Artificial Intelligence Engineering

Name: Artificial Intelligence Engineering
Rating: 4.1 (6 reviews)

Machine Learning | Artificial Intelligence Engineering: From Fundamentals to Deployment

Created byLubula Paul Chikwekwe

Last updated 7/2026

English

English [Auto],

What you'll learn

Understand the intuition of ML algorithms and performing hyperparameter optimization
Understanding of the ML pipeline and its components
Experience with ML and deep learning frameworks
Understanding of and experience in model training, deployment, and operational best practices

Course content

10 sections • 60 lectures • 5h 38m total length

Promo video1:06
Machine Learning and AI Engineering: Course Outline2:09
The History of Machine Learning3:45
Introduction to Machine learning | AI Engineering3:03
The Machine Learning/Artificial Intelligence Pipeline2:55
Quiz: Fundamentals of ML/AI
Role of an ML/AI Engineer9:27
Section A Quiz

Section 1 Completed Wow0:20
Celebrate the completion of section 1 in the artificial intelligence engineering course, highlighting progress and setting the stage for future lessons.
Mathematics for Machine Learning8:42
Master the core maths powering machine learning, from linear algebra with vectors and matrices to calculus with derivatives and gradients, and from probability and statistics to discrete math for algorithms.
Extra Assignment: Logic and Set Theory in Machine Learning
Linear Algebra Essentials2:34
Probability and Statistics Essentials1:47
Calculus for Optimization1:27
Applying Probability, Statistics, and Calculus in Machine Learning
Extra: Advisory Learning Method7:19
Extra: Breakdown of Essential Concepts2:40
Math Concepts in ML

Completed Section0:20
Python Basics and Data Structures2:20
Welcome to Pandas | NumPy | Matplotlib | Seaborn5:00
Explore NumPy, Pandas, Matplotlib, and Seaborn as an interconnected toolkit for data manipulation, analysis, and visualization, with tutorials and documentation guiding beginners through getting started and plotting techniques.
NumPy and Pandas for Data Manipulation2:54
Data Visualization with Matplotlib1:52
Data Visualization with Seaborn1:47
Project: Exploratory Data Analysis33:05

Supervised Learning5:49
Explore supervised learning with labeled data, training and testing phases, and key techniques like linear regression, decision trees, and classification using k nearest neighbors and support vector machines.
Unsupervised Learning10:11
Ensemble Methods (Random Forests, Gradient Boosting)4:10
Hyperparameter Optimization2:33
Quiz: ML Algorithm Selection
Bonus: Algorithm Cheat Sheet2:35
Project: Building and Optimizing ML Models
Bonus: All Machine Learning Algorithms Explained in 30 min30:50
Extra: Entire End to End Python Project33:29
Build an end-to-end movie recommendation model using cosine similarity and content-based filtering in Python, with TF-IDF features and exploratory data analysis in Google Colab.

Wow still here ?0:20
Neural Network Fundamentals6:47
Explore deep learning, a core AI subset, using neural networks to automatically learn features from images, text, and audio, and apply them to image classification, NLP, and speech recognition.
Convolutional Neural Networks (CNNs)2:50
Recurrent Neural Networks (RNNs) and LSTMs2:55
Transfer Learning and Fine-tuning2:45
Project: Image Classification with CNNs
Deep Learning Quiz (RNN,LSTMs,CNN etc)

Completed section!0:20
Explore the completed section of artificial intelligence engineering, focusing on the concepts covered within this portion of the course.
Bonus Content: System Design7:45
In this article, we will explore detailed step-by-step approach to tackle system design problems effectively in an interview setting.
Data Collection and Preprocessing2:16
Feature Engineering and Selection3:08
Model Training and Evaluation3:30
Model Interpretation and Explainability3:37
Extra Assignment: Machine Learning Pipeline
Part 1: Building A Movie Recommendation Model1:03:59
Build an end-to-end movie recommendation model using collaborative filtering, loading and preprocessing a movies dataset, creating a user-movie matrix, computing cosine similarities, and generating recommendations.
Part 2: End to End ML Pipeline (Improving the Movie Recommendation System)

