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Master in Strategic AI Engineering

Name: Master in Strategic AI Engineering
Rating: 4.3 (357 reviews)

Become an AI Engineer through a complete roadmap with real-world assignments from problem definition to ethical AI

Created byArun Singhal B-Tech MBA With 45+ Exp with Unilever, J&J, Danone, etc.

Last updated 8/2026

English

English [Auto],

What you'll learn

Follow a complete roadmap to become an AI Engineer—from identifying a real-world problem to building, deploying, monitoring and improving an AI solution
Apply each stage of the AI Engineering journey through real-world assignments that progressively develop your own AI solution
Learn how to work across problem definition, data, algorithm selection, feature engineering, deployment and continuous improvement
Define the right AI problem by understanding business needs, stakeholders, objectives and the criteria for AI success
Prepare and work with data for AI, including understanding data requirements, quality, preparation and the role of data in AI solutions
Select the right AI approach and algorithms by comparing alternatives based on the problem, data, performance and practical requirements
Develop and improve AI models by understanding model development, evaluation, experimentation and performance improvement
Use feature engineering to identify, create and improve the inputs that help AI models solve real-world problems
Plan the deployment of an AI solution and understand the practical requirements for moving an AI model into real-world use
Monitor AI performance after deployment and identify when changing data, performance issues or other problems require action
Work effectively with the people involved in developing AI solutions, including business stakeholders, data professionals and technical teams
Research and test new approaches to improve AI performance, efficiency or scalability using hypotheses, experiments and evidence-based decisions
Identify and address bias, privacy, transparency, accountability and other ethical risks when developing and deploying AI

Course content

14 sections • 67 lectures • 8h 45m total length

Introduction6:33
Introduction to the instructor and course
Roadmap to become AI Engineer7:10
At the end of this lecture, you will learn the following
•How to become a successful AI Engineer
Introduction Case Study11:10
•A challenging, realistic, and deeply insightful case study designed for the learners who want to become successful Artificial Intelligence (AI) Engineers
My AI Engineer Career Plan

Overview I6:23
At the end of this lecture, you will learn the following
What is Artificial Intelligence?
Overview I
What is Artificial Intelligence and career opportunities in this field6:21
At the end of this lecture, you will learn the following
•What is Artificial Intelligence and career opportunities in this field
Overview II
Overview III- What are the responsibilities of a AI Engineer?8:49
At the end of this lecture, you will learn the following
•What are the responsibilities of a AI Engineer?
Overview III
My AI Engineer Opportunity and Role

Problem Definition I - How to understand stakeholders' needs and define problems7:12
At the end of this lecture, you will learn the following
•How to understand stakeholders' needs and define problems that can be addressed using artificial intelligence and machine learning techniques
Problem Definition I
Problem Definition II- How to understand stakeholders' needs and define problems6:49
At the end of this lecture, you will learn the following
•How to understand stakeholders' needs and define problems that can be addressed using artificial intelligence and machine learning techniques
Problem Definition II
An example of understanding stakeholders' needs and define problems6:07
At the end of this lecture, you will learn the following
•An example of understanding stakeholders' needs and define problems that can be addressed using artificial intelligence and machine learning technique
Problem Definition III
An example of understanding stakeholders' needs and define problems5:56
At the end of this lecture, you will learn the following
•An example of understanding stakeholders' needs and define problems that can be addressed using artificial intelligence and machine learning technique
Problem Definition IV
Define Your AI Problem

Data Collection & Preprocessing I7:43
At the end of this lecture, you will learn the following
•How to gather relevant data from various sources, ensure its quality, and preprocess it to make it suitable for analysis and modeling
Data Collection & Preprocessing I
Standardizing data formats13:09
At the end of this lecture, you will learn the following
How to gather relevant data from various sources, ensure its quality, and preprocess it to make it suitable for analysis and modeling
Data Collection & Preprocessing II
Feature extraction11:46
At the end of this lecture, you will learn the following
•How to gather relevant data from various sources, ensure its quality, and preprocess it to make it suitable for analysis and modeling
Data Collection & Preprocessing III
Image feature extraction algorithms8:02
At the end of this lecture, you will learn the following
•How to gather relevant data from various sources, ensure its quality, and preprocess it to make it suitable for analysis and modeling
Data Collection & Preprocessing IV
An example of gathering relevant data from various sources, ensure its quality9:32
At the end of this lecture, you will learn the following
•An example of gathering relevant data from various sources, ensure its quality, and preprocess it to make it suitable for analysis and modeling
Data Collection & Preprocessing V
An example of gathering relevant data from various sources, ensure its quality10:06
At the end of this lecture, you will learn the following
•An example of gathering relevant data from various sources, ensure its quality, and preprocess it to make it suitable for analysis and modeling
Data Collection & Preprocessing VI
Prepare Data for Your AI Solution

