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Assignments & Projects
Recommended 6-8 hrs/ week
Next Cohort Starts
With 1:1 Skill Coaches
Key Highlights
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
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.
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.
Syllabus
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
Average Days to complete each course
Instructors & Mentors
Learn from India’s leading Product Practitioners.
Industry Projects and Assignments
Learn through real-life projects and assignments across industries
Program Advantage
Strong hand-holding with dedicated support to guide you in your journey in Artificial Intelligence & Machine Learning
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