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CourseraAdvanced Learning Algorithms
4.9(8,690)459k studentsBEGINNEREnglish
Korshub's take
Korshub Take
This is the second course in DeepLearning.AI's widely respected Machine Learning Specialization, and it's where things get genuinely hands-on — building neural networks in TensorFlow and tree-based models rather than just theory. The huge enrollment numbers aren't an accident; this specialization is one of the most trusted on-ramps into applied ML.
Who it's for
Learners who've grasped the basics of supervised learning and want to move into multi-class classification, decision trees, and ensemble methods like random forests and boosted trees.
What you'll build
- Neural networks in TensorFlow for multi-class classification
- Decision trees and tree ensembles (random forests, boosted trees)
- Model evaluation, optimization, and transfer learning techniques
- Practices for responsible, ethical AI development
With nearly 460,000 students, it's one of the most heavily enrolled machine learning courses anywhere — a strong signal this is a dependable, well-tested path into practical ML.
About this course
In the second course of the Machine Learning Specialization, you will:
• Build and train a neural network with TensorFlow to perform multi-class classification
• Apply best practices for machine learning development so that your models generalize to data and tasks in the real world
• Build and use decision trees and tree ensemble methods, including random forests and boosted trees
The Machine Learning Specialization is a foundational online program created in collaboration between DeepLearning.
Skills you'll gain: Model Training, Machine Learning Algorithms, Transfer Learning, Machine Learning, Applied Machine Learning, Data Ethics, Decision Tree Learning, Model Evaluation, Tensorflow, Responsible AI, Supervised Learning, Deep Learning, Classification Algorithms, Random Forest Algorithm, Model Optimization.
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