What will you learn in Advanced Learning Algorithms Course
Build and train a neural network with TensorFlow to perform multi-class classification.
Apply best practices for machine learning development so models generalize to real-world data and tasks.
Build and use decision trees and tree ensemble methods, including random forests and boosted trees.
Program Overview
Module 1: Neural Networks
⏳ 7 hours
Topics: Biological vs. artificial neurons, forward propagation, vectorized implementations.
Hands-on: Build neural nets in TensorFlow and from-scratch Python implementations.
Module 2: Neural Network Training
⏳ 10 hours
Topics: Activation functions, loss functions, optimizers (Adam vs. gradient descent), multiclass classification.
Hands-on: Train TensorFlow models on multiclass tasks and explore optimization strategies.
Module 3: Advice for Applying Machine Learning
⏳ 8 hours
Topics: Model evaluation, bias–variance trade-off, data-centric improvement, ethics, and fairness.
Hands-on: Perform error analysis, cross-validation, and apply ethical AI checks.
Module 4: Decision Trees
⏳ 7 hours
Topics: Tree construction, information gain, pruning, random forests, and XGBoost.
Hands-on: Implement decision trees and ensembles, then evaluate on real datasets.
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Job Outlook
Machine learning practitioners are in demand across tech, finance, healthcare, and e-commerce, with roles like ML Engineer, Data Scientist, and AI Specialist.
Entry-level salaries typically range from $85K–$115K, rising to $130K+ for experienced professionals skilled in deep learning and ensemble methods.
Mastery of TensorFlow, neural networks, and tree-based algorithms opens opportunities in research labs, product teams, and AI startups.
Explore More Learning Paths
Deepen your understanding of algorithms and problem-solving techniques with these advanced courses. Enhance your computational thinking and develop the skills needed to tackle complex programming challenges efficiently.
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Specification: Advanced Learning Algorithms Course
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