Algorithm Selection & Development I- Problem Understanding and Formulation8:35
At the end of this lecture, you will learn the following
How to research, select, and develop appropriate machine learning algorithms or deep learning architectures based on the problem at hand and the available data?
Algorithm Selection & Development I
How to determine type of output and evaluation metrices?10:07
At the end of this lecture, you will learn the following
•How to research, select, and develop appropriate machine learning algorithms or deep learning architectures based on the problem at hand and the available data?
Algorithm Selection & Development II
How to determine type of output and evaluation metrices- Contd6:44
At the end of this lecture, you will learn the following
•How to determine type of output and evaluation metrices- Regression and Clustering
Algorithm Selection & Development III
Algorithm Selection & Development IV6:58
At the end of this lecture, you will learn the following
•How does Silhouette Score measures how similar an object is to its own cluster compared to other clusters
Algorithm Selection & Development IV
Davies-Bouldin Index6:32
At the end of this lecture, you will learn the following
•How does Davies-Bouldin Index compute the average similarity between each cluster and its most similar cluster
Algorithm Selection & Development V
Adjusted Rand Index (ARI) and Adjusted Mutual Information (AMI)8:44
At the end of this lecture, you will learn the following
•How to does Adjusted Rand Index (ARI) and Adjusted Mutual Information (AMI) measure the agreement between true labels and cluster assignments
Algorithm Selection & Development VI
Data Understanding and Preparation •Researching Algorithms and Architectures6:55
At the end of this lecture, you will learn the following
•Data Understanding and Preparation
•Researching Algorithms and Architectures
•Model Selection and Evaluation
Algorithm Selection & Development VII
Learning rate in gradient descent hyperparameter7:05
At the end of this lecture, you will learn the following
•Learning rate in gradient descent hyperparameter
Algorithm Selection & Development VIII
Number of hidden layers in a neural network hyperparameter5:07
At the end of this lecture, you will learn the following
•Number of hidden layers in a neural network hyperparameter
Algorithm Selection & Development IX
Hyperparameter tuning18:24
At the end of this lecture, you will learn the following
•Hyperparameter tuning
Algorithm Selection & Development X
How to compare the performance of different models and architectures6:03
At the end of this lecture, you will learn the following
•How to compare the performance of different models and architectures to identify the most effective ones
Algorithm Selection & Development XI
How to Iterate on the model development process by fine-tuning hyperparameters7:45
At the end of this lecture, you will learn the following
•How to Iterate on the model development process by fine-tuning hyperparameters
Algorithm Selection & Development XII
Regularization, dropout, batch normalization, and learning rate scheduling14:13
At the end of this lecture, you will learn the following
•How to use techniques like regularization, dropout, batch normalization, and learning rate scheduling to improve model generalization and performance
Algorithm Selection & Development XIII
How to monitor and analyze model training/validation metrics7:32
At the end of this lecture, you will learn the following
•How to monitor and analyze model training/validation metrics
Algorithm Selection & Development XIV
How to consider the interpretability and explainability of the selected models8:06
At the end of this lecture, you will learn the following
•How to consider the interpretability and explainability of the selected models
Algorithm Selection & Development XV
How to train Decision trees algorithm for getting feature importance4:35
At the end of this lecture, you will learn the following
•How to train Decision trees algorithm for getting feature importance
Algorithm Selection & Development XVI
How to train Random Forests algorithm for getting feature importance4:00
•How to train Random Forests algorithm for getting feature importance
Algorithm Selection & Development XVII
How to train Gradient boosting machines algorithm for getting feature importance4:27
At the end of this lecture, you will learn the following
How to train Gradient boosting machines algorithm for getting feature importance
Algorithm Selection & Development XVIII
How to use feature importance analysis to provide insights into model prediction8:46
At the end of this lecture, you will learn the following
•How to use feature importance analysis to provide insights into model predictions
Algorithm Selection & Development XIX
Model Interpretability Methods8:34
At the end of this lecture, you will learn the following
•What are Model Interpretability Methods to consider the interpretability and explainability of the selected models
Algorithm Selection & Development XX
Attention mechanisms in deep learning models7:51
At the end of this lecture, you will learn the following
•What are attention mechanisms in deep learning models to consider the interpretability and explainability of the selected models
Algorithm Selection & Development XXI
Model deployment and continuous monitoring16:50
At the end of this lecture, you will learn the following
•Deploy the trained model in a production environment and integrate it into the application workflow.
•Implement monitoring and logging mechanisms to track model performance, drift, and errors over time.
•Continuously evaluate and update the model as new data becomes available or the problem requirements change
Algorithm Selection & Development XXII
An example of researching, selecting, and developing appropriate algorithm8:16
At the end of this lecture, you will learn the following
•An example of researching, selecting, and developing appropriate machine learning algorithms or deep learning architectures based on the problem at hand and the available data
Select Your AI Approach

