[100% Off] Full-Stack Ai Engineer 2026–Machine Learning Foundations - I

Build strong Machine Learning foundations with Python, real projects, and a Full-Stack AI Engineer mindset

Added on January 16, 2026 Development 2 min read

What you’ll learn

• Build end-to-end machine learning pipelines from data preprocessing to model evaluation using industry best practices
• Apply supervised
• unsupervised
• and ensemble ML algorithms to solve real-world regression
• classification
• and clustering problems
• Prevent common ML failures by handling data leakage
• feature scaling
• encoding
• and cross-validation correctly.
• Optimize model performance using feature selection
• hyperparameter tuning
• and proper evaluation metrics.
• Write clean
• reusable
• and production-ready ML code with reproducible workflows and pipelines.
• Think like an ML engineer and design models that scale beyond notebooks into real-world systems.

Requirements

• Basic Python knowledge (variables
• loops
• functions) is helpful but not required
• No prior machine learning or statistics experience needed
• A computer with internet access (Windows
• macOS
• or Linux)
• Willingness to learn and practice with real-world datasets

Description

“This course contains the use of artificial intelligence”

This course is Part 1 of the Full-Stack AI Engineer series, designed to help you build strong Machine Learning foundations before moving into Deep Learning and Generative AI.

You will start by understanding what a Full-Stack AI Engineer does, how modern AI systems are built end-to-end, and where Machine Learning fits in real-world applications. From there, the course walks you step by step through Python for Machine Learning, data analysis, and exploratory data analysis (EDA)—the most critical skills for building reliable AI models.

You’ll learn how to design and train supervised learning models including regression and classification, understand how algorithms actually work (not just how to use them), and evaluate models using industry-standard performance metrics. You’ll also explore ensemble methods like Random Forests and Gradient Boosting to improve accuracy and robustness.

Beyond modeling, the course focuses heavily on feature engineering, model optimization, cross-validation, and hyperparameter tuning, helping you turn basic models into production-ready Machine Learning pipelines. You’ll also gain practical experience with unsupervised learning, including clustering and dimensionality reduction, to uncover hidden patterns in data.

Throughout the course, you’ll work on hands-on exercises, mini-projects, and a capstone Machine Learning project that demonstrates your ability to build an end-to-end ML solution—from raw data to final insights. This project is designed to be resume-ready and serves as a strong foundation for advanced AI work.

By the end of this course, you will think like an AI Engineer, write clean and scalable ML code, and be fully prepared to continue into Deep Learning, LLMs, and Generative AI system design in the next courses of the series.