AI Camp: Machine Learning in Action – Building Prediction Models - AI-Driven LMS + Atey Tutor

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AI Camp: Machine Learning in Action – Building Prediction Models

Categories Automation, Machine Learning, Python

Course Prerequisite(s)

Please note that this course has the following prerequisites which must be completed before it can be accessed
Introduction to Python for Data Science

Understand machine learning basics: Learn key concepts like features, labels, training/testing data, and model evaluation.
Work with real-world datasets: Preprocess, clean, and prepare data for training ML models.
Build foundational ML models: Train and evaluate Linear Regression, Logistic Regression, and Decision Tree models.
Refine and optimize models: Perform feature engineering, hyperparameter tuning, and debugging to improve model performance.
Automate ML workflows: Use Python to create end-to-end ML pipelines for data processing, model training, and evaluation.
Prepare models for deployment: Save and load models for integration into real-world applications.
Solve real-world problems: Apply everything you learn to create a functional, intelligent ML model that addresses a specific business challenge.

Course Curriculum

Introduction to the Project and ML Basics
Begin by understanding the problem statement and the project scope. Learn the basics of machine learning, including features, labels, and dataset preparation, while setting up your first ML project.

Data Preprocessing and Feature Engineering
Explore techniques for cleaning and preparing raw data for modeling. Learn to handle missing values, normalize data, and engineer features for better model performance.

Training Your First ML Model
Train your first ML model using Linear Regression. Understand the concepts of training, testing, and evaluating model performance with metrics like RMSE or R².

Introduction to scikit-learn
Dive deeper into scikit-learn, a powerful Python library for machine learning. Use it to streamline the model-building process and experiment with hyperparameter tuning.

Experimentation with Multiple Models
Train and compare multiple supervised learning models, such as Logistic Regression, Decision Trees, and K-Nearest Neighbors, to solve the project’s problem statement.

Model Evaluation Metrics
Learn how to evaluate models using metrics like accuracy, precision, recall, F1-score, and confusion matrix. Use cross-validation to assess model generalizability.

Hyperparameter Tuning
Experiment with hyperparameter tuning techniques like grid search to optimize your models and improve their accuracy.

Introducing MLflow for Experiment Tracking
Learn the basics of MLflow and how to track experiments, including model parameters, metrics, and results. Compare and manage multiple models efficiently using MLflow's tracking capabilities.

Saving and Loading Models with MLflow
Save trained models to the MLflow model registry for easy reproducibility and deployment. Learn to load these models for further testing or integration into applications.

Integrating Models into Applications
Deploy your trained model into a Python application. Use it to make real-time predictions and evaluate its usability within a business or user-facing context.

Dockerizing the ML Workflow
Package your ML workflow, including the MLflow tracking server, into a Docker container for portability and deployment on any system.

Capstone: Finalize and Deploy Your ML-Powered App with MLflow
Build on the work from previous weeks to deliver a complete, ML-powered application. Showcase your model’s lifecycle management with MLflow, along with its deployment and custom features, in your final presentation.

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Level

Intermediate
Duration

72 hours
Last Updated

December 6, 2024
Certificate

Certificate of completion

A course by

DataU Instructor

Instructor

Aspiring data-driven app developers looking to understand and apply machine learning concepts.
Beginners in ML eager to gain hands-on experience with real-world datasets and foundational algorithms.
Students and professionals interested in building intelligent systems and automating data-driven decision-making.
Anyone who wants to learn ML by doing, solving real-world problems alongside an instructor.

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