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Applied Machine Learning with Hands-On Projects
Gain insights into developing and deploying machine learning solutions. Delve into the full lifecycle of data science projects, from raw data ingestion to production-ready APIs.
51 lessons
5 projects
8h
Created by Khayyam Hashmi
What's included
- 51 lessons across 6 modules
- 1 quizzes with instant feedback
- 5 projects in preconfigured environments
- AI Tutor on every lesson
Why this course
- Implement Data Quality SolutionsIdentify and resolve data quality issues to ensure high-quality datasets for machine learning projects.
- Build and Evaluate Regression ModelsDevelop regression models using scikit-learn and confidently interpret model coefficients and evaluation metrics.
- Apply Classification TechniquesUtilize classification algorithms to solve real-world problems and assess model performance using confusion matrices.
- Conduct Customer SegmentationLeverage unsupervised learning techniques to segment customers and derive actionable insights for business strategies.
- Optimize Ensemble Learning ModelsImplement ensemble methods to enhance model accuracy and prevent overfitting through effective hyperparameter tuning.
- Deploy Machine Learning APIsTransform machine learning models into production-ready APIs, ensuring robust input validation and error handling.
Who wrote it
Khayyam Hashmi
Computer scientist and Generative AI and Machine Learning specialist. VP of Technical Content @ educative.io.
Dr. Khayyam Hashmi is a seasoned computer scientist and Generative AI and Machine Learning specialist. As a researcher, educator, and the VP of Technical Content at Educative, he is dedicated to making complex computing concepts accessible for learners worldwide. With a PhD in Computer Science from Wayne State University and academic publications in service-oriented systems and intelligent computing, Khayyam excels at fusing an academic perspective with practical software engineering insights. Khayyam’s courses, writing, and research spans technical topics including adaptive AI systems, neural networks, Intelligent tutoring systems, and production-ready software design.
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What You'll Learn
- Identify data quality issues and apply techniques for handling missing values, duplicates, and outliers.
- Implement regression models using scikit-learn, including linear regression and evaluation metrics.
- Apply classification techniques such as logistic regression and decision trees, and evaluate model performance.
- Utilize unsupervised learning methods like k-means clustering and dimensionality reduction for customer segmentation.
- Employ ensemble learning strategies, including random forests and gradient boosting, with a focus on hyperparameter tuning.
- Deploy machine learning models to production by building APIs with FastAPI and ensuring input validation and error handling.
Learning Roadmap
1.
Data Preparation Fundamentals
Data Preparation Fundamentals
This comprehensive course provides a practical, end-to-end guidance for developing and deploying machine learning solutions. Designed for practitioners, the cou
2.
Regression for Prediction
Regression for Prediction
Master machine learning fundamentals, regression techniques, and model evaluation for impactful business insights.
Introduction to Machine LearningWhat Is Regression?Linear Regression Deep DiveFeature Engineering FundamentalsThe RFM FrameworkTrain-Test Split MethodologyTraining Models with Scikit-LearnModel Coefficients and InterpretationRegression Evaluation MetricsVisualizing Model PerformanceFrom Predictions to Business Actions
3.
Classification for Decision-Making
Classification for Decision-Making
8 Lessons
8 Lessons
Explore classification techniques, model evaluation, and handling data challenges in machine learning.
4.
Unsupervised Learning with Clustering
Unsupervised Learning with Clustering
8 Lessons
8 Lessons
Explore unsupervised learning techniques, clustering methods, and customer segmentation strategies for effective data analysis.
5.
Ensemble Methods
Ensemble Methods
7 Lessons
7 Lessons
Explore ensemble learning techniques, hyperparameter tuning, and model evaluation for robust machine learning.
6.
Model Deployment Basics
Model Deployment Basics
9 Lessons
9 Lessons
Transition machine learning models to production, focusing on APIs, error handling, and monitoring for reliability.
Certificate of Completion
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About this course
WHY I WROTE THIS COURSE
Modern AI systems increasingly need state, branching, retries, checkpoints, and human approval flows. These capabilities go beyond simple sequential chains. This is where LangGraph becomes useful. In this course, I’ll show you how to design AI systems around graphs, state, and explicit control flow: core patterns used in many production agent workflows.
I built this course from my work on adaptive AI and machine learning systems. In research and production environments, I kept seeing the same problem: developers could build strong LLM prototypes, but struggled to move those prototypes into applications that needed persistence, coordination, retries, observability, and reliable execution. LangGraph helps address this gap by introducing explicit orchestration and state management for production-oriented AI workflows.
This course takes a practical, engineering-focused approach using Python, LangChain, LangGraph, and LangSmith. Instead of abstract agent theory, you’ll build a research assistant agent step by step while learning nodes, routing, tools, memory, interrupts, retries, checkpoints, and human-in-the-loop workflows. By the end, you’ll have built a complete end-to-end AI agent and developed a strong foundation in modern agent orchestration.
As AI engineering shifts from prototypes to production systems, understanding orchestration and control flow is becoming a core engineering skill. If you want to build reliable, stateful AI applications that can handle real-world complexity, this is the place to start.
ABOUT THE AUTHOR
Khayyam Hashmi
Computer scientist and Generative AI and Machine Learning specialist. VP of Technical Content @ educative.io.
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