AI Engineer Full Course | 16+ Hours | Beginner to Pro | Build Real AI Projects

This complete 16+ hour AI Engineer course covers everything you need to master modern AI—from the fundamentals to building real-world AI applications. Whether you’re a beginner, student, software developer, or working professional, this course will help you build a strong foundation in AI Engineering using the latest tools, frameworks, and industry practices.

Free Download 500+ Pages AI Engineer Notes from here –

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📚 What You’ll Learn

✅ Python for AI
✅ Machine Learning Fundamentals
✅ Deep Learning
✅ Generative AI
✅ Large Language Models (LLMs)
✅ Prompt Engineering
✅ AI Agents
✅ LangChain
✅ RAG (Retrieval-Augmented Generation)
✅ MCP (Model Context Protocol)
✅ AI Automation
✅ AI Workflows
✅ Vector Databases
✅ Model Deployment
✅ Real-World AI Projects
✅ AI Engineer Roadmap

Time Stamp
00:00:00 – AI Engineer Course Introduction
00:07:06 – AI Engineer Project Challenges
00:15:51 – Evolution of AI & AI Stages
00:22:41 – Generative AI Model
00:41:11 – Prompt Engineering
01:30:56 – Core Prompting Techniques
02:12:51 – Anaconda Navigator Installation
02:24:31 – Variables & Operators
02:44:02 – Built in Functions
03:06:28 – Control Flow Statements
03:34:35 – Loops in Python
03:54:14 – Use Defined Functions
04:16:40 – Strings in Python
04:36:00 – List in Python
04:57:36 – Tuples in Python
05:08:22 – Dictionary
05:31:45- Sets
05:38:40 – Numpy Library
05:57:05 – Pandas Library
06:31:26 – Matplotlib Library
07:10:13 – Voice Assistant AI Project
07:18:59 – Statistics
08:11:39 – Mean , Mode ,Median
08:32:06 – Scipy Library
08:40:52 – Measure of Disperssion
09:02:20 – Sample Variance
09:20:51 – Normal & Gaussian Distribution
09:40:20 – Uniform Distribution
09:48:09 – Inferential Statistics
09:56:56 – Hypothesis Testing Mechanism
09:58:00 – Z,P,Anova Test
10:35:58 – Machine Learning full course
10:51:32 – Machine Learning Types
11:03:14 – Supervised Machine Learning
11:04:41 – Unsupervised Machine Learning
11:05:23 – Reinforcement Learning
11:20:13 – Linear Regression
11:37:04 – Bias vs.Variance Trade-off
11:45:56 -Ridge Regression
12:00:00 – Lasso Regression
12:10:55 – Logistic Regression Algorithm
12:21:50 – Introduction to Logistic Regression
12:32:45 – Sigmoid Function Mathematics
12:43:40 – Linear Regression Line
12:54:35 – Visualizing the Confusion Matrix with Heatmaps
13:05:30 – Naive Bayes Algorithm Fundamentals
13:16:25 – Dependent and Independent Events
13:27:20 – Bayes Theorem Formula Construction
13:38:15 – Gaussian Naive Bayes Implementation
13:49:10 – K-Nearest Neighbors (KNN) Theory
14:00:05 – Manhattan vs. Euclidean Distance
14:11:00 – Decision Tree Algorithm Structure
14:21:55 – Unsupervised Algorithms
14:32:50 – Ensemble Techniques: Bagging vs. Boosting
14:43:45 – Bootstrap Aggregating
14:54:40 – Random Forest Algorithm
15:05:35 – AdaBoost Algorithm
15:16:30 – K-Means Clustering Algorithm
15:27:25 – The Elbow Method for Optimal Clusters
15:38:20 – Hierarchical Clustering and Dendrograms
15:49:15 – DBSCAN: Density-Based Clustering
16:00:00 – Final Summary

🎯 Who Is This Course For?

✔ Complete Beginners
✔ Students & Freshers
✔ Software Developers
✔ Data Analysts & Data Scientists
✔ AI & Machine Learning Enthusiasts
✔ Anyone Looking to Build a Career in AI

💼 By the End of This Course, You’ll Be Able To

🚀 Build AI-powered applications
🚀 Create AI Agents and LLM-based solutions
🚀 Develop real-world AI projects from scratch
🚀 Understand modern AI frameworks and workflows
🚀 Build a strong portfolio for AI Engineer roles

✨ Kickstart your career in a Data Analyst. Apply today! –

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