Supervised Learning
Train and evaluate classification and regression models.
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After this skill you can…
- Train decision trees, random forests, and neural nets
- Evaluate with accuracy, F1, AUC
- Avoid overfitting with regularisation
Prerequisites
Watch (10 videos)
Generative vs Discriminative Models - Explained
→ Implement Naive Bayes and Logistic Regression algorithms→ Analyze the strengths and weaknesses of different classification algorithms
How Data Science and AI Changed My Career Forever | Success Story | Intellipaat 🚀
→ Train supervised learning models→ Evaluate model performance→ Apply supervised learning to real-world problems
Overfitting and Regularization in Deep Learning
→ Train neural networks→ Evaluate model performance
Machine Learning With Python Full Course 2026 | Python Machine Learning For Beginners | Simplilearn
→ Train supervised learning models→ Evaluate supervised learning models
AI With Python Full Course 2026 [FREE] | Learn Artificial Intelligence With Python | Simplilearn
→ Train supervised learning models→ Evaluate model performance→ Optimize hyperparameters
Best Machine Learning Courses Online | Top Machine Learning Courses In 2026 | #Simplilearn | #Shorts
→ Learn Supervised Learning Algorithms→ Apply Supervised Learning to Real-World Problems→ Build Predictive Models
2. Artificial Intelligence (AI) Explained | AI Problems, AI Techniques & Real-World Applications
→ Implement supervised learning algorithms→ Solve real-world problems using AI techniques
4. Problem Formulation in AI | Production Systems, Control Strategies & Problem Characteristics
→ Recognize problem characteristics→ Implement machine learning algorithms
Talk by Pranjal Awasthi (Google)
→ Design and implement supervised learning algorithms→ Analyze the computational complexity of machine learning models
Efficient Algorithms for Reliable Machine Learning
→ Implement supervised learning algorithms→ Analyze the role of distributional assumptions in supervised learning
DeepCamp AI