About Machine Learning 1

-

 Machine Learning

  • The Machine Learning Landscape
  • Classification
  • Support Vector Machines
  • Decision Trees
  • Ensemble Learning and Random Forests
  • Dimensionality Reduction
  • Clustering

Unit I:

The Machine Learning Landscape: What Is Machine Learning? Why Use Machine Learning? Types of Machine Learning Systems, Supervised/Unsupervised Learning, Batch and Online Learning, Instance-Based Versus Model-Based Learning, Main Challenges of Machine Learning, Insufficient Quantity of Training Data, Nonrepresentative Training Data, Poor-Quality Data, Irrelevant Features, Overfitting the Training Data, Underfitting the Training Data, Stepping Back, Testing and Validating.

👉UNIT 1(A) NOTEs : The Machine Learning Landscape Notes

👉UNIT 1(A) PPTs: The Machine Learning Landscape

👉UNIT 1(B) NOTEs: The Machine Learning Landscape NOTEs

👉Machine Learning 1 : UNIT 1(B) PPTs: The Machine Learning Landscape PPTs

👉Machine Learning 1: UNIT 1 The Machine Learning Landscape Questions


Unit II:

Classification: Training a Binary Classifier, Performance Measures, Measuring Accuracy UsingCross-Validation, Confusion Matrix, Precision and Recall, Precision/RecallTradeoff , The ROC Curve, Multiclass Classification, Error Analysis, Multilabel Classification, Multi Output Classification. k-NN Classifier.

👉Machine Learning 1: UNIT 2 NOTEs: Classification Notes

👉Machine Learning 1 : UNIT 2: Classification PPTs

👉Machine Learning 1: UNIT 2: Classification MCQs

👉Machine Learning 1: UNIT 2: Classification Questions

Unit III:

Support Vector Machines: Linear SVM Classification, Soft Margin Classification, Nonlinear SVM Classification, Polynomial Kernel, Adding Similarity Features, Gaussian RBF Kernel, Computational Complexity, SVM Regression, Under the Hood, Decision Function and Predictions, Training Objective, Quadratic Programming, The Dual Problem, Kernelized SVM, Online SVMs.

👉Machine Learning 1: UNIT 3 (A) NOTES: Support Vector Machines NOTEs

👉Machine Learning 1: UNIT 3 (A) PPTs: Support Vector Machines PPTs

👉Machine Learning 1: UNIT 3 (B) NOTEs: Support Vector Machines NOTEs

👉Machine Learning 1: UNIT 3 (B) PPTs: Support Vector Machines PPTs

👉Machine Learning 1: UNIT 3 A & B : Support Vector Machines Questions

👉Machine Learning 1: UNIT 3 : Support Vector Machines MCQs

Unit IV:

Decision Trees: Training and Visualizing a Decision Tree, Making Predictions, Estimating Class Probabilities, The CART Training Algorithm, Computational Complexity, Gini Impurity or Entropy? Regularization Hyperparameters, Regression

👉Machine Learning 1: UNIT 4 (A) NOTEs: Decision Trees NOTEs

👉Machine Learning 1: UNIT 4 (A) PPTs: Decision Trees PPTs

👉Machine Learning 1: UNIT 4 (A): Decision Trees Questions

👉Machine Learning 1: UNIT 4 (A) : Decision Trees MCQs

Ensemble Learning and Random Forests: Voting Classifiers, Bagging and Pasting, Bagging and Pasting in Scikit-Learn, Out-of-Bag Evaluation, Random Patches and Random Subspaces, Random Forests, Extra-Trees, Feature Importance, Boosting, AdaBoost, Gradient Boosting, Stacking.

👉Machine Learning 1: UNIT 4 (B) NOTES: Ensemble Learning and Random Forests NOTES

👉Machine Learning 1: UNIT 4 (B) PPTs: Ensemble Learning and Random Forests PPTs

👉Machine Learning 1: UNIT 4 (B) : Ensemble Learning and Random Forests Questions

👉Machine Learning 1: UNIT 4 (B) : Ensemble Learning and Random Forests MCQs

Unit V:

Dimensionality Reduction: The Curse of Dimensionality, Main Approaches for Dimensionality Reduction, Projection, PCA.

