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Master in AI Engineering

Name: Master in AI Engineering
Rating: 4.3 (356 reviews)

Become AI Engineer by mastering problem definition, data preparation, algorithm selection / development and deployment

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

Last updated 7/2026

English

English [Auto],

What you'll learn

Build a clear roadmap to become a successful AI Engineer
Understand the complete AI engineering lifecycle from business problem to production deployment
Build and deploy end-to-end AI solutions using industry best practices
Learn how to define AI problems aligned with business goals and real-world use cases
Master data collection, cleaning and preprocessing for AI models
Build strong feature engineering skills to improve model performance T
Learn how to select, develop, evaluate and optimize AI/ML algorithms for real-world problems
Deploy AI models into production and monitor their performance
Learn how to build and use AI Agents and modern AI systems
Understand how AI Engineers collaborate with data scientists, software engineers and business stakeholders

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

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

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

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

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

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

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

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

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

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

Requirements

Willing to spend 8+ hours learning about Artificial Intelligence

Description

Do you want to become an AI Engineer but feel overwhelmed by machine learning, data preparation, algorithm selection, deployment, monitoring, and the rapidly evolving AI landscape?

Many courses teach individual AI topics. Some focus only on machine learning, while others concentrate on AI Agents, LLMs, or automation tools. However, successful AI Engineers understand the complete engineering lifecycle—from identifying the right business problem to building, deploying, monitoring, and continuously improving AI solutions.

This course is designed to help you understand that complete journey.

What Makes This Course Different?

This course is built around how AI Engineers actually work in real-world projects.

Rather than teaching isolated concepts, it provides a structured framework that follows the complete AI Engineering lifecycle—from problem definition to deployment and monitoring.

You will learn how to:

• Understand business problems before building AI solutions

• Collect, clean, and prepare high-quality data

• Select and develop appropriate AI and machine learning algorithms

• Build and optimize machine learning models

• Apply feature engineering to improve model performance

• Deploy AI models into production environments

• Monitor, maintain, and continuously improve deployed AI systems

• Understand ethical and responsible AI practices

• Build a structured roadmap to become an AI Engineer

What You Will Learn

Throughout this course, you will master every major stage of the AI Engineering lifecycle.

Problem Definition

Learn how successful AI Engineers understand stakeholder requirements, identify business opportunities, and define the right AI problems before development begins.

Data Collection and Preparation

Learn how to collect, clean, preprocess, and organize data to build reliable and high-performing AI solutions.

Algorithm Selection and Development

One of the most important responsibilities of an AI Engineer is selecting the right algorithm for the right problem.

You will learn how to:

• Select appropriate AI and machine learning algorithms

• Design AI solutions for different business problems

• Train and optimize machine learning models

• Evaluate competing models

• Improve prediction accuracy and model performance

Feature Engineering

Discover practical techniques for identifying, creating, and selecting meaningful features that improve AI model accuracy and performance.

Deployment

Learn how trained AI models are deployed into production environments where they create real business value.

Monitoring and Maintenance

AI Engineering does not end after deployment.

Understand how AI Engineers monitor model performance, detect model drift, maintain production systems, and continuously improve deployed AI solutions.

Collaboration

Learn how AI Engineers collaborate with business leaders, data scientists, software engineers, product managers, and other stakeholders throughout AI projects.

Research and Innovation

Explore emerging AI technologies, AI Agents, automation concepts, and innovations shaping the future of AI Engineering.

Responsible AI

Understand how to develop AI solutions that are fair, transparent, accountable, and aligned with responsible AI principles.

Why Learn AI Engineering?

AI Engineering is one of the fastest-growing career paths in technology.

Organizations need professionals who can do much more than build machine learning models. They need AI Engineers who understand the complete process of defining business problems, preparing data, selecting algorithms, developing models, deploying AI solutions, monitoring performance, and continuously improving systems.

This course provides a structured learning path to help you develop that complete understanding.

Who Should Take This Course?

This course is ideal for:

• Aspiring AI Engineers

• Software Engineers

• Machine Learning Engineers

• Data Scientists

• Data Analysts

• Technology Professionals transitioning into AI

• Students interested in building a career in Artificial Intelligence

• Anyone who wants to understand the complete AI Engineering lifecycle

Why Learn From Me?

My goal is not simply to teach AI concepts.

My objective is to help you understand how AI Engineers think, solve problems, and build complete AI solutions.

The course combines structured frameworks, practical examples, real-world engineering workflows, and step-by-step explanations to help you build a strong foundation in AI Engineering.

Start Your AI Engineering Journey

If you want to become an AI Engineer who understands the complete AI Engineering lifecycle—from problem definition and data preparation to algorithm development, deployment, monitoring, and continuous improvement—this course will provide the structured learning path you need.

Whether you are starting your AI career or expanding your existing technical skills, this course will help you build the knowledge, confidence, and engineering mindset required to succeed.

Enroll today and take the next step toward becoming 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 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: