Machine Learning Engineer

Dive deep into the algorithms and engineering practices that power intelligent systems.

• Duration: 12 Weeks (Part-time: ~20 hours/week)

• Prerequisites: "Data Science Foundations" or equivalent knowledge (Python, Pandas, basic statistics).

• Learning Format: Project-based learning with code reviews, guest lectures from industry ML engineers, and hands-on labs.

• Syllabus & What You'll Learn:

  • Module 1: Core Machine Learning Algorithms

    • Supervised Learning: Linear/Ridge/Lasso Regression, Logistic Regression, Decision Trees, Random Forests, SVM, K-Nearest Neighbors.

    • Unsupervised Learning: K-Means Clustering, PCA (Principal Component Analysis).

  • Module 2: Model Evaluation & Tuning

    • Bias-Variance Tradeoff, Cross-Validation, Hyperparameter tuning with GridSearch and RandomSearch.

    • Performance metrics: Accuracy, Precision, Recall, F1-Score, ROC-AUC.

  • Module 3: Introduction to Deep Learning

    • Building and training Neural Networks with TensorFlow/Keras.

    • Introduction to Convolutional Neural Networks (CNNs) for image data.

  • Module 4: Machine Learning Engineering & MLOps

    • Version control for models (DVC), building reproducible data pipelines.

    • Introduction to model deployment as a REST API using FastAPI and Docker.

  • Capstone Project: Build an end-to-end ML application. For example, develop a model to predict customer churn and deploy it to a cloud server.

• Outcome: You will be able to build, evaluate, tune, and deploy production-level machine learning models, qualifying you for roles like Machine Learning Engineer or Applied Scientist.