👉Machine Learning 1: UNIT-5(A) NOTES: Dimensionality Reduction NOTES

👉Machine Learning 1: UNIT-5(A) PPTs: Dimensionality Reduction PPTs

👉Machine Learning 1: UNIT-5(A): Dimensionality Reduction Questions

Clustering: How does clustering work: finding similarities using distances, Euclidean distance and other distance metrics. k-Means Clustering: Plotting customers with their segments, normalizing features, cluster centres and interpreting the Clusters. Hierarchical Clustering.

👉Machine Learning 1: UNIT 5(B) NOTEs: Clustering NOTES

👉Machine Learning 1: UNIT 5 (B) PPTs: Clustering PPTs

👉Machine Learning 1: UNIT 5 (B) : Clustering Questions

Textbooks:

1. Géron, Aurélien. Hands-on machine learning with Scikit-Learn, Keras, and TensorFlow: Concepts, tools, and techniques to build intelligent systems. O'Reilly Media, 2019.
2. Pradhan, Manaranjan, and U. Dinesh Kumar. Machine Learning using Python. Wiley, IIM Bangalore, 2019.

References: 

1.     Introduction to Machine Learning, Ethem Alpaydin 2nd Edition, MIT Press 2000

2.     Machine Learning, Tom M. Mitchell, McGraw Hill, 1997, ISBN: 0-07-042807-7. 


Comments

Post a Comment

Popular posts from this blog

Machine Learning 1: UNIT 3 : Support Vector Machines MCQs

-
UNIT 3 Support Vector Machines MCQs  ----------------------------------------------------------------------------------------------------------------------------- 1. A Support Vector Machine can be used for A.     Performing linear or nonlinear classification B.     Performing regression C.     For outlier detection D.     All of the above Ans: D 2. The decision boundaries in a Support Vector machine is fully determined (or “supported”) by the instances located on the edge of the street? Top of Form True False Ans: A 3. Support Vector Machines are not sensitive to feature scaling A.     Top of Form True False Ans: B 4. If we strictly impose that all instances be off the street and on the right side, this is called  Soft margin classification  Hard margin classification  Strict margin classification  Loose margin classification Ans...
Machine Learning

Machine Learning MCQs-2 (Performance Metrics, Linear Regression, Naïve Bayes Classifier )

-
                               Machine Learning MCQs- 2 Performance Metrics, Linear Regression, Naïve Bayes Classifier  1.   The greater the value for ROC AUC, better the model: True False Ans: 1   2.   A set of data are all close to each other, and they are close to the actual value.  This set of data can be described as... Accurate Precise both Precise and accurate None of the above Ans: 3 3. The maximum value of the ROC AUC is: 0.8 0.9 1 0 Ans: 3 4. Recall can be increased by increasing the decision threshold. True or False? True False Ans: 2 5. Which of these is a good measure to decide which threshold to use? Confusion matrix F1 score ROC curve Precision & Recall versus Threshold Curve Ans: 4 6.  Which of these may have to be performed before analyzing and training the dataset? Shuffling Cross-Validation F1 Score None Ans: 1 7. For the below confusio...
Machine Learning

VERSION SPACES AND THE CANDIDATE-ELIMINATION ALGORITHM

-
Image
  VERSION SPACES AND THE CANDIDATE-ELIMINATION ALGORITHM   •         The key idea in the CANDIDATE-ELIMINATION algorithm is to output •         a description of the set of all hypotheses consistent with the training examples Note difference between definitions of consistent and satisfies ·        An example x is said to satisfy hypothesis h when h(x) = 1, regardless of whether x is a positive or negative example of the target concept . ·        An example x is said to consistent with hypothesis h iff h(x) = c(x) The LIST-THEN-ELIMINATION algorithm The LIST-THEN-ELIMINATE algorithm first initializes the version space to contain all hypotheses in H and then eliminates any hypothesis found inconsistent with any training example . ___________________________________________________________________________ 1. VersionSpace c...
Machine Learning