Well Done !!!!0:20
Artificial intelligence engineering course overview references the well done lecture title to emphasize core concepts in artificial intelligence engineering.
Introduction to MLOps3:19
Model Versioning and Experiment Tracking4:02
Containerization with Docker3:00
Deployment on Cloud Platforms (AWS, GCP, Azure)3:54
Monitoring and Maintaining ML Models in Production3:46
Advanced Project: Deploying and Monitoring an ML Model
MLOps and Deployment

Completed section!0:20
Review key ideas from the completed section of artificial intelligence engineering, reinforcing core concepts and their practical implications.
Introduction to LLMs and Foundation Models3:37
Fine-tuning Pre-trained Models3:32
Prompt Engineering and Few-shot Learning4:03
Extra: Transformers3:34
Ethical Considerations in AI3:09
Reinforcement Learning3:38
Fine-tuning and Deploying an LLM
LLM, Foundation Models, Fine-tuning and Deploying an LLM

Design, implement, and deploy an end-to-end AI solution3:24
Udemy Assignment: Exploring the Depths of Machine Learning and AI
Final Quiz: THE FINAL BOSS
Completed Course !!!0:20
Explore the completed course in artificial intelligence engineering, showcasing the journey of learning and the current status of the program for prospective learners.
Bonus: Entire AI Engineer Roadmap
Entire roadmap for Ai Engineer, go to https://roadmap.sh/ for more. This is only the start of your journey

Requirements

This course is designed to accommodate learners with varying levels of experience, including beginners. While there are no strict prerequisites, having a basic understanding of programming concepts and familiarity with Python would be beneficial
Interest in Data Science, AI and Machine Learning
Desire to Learn and research

Description

This in-depth course is tailored for individuals aiming to become Machine Learning and AI Engineers. It encompasses the full ML pipeline, from basic principles to sophisticated deployment techniques. Participants will engage in hands-on projects and study real-world scenarios to acquire practical skills in creating, refining, and implementing AI technologies.

The Udemy course for Machine Learning and AI Engineering is structured around the roles and responsibilities within the field. It provides a thorough exploration of all essential aspects, such as ML algorithms, the ML pipeline, deep learning frameworks, model training, deployment, and best practices for operations.

Organized into 11 comprehensive sections, the course begins with the basics and gradually tackles more complex subjects. Each section is comprised of several lessons, practical projects, and quizzes to solidify the concepts learned.

Here are some key features of the course:

Comprehensive coverage: The course covers everything from basic math and Python skills to advanced topics like MLOps and large language models.
Hands-on projects: Each major section includes a practical project to apply the learned concepts.
Industry relevance: The course includes sections on MLOps, deployment, and current trends in AI, preparing students for real-world scenarios.
Practical skills: There's a strong focus on practical skills like hyperparameter optimization, model deployment, and performance monitoring.
Ethical considerations: The course includes a discussion on AI ethics, an important topic for AI engineers.
Capstone project: The course concludes with a multi-week capstone project, allowing students to demonstrate their skills in a comprehensive manner.

Who this course is for:

Students and Professionals
Beginners in AI, Machine learning and Data Science
Self-Learners and Lifelong Learners
Professionals Seeking Career Advancement

Artificial Intelligence Engineering

What you'll learn

Explore related topics

Course content

Introduction6 lectures • 22min

Mathematics for Machine Learning7 lectures • 25min

Section 3: Python for Machine Learning7 lectures • 47min

Machine Learning Algorithms7 lectures • 1hr 30min

Deep Learning and Neural Networks5 lectures • 16min

ML/AI Frameworks and Tools5 lectures • 10min

The ML Pipeline7 lectures • 1hr 25min

MLOps and Deployment6 lectures • 18min

Large Language Models and Foundation Models7 lectures • 22min

Section 10: Capstone Project3 lectures • 4min

Requirements

Description

Who this course is for: