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Executive Education Bauer

International Certification in Artificial Intelligence & Machine Learning

Become an Artificial Intelligence Leader with Skills in Artificial Intelligence Machine Learning Analytics Deep Learning

Cohorts from

Boston Consulting Group

5 months | 5 Courses

Assignments & Projects

Weekly Faculty Sessions

Recommended 6-8 hrs/ week

January 01, 2022

Next Cohort Starts

Unlimited Labs

With 1:1 Skill Coaches

Program Overview

Key Highlights


Multiple Case Studies and Assignments | 5 Capstone Industry Projects


Unlimited 1:1 Mentorship with Mentors & Faculty

hands on learning

Learning Library with Toolkits, Frameworks & Templates

build your idea

Focused New Age Curriculum

Team Building

Weekly Faculty Sessions (Live)


Exclusive Career Management Cell with Hiring Talentathons


Networking Opportunities on Campus and Off Campus

Career Labs - Career Coaching & Mentoring, Portfolio Building, Interview Preparation

"Industry wants to hire professionals who not only understand the depth of data science but can leverage that to business outcomes."
atul batra
Atul Batra
CTO, Manthan Systems

Top Skills You Will Learn

Python for Artificial Intelligence & Machine Learning, Prescriptive & Predictive Analytics, Feature extraction, Feature Engineering, Data Wrangling, Building a Neural Network, Applying Machine Learning in different problems.

Who Is This Program for?

Domain Experts, Engineers,  Software and IT Professionals, Project Managers, Product Managers, Business Analysts, Consultants, Entrepreneurs.

Minimum Eligibility

■ The applicant should have at least 1 year of work experience in a technical or business-related space and an undergraduate degree.
■ Prior knowledge of programming is preferred.

Job Opportunities

Data Analyst, Data Science Manager, Entry-level Data Scientist, Data Engineer

Do you have the skills to be a Machine Learning Expert ?

Tools Covered


Data Analytics Certification Program

Get a certificate issued by the C. T. Bauer College of Business at the University of Houston and Institute of Product Leadership.

Bauer College’s Cyvia and Melvyn Wolff Center for Entrepreneurship ranked No. 2 in U.S. on the Top 25 Best Undergrad Programs for Entrepreneurs in 2019. (Top 10 since 2007; No. 1 in 2008, 2010 and 2011)

Course audited and approved by the Bauer College.

Preparing you for Global Certification


Artificial Intelligence & Machine Learning Certification Program

Get a certificate issued by the C. T. Bauer College of Business at the University of Houston and Institute of Product Leadership.

Bauer College’s Cyvia and Melvyn Wolff Center for Entrepreneurship ranked No. 2 in U.S. on the Top 25 Best Undergrad Programs for Entrepreneurs in 2019. (Top 10 since 2007; No. 1 in 2008, 2010 and 2011)

Course audited and approved by the Bauer College.

One Program, Ten Outcomes

#1Discover patterns in Data
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Apply pattern recognition and machine learning techniques to practical applications and detect patterns in the data.
#2Design and Build Algorithms
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Analyze existing algorithms as well as design novel algorithms.
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Use recent ML techniques to evaluate & train models, conduct experiments and develop real-world ML-based applications.
#4Select Appropriate
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Know software and hardware tools that are commonly used to implement and/or support AI and machine learning techniques.
#5Implement AI Frameworks
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Develop systems that process unstructured, uncurated data automatically using AI frameworks.
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Supervised & Unsupervised, Multiple linear & logistic regression.
#7AI integration with Technology
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Develop AI systems to improve business and technology outcomes.
#8Solve Real-World Problems
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Apply critical thinking & problem-solving skills in organizational processes and workflows
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Learn predictive modeling techniques including Neural Networks.
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Move faster and higher in your career by differentiating yourself with AI & ML skills.
Its very simple - those with actionable skills will get promoted faster. End to End Understanding of the business context, customer context and innovation context is key for decision making senior roles.
Amit Phadnis
Chief Digital Officer, GE Healthcare


An executive leadership program created and curated by industry CXOs with case studies, simulations, real-life projects, assignments and personalized coaching.

