Machine Learning

Study Materials:

• Introduction to Machine Learning 

• Top 10 Applications of Machine Learning 

• Different Types of Machine Learning 

• Supervised Learning 

• Hypothesis Space and Inductive Bias

• Evaluation and Cross-Validation 

• Linear Regression 

• Decision Tree 

• Learning Decision Tree 

• Overfitting 

• K-nearest neighbour 

• Feature Selection 

• Feature Extraction 

• Collaborative Filtering 

• Bayesian Learning 

• Bayesian Network 

• Logistic Regression 

• Support Vector Machine (SVM) 

• SVM : The Dual Formulation 

• SVM: Maximum Margin with Noise 

• Non-linear SVM and Kernel Function 

• SVM: Solution to the Dual Problem 

• Deep Neural Network 

• Multilayer Neural Network 

• Iris Dataset Prediction

• Convolutional Neural Networks Architecture and Applications  

• Artificial Neural Network (ANN)   

• Artificial Neural Network Applications in the Real World  

• Recurrent Neural Networks – Deep Learning Fundamentals  

• XGBoost in Machine Learning – Features & Importance  

• GBoost Algorithm – Applied Machine Learning  

• AdaBoost Algorithm For Machine Learning  

  Agglomerative Hierarchical Clustering  

  Linear Algebra: Vector Spaces, Subspaces, Orthogonal Matrices, Quadratic Form  

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