Skip to main content
grasp.study
About
Explore
Pricing
Log in
Try it for free
Back to course
👋
Welcome to a Grasp lesson
Create a free account
to keep studying and create your own study path.
Gate Data Science and AI
·
Module 10
Advanced Machine Learning Models
1
Ensemble Methods: Bagging and Boosting
Apply ensemble methods: bagging (Random Forests) and boosting (Gradient Boosting).
2
Support Vector Machines for Classification
Apply Support Vector Machines (SVM) for classification problems.
3
K-Means Clustering: Unsupervised Learning
Apply the k-means clustering algorithm for unsupervised learning tasks.
4
Dimensionality Reduction with PCA
Apply Principal Component Analysis (PCA) for dimensionality reduction.
5
Streamlining ML Workflows with Scikit-learn Pipelines
Build a complete modeling workflow using scikit-learn's Pipeline object.
6
Choosing the Right ML Model
Implement a solution for a representative problem by selecting and justifying the choice of ML model.
7
Multi-Layer Feedforward Neural Network Architecture
Describe the structure of a multi-layer feedforward neural network.
8
Understanding Activation Functions
Explain the role of activation functions like Sigmoid, ReLU, and Softmax.
Previous module
Model Evaluation and Improvement
Next module
Deep Learning with PyTorch