Core Courses

Data Analytics Foundations

■ What is statistics?
■ Why is statistics relevant to Data Science, Machine Learning and Deep Learning
■ Describe Quantitative Data and methods/graphs
■ Quantitative description measures
■ What is Probability
■ Conditional Probability
■ How to draw a sample
■ Population and Sampling Distributions
■ Chi Square tests and Analysis of Variance

Introduction to DBMS
■ ER diagram
■ Schema design
■ Key constraints & basics of normalization
■ Joins
■ Subqueries involving joins & aggregations
■ Sorting
■ Independent subqueries
■ Correlated subqueries
■ Analytic functions
■ Set operations
■ Grouping and filtering
■ SQL Aggregate & Rank Functions
■ SQL Analytics Functions

Python for Artificial Intelligence & Machine Learning

■ Introduction to Artificial Intelligence (AI), Data Science (DS), Machine Learning (ML), Deep Learning (DL), and Natural Language Processing (NLP)
■ Basics of Data Science: Feature extraction, Feature Engineering, Data Wrangling, Outliers

■ Python Structure, Variables,
■ Conditionals, loops
■ Functions
■ list, dict, tuple, set, bytearray
■ Exceptions handling and raising
■ Python functions, packages and routines
■ Numpy
■ Pandas

Machine Learning Foundations

■ What is Machine Learning
■ How does it work?
■ Supervised Machine Learning
■ Unsupervised Machine Learning
■ Different Machine Learning algorithms.
■ Regression and Classification algorithms
■ Use cases of different types of Machine Learning algorithms
■ Machine Learning Practice Workouts

■ What is regression,
■ Regression algorithms:
■ Simple Linear Regression Statistical method using OLS
■ Programming with Simple Linear Regression Statistical method and Statslib
■ Regression Algorithm Gradient Descent method (incl derivation of Gradients)
■ Programming with Gradient Descent method, Stochastic Gradient Descent, sklearn
■ Overfitting/Bias
■ Non-Linear Regression
■ Programming Assignment on Linear Regression using Gradient Descent Method*

■ What is Logistic Regression? Is it regression or classification?
■ Learning in Logistic Regression
■ Gradient Descent method
■ Programming with Statistical Method using Statslib
■ Programming with Gradient Method using Native Python

■ Reconnecting to Math foundations
■ Different Similarity measures (include Cosine Theta)
■ KNN Algorithm
■ Intro to Sklearn Framework
■ Programming and building a KNN model with Sklearn Framework
■ Programming and building a KNN model with Native Python

Advanced Artificial Intelligence

■ What is Decision Tree
■ How is it constructed?
■ Programming and building a Decision Tree with Sklearn Framework
■ Overfitting Problems in Decision Tree
■ What is Bagging, Random Forest,
■ Hyperparameters in Decision Tree, Random Forest
■ Hyperparameter tuning: Programming and building an optimal Decision Tree using Grid Search, Decision Tree, and Random Forest

■ Adaboost,
■ Gradient Boost,
■ XG Boost
■ Hyperparameters in Adaboost, Gradient Boosting, XGBoosting
■ Hyperparameters tuning: Programming and building an optimal model using Grid Search

■ Review of Unsupervised Learning
■ Clustering
■ K Means Clustering

■ How do you solve an ML problem?
■ Use different models and choose the best or best combination
■ Waterfall or AGILE?
■ A sample problem and code

Deep Learning

■ Problems solved with deep learning
■ What is deep learning
■ Intro to Neural Network
■ Forward propagation in Neural Network

■ Review of Gradient Descent used in Lin and Log Regression
■ Back Propagation in Neural Network
■ Hyperparameters in Neural Network
■ Hyperparameter Tuning
■ Overfitting in Neural Network
■ How to handle Overfitting in Neural Network

■ Introduction to Tensorflow
■ Introduction to keras
■ Build a simple NN using Keras
■ Solve a problem using Keras
■ Hyper-parameter tuning using keras

