Train, Tune, & Ship: End-to-End Machine Learning Engineering

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This comprehensive program takes you through the complete machine learning engineering lifecycle, from training your first models to shipping optimized, production-ready systems. You'll develop the technical depth and practical judgment needed to build ML systems that perform reliably at scale. Starting with foundational model training and evaluation, you'll progress through hands-on courses covering hyperparameter tuning, custom neural network design, computer vision, and deep learning optimization. Each course emphasizes real-world workflows using industry-standard tools including PyTorch, TensorFlow, scikit-learn, and SHAP, ensuring the skills you build translate directly to professional ML engineering roles. You'll learn to diagnose training instability, tune models systematically, validate performance rigorously, and explain model behavior to both technical and non-technical stakeholders. The program also covers critical production considerations including computational cost benchmarking, algorithm selection, model quantization, and edge deployment using TensorFlow Lite. By program completion, you'll possess the end-to-end skills to confidently take a machine learning problem from business requirement to deployed, optimized solution, making you a more effective and versatile ML practitioner.

Syllabus

Course 1: ML: Build, Train, Justify Models
Course 2: Model Training & Evaluation
Course 3: Optimize AI: Build & Evaluate Predictive Models
Course 4: Optimize ML Models: Hyperparameter Tuning
Course 5: Choose Cost-Effective ML Algorithms Fast
Course 6: Optimize and Benchmark AI Algorithms for Speed
Course 7: Validate and Explain Your ML Models
Course 8: Design and Build Custom Neural Networks
Course 9: Vision Models: Train and Evaluate
Course 10: Optimize Deep Learning Models for Peak AI
Course 11: Build & Optimize TensorFlow ML Workflows

