Machine Learning Engineer Professional Certificate

Learn machine learning from its foundations and statistics to deep learning, and take models all the way to production.

Build models with scikit-learn on sound statistics, then implement the classic algorithms from scratch so you know what the libraries do. Learn classification and clustering, feature engineering and honest model evaluation, then train neural networks with TensorFlow and Keras, make deep networks train well, and apply them to real problems. You finish by tracking experiments with MLflow and serving models in production. Every course is hands-on in Python, with labs and a capstone project your instructors review.

Prepares you for

Machine learning engineer, Data scientist, AI developer, MLOps engineer

10 courses in this Professional Certificate

  • Machine Learning Foundations with Python: Prepare data, train regression and classification models with scikit-learn, evaluate them honestly and build a tuned pipeline end to end, all in PyCharm. (Beginner, about 11 hours)
  • Statistics for Data Science and AI: Describe data, reason with probability, and test ideas with confidence intervals, hypothesis tests, regression and A/B tests, in Python with pandas and SciPy. (Beginner, about 10 hours)
  • Machine Learning from Scratch: Build gradient descent, k-NN, k-means, Naive Bayes, linear and logistic regression and decision trees in NumPy, and prove each one matches scikit-learn. (Intermediate, about 26 hours)
  • Classical Classification and Clustering: Apply and tune logistic regression, k-NN, SVMs, decision trees and ensembles, then find structure in unlabelled data with k-means and PCA, all in scikit-learn. (Intermediate, about 16 hours)
  • Feature Engineering and Model Evaluation: Prepare real, messy data for machine learning, evaluate models reliably with cross-validation, tune them honestly, and wrap it all in leak-proof scikit-learn pipelines. (Intermediate, about 15 hours)
  • Introduction to Neural Networks and TensorFlow: Build neural networks from the single neuron up: code a perceptron and backpropagation in NumPy, then train and evaluate feed-forward networks with TensorFlow and Keras. (Intermediate, about 13 hours)
  • Training Deep Neural Networks: Diagnose and fix what stops deep networks learning: vanishing and exploding gradients, slow or unstable optimisation, overfitting and badly scaled activations, in TensorFlow and Keras. (Advanced, about 14 hours)
  • Applied Machine Learning with TensorFlow: Build end-to-end TensorFlow projects: tf.data pipelines and preprocessing layers, CNNs and transfer learning for images, TF-IDF and LSTM models for text, and models saved, exported and served. (Intermediate, about 17 hours)
  • MLOps Foundations: Experiments, Models and Reproducibility with MLflow: Track every experiment, package models with signatures, promote them with registry aliases and rebuild any training run, using MLflow on your own laptop. (Intermediate, about 16 hours)
  • Deploying and Serving Machine Learning Models: Serve models fast and safely: validated APIs, ONNX, gRPC and micro-batching, lean containers, canary and shadow releases, and load tests that tell the truth. (Advanced, about 17 hours)