■ Problems of Vanishing Gradient
■ Handling Overfitting using Dropouts
■ How to handle Vanishing Gradient – Batch Normalization, Skip Connections
■ Solve a practical problem using Neural Network and Keras
■ Issues with Neural Network

Elective Courses (Optional)

Electives are recommended add-on courses for those who want to have a broader coverage

Product Analytics and Metrics

■ Product analytics definition
■ Differences between product and “classic” web analytics
■ Common questions and mistakes

■ Where, when, and how to collect data correctly
■ Data formatting and standards
■ Implications of incorrect data

■ Overview of the tools available
■ Gathering requirements and defining use cases
■ Evaluating tools for your use cases

■ Customer Acquisition Cost, Customer Lifetime Value, Churn Rate
■ Leading vs. lagging metrics
■ Benefits and drawbacks of core metrics
■ SaaS Metrics

■ How to understand users
■ Cohort creation and analysis
■ Sample user-based metrics used in product analytics

■ A selection of metrics for product analytics
■ Setting up reporting and fundamental data visualization principles
■ Setting up monitoring

Social and Web Analytics

■ Which metrics can be monitored
■ Which metrics matter and how they’re related
■ How marketing strategy or editorial decisions are effected by web data
■ Why SEO is relevant

■ Introducing: Social Media Measurement
■ Social Media Analytics: Subscribers, Engagement, Reach, Velocity and Sentiment
■ Measuring Likes and Followers and Subscribers on Facebook, Twitter, Instagram
■ What is Engagement and How to Measure Engagement on Facebook & Twitter
■ Reach – Can Actual Exposure and Reach be Measured on Facebook and Twitter?

■ Post success – impressions, reach, engagement and the difference between them.
■ Understanding Engagement – engagement metrics and how they relate to strategy.
■ Tracking and understanding audiences – who are your followers and why, what is their reach and how it affects strategy.
■ Downloading reports – how to track and measure analytics month-on-month.

■ Tweet impressions v engagement and how to track success.
■ Follower data and how to apply that to strategy.

■ Velocity in Social Media – Facebook Virality and Twitter Trends
■ Marketing Intel and Social Media Measurement: Analytics and Psychographics
■ Measuring ROI (Return on Investment) and the Ecosystem of Apps, widgets, mashups

Typical Learning Path for each course

Weekly Faculty Sessions
Plus Curated Video Lessons
Self & Group Submissions
Capstone Project
Live Presentations with Jury
Course Certificate

Average Days to complete each course

Instructors & Mentors

Learn from India’s leading Product Practitioners.

Manohar Rao
ex. Director,
RainMan Consulting
Amit Sharma
Amit Sharma
Director - Global AI Accelerator,
Yogesh K. Potdar
Executive Leader,
GE Global Research
Manjunath Subramanian
Manjunath Subramanian
Senior Principal Product Manager,
Oracle India