Feature Engineering I- How to identify and extract meaningful features5:17
At the end of this lecture, you will learn the following
•How to identify and extract meaningful features from the data to improve the performance of machine learning models
Feature Engineering I
How to engineer new features or transform existing features16:10
At the end of this lecture, you will learn the following
How to engineer new features or transform existing features
Feature Engineering II
How to select a subset of the most relevant features9:56
At the end of this lecture, you will learn the following
How to select a subset of the most relevant features
Feature Engineering III
How to reduce the dimensionality of the feature space8:33
At the end of this lecture, you will learn the following
How to reduce the dimensionality of the feature space while preserving as much relevant information as possible
Feature Engineering IV
Feature Engineering V- Remaining steps of feature engineering6:14
At the end of this lecture, you will learn the following
Remaining steps of feature engineering
Feature Engineering V
An example of identifying and extracting meaningful features7:24
At the end of this lecture, you will learn the following
An example of identifying and extracting meaningful features from a dataset to improve the performance of a machine learning model
Engineer Better Features

Deployment I7:05
•How to deploy trained models into production environments, ensuring they integrate smoothly with existing systems and meet performance requirements- Model Serialization
Deployment I
Deployment II6:32
•How to deploy trained models into production environments, ensuring they integrate smoothly with existing systems and meet performance requirements- Remaining steps
Deployment II
An example of deploying trained models into production environments5:53
•An example of deploying trained models into production environments, ensuring they integrate smoothly with existing systems and meet performance requirements
Deploy Your AI Solution

Monitoring and Maintenance I4:30
•How to monitor the deployed models to ensure they continue to perform well over time, and update or retrain them as needed to adapt to changing conditions or requirements
Monitoring and Maintenance I
How to use statistical tests, visualization techniques, or drift detection algor7:53
At the end of this lecture, you will learn the following
How to use statistical tests, visualization techniques, or drift detection algorithms to identify data drift
Monitoring and Maintenance II
Model Drift Detection7:47
At the end of this lecture, you will learn the following
Model Drift Detection
Monitoring and Maintenance III
Monitoring and Maintenance IV4:49
•How to monitor the deployed models to ensure they continue to perform well over time, and update or retrain them as needed to adapt to changing conditions or requirements
Monitoring and Maintenance IV
An example of how to monitor the deployed models6:20
•An example of how to monitor the deployed models to ensure they continue to perform well over time, and update or retrain them as needed to adapt to changing conditions or requirements
Monitor and Maintain Your AI Model

Collaboration I4:15
At the end of this lecture, you will learn the following
•How to collaborate with data scientists, software engineers, and domain experts to develop comprehensive AI solutions that address real-world problems effectively
Collaboration I
Collaboration II4:08
At the end of this lecture, you will learn the following
•How to collaborate with data scientists, software engineers, and domain experts to develop comprehensive AI solutions that address real-world problems effectively
Collaboration II
An example of collaborating with data scientists, software engineers, and....7:01
At the end of this lecture, you will learn the following
•An example of collaborating with data scientists, software engineers, and domain experts to develop comprehensive AI solutions that address real-world problems effectively
Build Your AI Collaboration Plan

Research and Innovation I4:58
At the end of this lecture, you will learn the following
How to conduct research to explore new techniques and methodologies that could improve the performance or efficiency of AI system
Research and Innovation I
Research and Innovation II4:57
At the end of this lecture, you will learn the following
•How to conduct research to explore new techniques and methodologies that could improve the performance or efficiency of AI systems
Research and Innovation II
An example of conducting research to explore new techniques & methodologies6:48
At the end of this lecture, you will learn the following
•An example of conducting research to explore new techniques and methodologies that could improve the performance or efficiency of AI systems
Research and Improve Your AI Solution

Requirements

Willing to spend 8+ hours learning about Artificial Intelligence

Description

Can you truly become an AI Engineer by simply learning AI concepts and tools?

