Foundational Courses
Machine Learning Engineering
Essentials
To provide participants with foundational knowledge and skills
5
11 enrolled students
Objective
To provide participants with foundational knowledge and skills in building, deploying, and optimizing machine learning models, preparing them to apply machine learning techniques effectively in production environments.
Basic To Advance
You will progress through this course from basics to advanced level.
Duration
3 Months
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Modules
Module 1: Introduction to Machine Learning for Engineers
Topics:
- The Machine Learning Pipeline: From data collection to deployment
- Supervised vs. Unsupervised Learning
- Key ML Algorithms:
- Linear Regression, Logistic Regression
- Decision Trees, Random Forests
- Ridge and Lasso Regression
- SVM, KNN
- Boosting techniques: AdaBoost, Gradient Boosting, XGBoost
- Model evaluation metrics: accuracy, precision, recall, AUC, F1-score
- Theory behind algorithms: Overfitting, regularization, bias-variance trade-off
Hands on exercises:
- Implement and evaluate key algorithms in Python using Scikit-learn.
- Work with datasets (e.g., Titanic, MNIST) to perform classification and regression.
Module 2: Data Engineering for Machine Learning
Topics:
- Introduction to Data Pipelines: Extract, Transform, Load (ETL) for ML
- Data wrangling and preprocessing techniques (scaling, encoding, handling missing values)
- Feature Engineering: Feature selection, extraction, and creation
- Working with large-scale data using tools like Apache Spark and Dask
- Data Storage and Retrieval: Introduction to SQL, NoSQL, and Data Lakes (e.g., AWS S3, Google BigQuery)
- Real-time data processing with Kafka (optional introduction)
Hands on exercises:
- Set up a basic ETL pipeline using Pandas, Dask, or Apache Spark.
- Design a data pipeline for a machine learning model, covering data ingestion, preprocessing, and feature generation
Module 3: Advanced Machine Learning and Deep Learning Concepts
Topics:
- Advanced Algorithms: Support Vector Machines, K-means, Hierarchical Clustering, DBSCAN
- Dimensionality Reduction: PCA, t-SNE
- Introduction to Neural Networks:
- Feedforward Neural Networks (FFNN)
- Convolutional Neural Networks (CNN)
- Recurrent Neural Networks (RNN)
- Introduction to TensorFlow and PyTorch frameworks
Hands on exercises:
- Build a neural network using TensorFlow or PyTorch for image classification.
- Apply PCA to a high-dimensional dataset to reduce dimensions and visualize results.
Module 4: Introduction to MLOps
Topics:
- MLOps Principles: CI/CD for machine learning, automation, and scalability
- Versioning Data, Models, and Code: Data drift, model drift
- Model Governance: Reproducibility, model auditing, and compliance
Tools Covered:
- Git and GitHub for version control
- Docker and Kubernetes for containerization and orchestration
- CI/CD Pipelines using Jenkins, GitLab CI, or GitHub Actions
Hands on exercises:
- Build a CI/CD pipeline for a machine learning model using GitHub Actions and Docker.
- Monitor a model in production for drift and performance degradation.
Module 5: Experiment Tracking and Model Management with MLflow
Topics:
- MLflow Overview: Tracking experiments, logging parameters and metrics
- Model Versioning: Managing model versions in production
- Reproducibility: Ensuring experiments can be replicated
- Model Registry: Storing, annotating, and promoting models to production
Hands on exercises:
- Use MLflow to track experiments and hyperparameter tuning.
- Create and manage a Model Registry to store and version models for deployment.
Module 6: TensorFlow Serving and Model Deploymen
Topics:
- Introduction to TensorFlow Serving: Scalable serving of machine learning models
- Deploying models with Flask and FastAPI
- REST APIs for ML Models: Exposing machine learning models as REST APIs
- Dockerizing ML Models: Creating Docker containers for scalable deployments
- Model Deployment on Cloud Platforms: AWS, Google Cloud, Azure
Hands on exercises:
- Serve a TensorFlow model using TensorFlow Serving.
- Build a simple REST API for a model using FastAPI or Flask.
- Dockerize the API and deploy it to a cloud service (AWS/GCP/Azure).
Module 7: Workflow Orchestration with Apache Airflow
Topics:
- Introduction to Airflow: Why orchestration is important for ML pipelines
- Creating DAGs (Directed Acyclic Graphs): Scheduling and automating
- machine learning tasks
- Integrating Airflow with ETL pipelines and model training
- Managing dependencies, scheduling tasks, and handling failures
Hands on exercises:
- Set up an Airflow instance and design a DAG to automate a data preprocessing pipeline.
- Schedule and automate model training, retraining, and deployment using Airflow.
Module 8: Scalable and Distributed Machine Learning
Topics:
- Distributed Machine Learning: Parallelizing machine learning tasks with Spark MLlib, Horovod, or Dask
- Scalable Neural Network Training: Using TensorFlow and PyTorch on multi-GPU or multi-node clusters
- Hyperparameter Tuning at Scale: Distributed hyperparameter tuning with Ray or Optuna
Hands on exercises:
- Use Spark MLlib or Dask to implement a distributed machine learning algorithm.
- Train a deep learning model on multiple GPUs using Horovod or TensorFlow’s distributed training API.
Module 9: Capstone Project: End-to-End Machine Learning Pipeline
Topics:
- Solving a real-world business problem using the complete machine learning lifecycle
- Data engineering: Data ingestion, transformation, and storage
- Machine learning: Model training, evaluation, and tuning
- MLOps: Deploying the model and monitoring performance
- Model serving and scaling the deployment using cloud platforms
Practical Project
- Design an end-to-end machine learning pipeline, from data ingestion to deployment.
- Use Airflow to automate the pipeline, MLflow to track experiments, and TensorFlow Serving for model deployment.
Frequently Asked Questions
1. What is the Machine Learning Engineering Essentials course?
This course is designed to provide foundational skills in machine learning, covering essential concepts, algorithms, and practical tools to help you become proficient in building and deploying machine learning models.
2. Who is this course best suited for?
This course is ideal for beginners, data enthusiasts, software developers, and anyone interested in building a strong foundation in machine learning. Some programming experience is recommended, but no prior machine learning knowledge is required.
3. What topics will be covered in this course?
Topics include supervised and unsupervised learning, model evaluation, feature engineering, data preprocessing, overfitting/underfitting, and hyperparameter tuning. You will work with algorithms like linear regression, decision trees, SVMs, and neural networks using Python and ML libraries.
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