Courses

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This course teaches you how to evaluate and design custom neural network architectures for real machine-learning tasks. You start by learning how to compare common model families—such as CNNs, RNNs, and Transformers—and match them to task needs, data patterns, and compute limits. You then learn how to construct custom architectures using layers, activations, and regularization techniques that improve generalization and training stability. Through videos, readings, hands-on practice, and guided coach support, you build models in PyTorch and test how design choices affect performance. By the end of the course, you can confidently select topologies, justify architectural decisions, and design models ready for real-world deployment.
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In this short course, you’ll learn how to train and evaluate machine learning models with confidence. You’ll explore how mini-batch training and learning-rate schedulers shape convergence, how to read loss curves and logs to diagnose issues, and how class-imbalance techniques affect F1 scores. Through hands-on PyTorch practice, you’ll train models, investigate instability, and compare weighting and SMOTE. By the end, you’ll understand how to guide models toward stable, reliable performance.
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This short, hands-on course helps learners adapt and optimize deep learning models for real-world use. Learners begin by exploring how transfer learning accelerates model development when data is limited. Through guided practice, they fine-tune a pretrained model, adjust freezing and unfreezing strategies, and troubleshoot common training challenges. The course then shifts to evaluating model configurations for deployment, focusing on accuracy, latency, memory footprint, and efficiency. Learners experiment with optimization methods such as hyperparameter tuning and quantization, compare multiple model setups, and make evidence-based recommendations for production environments. By the end, learners can confidently balance accuracy and performance constraints to choose the right model for their needs.
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ML: Build, Train, Justify Models gives learners a practical, end-to-end experience in turning real business problems into well-framed machine learning tasks, training multiple model families, and justifying model choices using bias–variance reasoning. Through short videos, hands-on exercises, and a Coursera Lab environment, learners practice reading product specifications, identifying the correct ML task, and building reproducible modeling workflows with APIs and experiment tracking. They train logistic regression, random forest, and gradient boosting models on tabular data, compare model behavior across repeated splits, and learn how to write clear, evidence-based recommendations. By the end, learners can confidently map business needs to ML tasks, train and evaluate diverse algorithms, and select models based on stability, interpretability, and performance rather than guesswork.
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This short course gives you practical experience training and evaluating computer vision models. You’ll learn how to build image preprocessing pipelines, apply data augmentation, and train deep learning models such as CNNs and Vision Transformers. You’ll also learn to evaluate performance using metrics such as mean Average Precision (mAP), Intersection over Union (IoU), precision, and recall, and to use error analysis to understand failure patterns. Through short videos, focused readings, hands-on labs, and guided coaching, you’ll practice real job tasks such as writing TensorFlow data loaders, training a Vision Transformer on plant-disease images, computing per-class AP and mAP, and comparing results across IoU thresholds. By the end, you’ll have a complete workflow you can adapt to your own projects and use to demonstrate your skills.
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This short course helps you build and optimize machine learning workflows using TensorFlow 2.x. You’ll start by structuring an end-to-end pipeline that includes data ingestion with tf.data, model definition with Keras, and custom training with checkpointing for reliability. You’ll then learn how to optimize your models for deployment using TensorFlow Lite, including post-training quantization and latency benchmarking. Along the way, you’ll see how ML engineers measure performance, evaluate tradeoffs, and deploy models to mobile and edge devices. Through hands-on practice and real-world examples, you’ll learn to think like an applied ML practitioner who builds efficient, production-ready TensorFlow systems.
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Optimize ML Models: Hyperparameter Tuning gives you the practical skills to move from “good enough” models to models that perform reliably at scale. You’ll learn how default hyperparameters shape model behavior, how computational complexity affects training cost, and why structured tuning methods outperform guesswork. Through short videos, hands-on practice, and a guided GridSearchCV project, you’ll build a complete workflow for selecting, evaluating, and explaining tuned model configurations. By the end of the course, you’ll know how to design effective search spaces, run systematic tuning experiments, interpret cross-validated results, and save tuned parameters for real ML pipelines—all essential skills for modern machine learning and AI roles.
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This short course helps you validate and explain machine learning models with confidence. You’ll learn practical strategies for using k-fold cross-validation and stratified sampling to estimate performance more accurately, especially when working with imbalanced data. You’ll also explore feature-importance techniques, including SHAP, to understand how your model behaves and how to explain its decisions clearly to technical and non-technical audiences. Through accessible videos, short readings, and hands-on activities, you’ll strengthen your ability to evaluate models beyond a single accuracy score. By the end of the course, you’ll know how to choose the right validation strategy, interpret model explanations, and communicate insights that support responsible deployment in real-world domains like fraud detection and loan approvals.
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Choose Cost-Effective ML Algorithms Fast teaches you how to evaluate and compare machine learning algorithms based on their resource utilization—not just accuracy. In real ML pipelines, training time, memory footprint, and compute cost determine whether a model can run reliably at scale. In this short, practical course, you’ll examine how algorithm design affects efficiency, learn how to benchmark models fairly, and interpret logs to uncover cost patterns. You’ll complete a hands-on lab comparing XGBoost and Random Forest on a large dataset, charting training time and memory usage, and making a clear recommendation for the most cost-effective option. By the end of the course, you’ll know how to select algorithms that meet performance goals while staying efficient, predictable, and production-ready.
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This short course helps you build and evaluate predictive models using supervised and unsupervised techniques. You will practice training algorithms with scikit-learn, explore how cross-validation affects model reliability, and analyze performance metrics like accuracy and F1 to make data-driven improvements. Instead of relying on guesswork, you’ll learn how to iterate systematically so your models meet defined performance targets. Through hands-on labs and guided coaching, you will build logistic-regression and clustering models, apply 5-fold cross-validation, and refine features until your model performs at the level you need. By the end, you will be able to apply these workflows to real predictive modeling tasks in retail and credit-risk contexts.
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In this course, you’ll learn how to analyze and benchmark AI-related algorithms so your systems run efficiently at scale. You’ll use computational complexity and data-structure behavior to predict performance as workloads grow, then validate those predictions with small prototype implementations. You’ll learn how to design fair benchmarks, interpret results using metrics like latency, throughput, memory, and scaling curves, and make defensible decisions when trade-offs are unavoidable. By the end, you’ll be able to identify bottlenecks, communicate performance findings clearly, and choose the best-performing approach for real-world AI workloads using reproducible measurement.