This course takes a different approach: you don't just learn AI Engineering—you apply it.

You will become an AI Engineer through a complete roadmap, applying what you learn through real-world assignments at every major stage of the AI Engineering journey. Each assignment builds on the previous one, progressively developing your own AI solution—from identifying the right problem and preparing data to selecting the AI approach, developing and deploying the solution, monitoring its performance, improving it, and addressing ethical risks.

Instead of learning isolated AI topics, you will learn how the different stages fit together and how an AI Engineer applies them to a real-world problem.

From Problem Definition to Ethical AI

Your journey begins with one of the most important AI Engineering decisions: defining the right problem.

You will learn how to understand business needs, stakeholders, objectives and success criteria before deciding whether and how AI should be used.

You then progress through the key stages of the AI Engineering lifecycle:

Problem Definition → Data → AI Approach & Algorithms → Model Development → Feature Engineering → Deployment → Monitoring → Improvement → Ethical AI

At each stage, you will learn the relevant concepts and then put them into practice through an assignment. This creates a connected learning experience rather than a collection of unrelated exercises.

Learn It. Apply It. Build on It.

The practical assignments are central to the course.

You will not simply answer questions about AI Engineering. You will make decisions for your own AI solution and build on those decisions as you progress.

You will:

Define a real-world AI problem and determine what success should look like.
Identify the data your solution needs and determine how it should be prepared.
Evaluate possible AI approaches and select appropriate algorithms.
Plan model development, evaluation and improvement.
Engineer better features to strengthen the inputs to your AI model.
Plan how your AI solution can be deployed for real-world use.
Determine what should be monitored after deployment and how problems can be identified.
Develop a collaboration approach for working with business and technical teams.
Research and test new approaches to improve AI performance, efficiency or scalability.
Identify and address bias, privacy, transparency, accountability and other ethical risks.

Every major assignment moves your AI solution one step further.

This progressive application is the heart of the course.

Why a Complete AI Engineering Roadmap Matters

An AI Engineer needs to look beyond an individual model or technology.

The real challenge is knowing what to do before the model is built, how to make the right technical choices, what happens when the solution goes into use, and how to improve it responsibly over time.

That is why this course covers the complete journey.

You will develop the ability to connect business needs, data, AI approaches, model development, feature engineering, deployment, monitoring, collaboration, research and responsible AI into one coherent AI Engineering process.

What You Will Gain

By completing the course, you will have developed a structured way to approach AI Engineering—from the initial problem through the lifecycle of an AI solution.

More importantly, you will have practised applying that approach through real-world assignments, rather than only learning the theory behind it.

Don't just learn AI concepts. Learn how to apply AI Engineering across the complete journey from problem to ethical AI.

Start your journey to become an AI Engineer.

This Course is Part of a Structured Learning Path

Learning Path: TECHNOLOGY PATH (Starter → Builder → Advanced)

This course is your ADVANCED step.

Next Recommended Courses

After completing this course, continue your growth with:

How to become Software Developer (Starter)

Software Development Excellence (Builder)

End to end Solution Design (Builder)

Solution Architecture (Builder)

IT Product Management (Advanced)

Generative AI (Advanced)

Who this course is for:

Aspiring AI Engineers and Machine Learning Engineers who want to learn the complete end-to-end AI development process
Aspiring AI Engineers and Machine Learning Engineers who want to learn the complete end-to-end AI development process
Software Engineers and Developers who want to build and deploy AI-powered applications
Software Engineers and Developers who want to build and deploy AI-powered applications
Professionals looking to upskill in AI, ML, and modern AI systems (including AI agents)
Product Managers and Tech Leaders who want to understand how AI solutions are designed, built, and scaled
Entrepreneurs and innovators who want to apply AI to solve real business problems
Anyone who wants a structured, practical roadmap to move from beginner to AI Engineer

Master in Strategic AI Engineering

What you'll learn

Explore related topics

Course content

Introduction3 lectures • 25min

Overview3 lectures • 22min

Problem Definition4 lectures • 26min

Data Collection & Preprocessing6 lectures • 1hr

Algorithm Selection & Development23 lectures • 3hr 12min

Feature Engineering6 lectures • 54min

Deployment3 lectures • 20min

Monitoring and Maintenance5 lectures • 31min

Collaboration3 lectures • 15min

Research and Innovation3 lectures • 17min

Requirements

Description

Who this course is for: