentiment analysis.

• NLP Demonstration on Sentiment analysis
• Hands-on using AWS

You would participate in hands-on lab sessions using AWS.

• Case study session with experts

You would also partake in a case study session guided by experts.

Computer Vision is a trending application of Artificial Intelligence. Computer Vision is the technique of training the systems to process data from the images. In this submodule, you will learn to process the images for Image classification employing Neural Networks.

• Introduction to Computer Vision

You would gain a basic understanding of Computer Vision.

• Types of CV problems

You will learn about several types of CV problems and the techniques to solve them.

• Pixel

A pixel in computer vision refers to the numerical value assigned to one specific point of the picture.

• How does the computer see an image?

You would learn how the computer sees an image with regard to computer vision.

• 3D images

You would gain an in-depth understanding of the CV of 3D images.

• Resolution

Resolution in computer vision refers to the number of pixels that exist in a display monitor. Resolution refers to the sharpness of the image in a display.

• Convolution & Pooling

Convolutional layers in a convolutional neural network compile the behavior of features in an input image and Pooling layers provide access to downsample the future maps.

• Convolutional Neural Networks

Convolutional neural networks deep learning algorithms, made of neurons that receive inputs that assign importance to each of the objects of the given image and differentiate from one another.

• Hands on using AWS

You would participate in hands-on lab sessions using AWS.

• Case study session with experts

You would also partake in a case study session guided by experts.

Throughout this module, you would work on a project and 3 hands on labs besides working on 9 case studies spanning various industries and domains

• Transfer Learning

Transfer learning is a machine learning research application that concentrates on storing the insights obtained while solving the given problem.

• How it works

You would gain an in-depth understanding of the functionality of Transfer Learning.

• Applications of Transfer Learning – Advantages vs Disadvantages

In this submodule, you would learn about the various applications of Transfer Learning, its advantages, and disadvantages.

• Dealing with Imbalanced data - Data Augmentation

AI and ML require to deal with a lot of imbalanced data. In this submodule,, you would learn several techniques applied in dealing with imbalanced data. You would learn the process of Data Augmentation.

• Data Augmentation types

Data augmentation is a strategy that empowers AI professionals to enhance the variety of existing or available data for training models, without collecting new data. In this submodule, you would learn different types of Data Augmentation types.

• Model Deployment

In this submodule, you would learn the techniques and methodologies applied for the deployment of a Machine Learning model.

• Modes of training

You would master the several modes of training techniques and methodologies in this submodule.

• Serialization

Serialization is a technique applied in Artificial Intelligence for transforming objects into a structured format that can be stored in a file.

• Model Monitoring & Recalibration

In this submodule, you would master several techniques of model monitoring & recalibration.

Throughout this module, you would work on a project that helps enhance your understanding of the concepts learned.

This submodule was designed to help you deploy your AI base projects and oversee a successful implementation.You would now have a proper overview of AI projects and their stages from the beginning to deployment

• Overview of ChatGPT and OpenAI
• Timeline of NLP and Generative AI
• Frameworks for understanding ChatGPT and Generative AI
• Implications for work, business, and education
• Output modalities and limitations
• Business roles to leverage ChatGPT
• Prompt engineering for fine-tuning outputs
• Practical demonstration and bonus section on RLHF

• Welcome & Program Orientation
• Introduction to Decision Sciences & AI
• Campus Tour & Group Photo
• Introduction to Dynamic Programming
• Programming an AI agent to Play a Variant of Blackjack

• Introduction to Reinforcement Learning
• Programming an AI Agent that learns by itself to play computer games
• Session with Industry Mentor 
• The Art and Science of Negotiations

• Project Brief and Active group work
• Group work on Project 
• Certifications and Photo Ops