Industry Projects and Assignments

Learn through real-life projects and assignments across industries

Price Match Guarantee
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In this competition, you’ll apply your machine learning skills to build a model that predicts which items are the same products. Your contributions to product matching could support more accurate product categorization and uncover marketplace spam. Customers will benefit from more accurate listings of the same or similar products as they shop. Perhaps most importantly, this will aid you and your fellow shoppers in your hunt for the very best deals.
Song Popularity Challenge
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In this challenge you will use a song’s attributes to predict a track’s ‘popularity’? The better the App can quantify music, the better they can tune their systems and algorithms to generate more revenue for themselves and their stakeholders.The goal is to see whether hit songs shared similar features, and if so, whether those features could be used to predict which songs would be hits in the future.
Cab Aggregator - Supply Demand Gap
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We may have some experience of travelling to and from the airport. We have used different cab services for this travel? Did you at any time face the problem of cancellation by the driver or non-availability of cars? The aim of analysis is to identify the root cause of the problem (i.e. cancellation and non-availability of cars) and recommend ways to improve the situation. As a result of your analysis, you should be able to present to the client the root cause(s) and possible hypotheses of the problem(s) and recommend ways to improve them.
Predicting Used Car Prices
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Deciding whether a used car is worth the posted price when you see listings online can be difficult. Several factors, including mileage, make, model, year, etc. can influence the actual worth of a car. From the perspective of a seller, it is also a dilemma to price a used car appropriately. Determine whether the listed price of a used car is a challenging task, due to the many factors that drive a used vehicle’s price on the market. The focus of this project is developing machine learning models that can accurately predict the price of a used car based on its features, in order to make informed purchases.
Cab Traffic Data Visualization
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Managing traffic problems in the metro cities. Modern cities are changing. The rise of vehicular traffic has been changing the design of our cities. It is very important to know how traffic moves in a city and how it changes during different times in a week. Hence it is very important to analyse and gain insights from traffic data. The challenge is to analyse the traffic data from Bengaluru. The data gives us some information about how traffic moves from source to destination under various circumstances.
Taxi Cab Fare Prediction Machine Learning in Real Time
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Do you remember the days before Uber, Lyft, or Gett? Standing in the street trying to hail a taxi waiting for the moment a free cab might drive by and spot you? These days that world seems so far away. And you might often wonder: how do these apps work? After all, that set price is not a random guess. Given data from past rides, you are required to design the best taxi fare prediction machine learning model.
Stay Listing Price Prediction Challenge
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Predict the price of the listing. Company is an online marketplace for arranging or offering lodging, primarily homestays, or tourism experiences. Help the company’s data science team to predict prices of stay in the US based on attributes present in the dataset.
Stay Listing New User Bookings
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New users on this stay listing platform can book a place to stay in 34,000+ cities across 190+ countries. By accurately predicting where a new user will book their first travel experience, they can share more personalized content with their community, decrease the average time to first booking, and better forecast demand. In this challenge, you are given a list of users along with their demographics, web session records, and some summary statistics. You are asked to predict which country a new user's first booking destination will be. All the users in this dataset are from the USA.
Email Sentiment Analysis
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In this challenge you need to determine the emotional background for a given letter (or a set of letters?). Testing will be performed on prepared sample letters, which will be provided to the teams directly during the final presentation. The results will depend on: 1) quality of the sentiment predicted by the model for given emails (during the presentation); metric - MAE (1 - Very negative, …, 5 - Very positive) 2) idea behind the proposed method 3) quality of the presentation.
Customer Segmentation and Churn Model for Telecom
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Customer acquisition and retention is a key concern for many industries, especially acute in the strongly competitive and quick growth telecommunications industry. The primary goal of churn analysis is usually to create a list of contracts that are likely to be cancelled in the near future. The customers holding these contracts are then targeted with special incentives designed to deter cancellation.

Program Advantage

Strong hand-holding with dedicated support to guide you in your journey in Artificial Intelligence & Machine Learning

Assurance of Skills v/s Knowledge
Assurance of Skills v/s Knowledge
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Unlike eLearning and online leadership courses, our program has continuous faculty interactions with feedback on projects & 1:1 personalized coaching that will build real skills.
Expensive doesnt mean Effective
Expensive doesnt mean Effective
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Unlike Ivy League branded programs which will give you bragging rights, this will actually generate ROI and build YOUR brand.
Focussed on Career Transition
Focussed on Career Transition
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Unlike generic Executive MBA or MDP programs, focus is on building actionable skills for  senior roles in the digital economy
Learning by Doing
Learning by Doing
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Work on real world industry challenges and projects to build skills and present to hiring managers at skillathons.

Career Impact

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Corporate Partners


Corporate Partners



Career Transitions



Highest Salary

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Our Students work at

Boston Consulting Group

Program Fee covers


$ 1299
(Includes $450 admissions fees)
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₹99000 75000
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0% EMI

+ 8 Monthly Payments of ₹8000/mo
  • Start learning today! with a Pay as you go model

Our Students work at

Program Fee covers

Upfront Payment

₹99000 75000
+ GST (Includes ₹30,000 Admission Fee)
  • Start Learning Today! Get maximum flexibility to learn at your own pace.


8 Months (₹8000/mo) @ 0%
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$ 1299
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