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Machine Learning Notes PDF

Topics in our Machine Learning Notes PDF

In these “Machine Learning Notes PDF”, you will study the basic concepts and techniques of machine learning so that a student can apply these techniques to a problem at hand.

The topics we will cover in these Machine Learning Notes PDF will be taken from the following list:

Introduction: Basic definitions, Hypothesis space and inductive bias, Bayes optimal classifier and Bayes error, Occam’s razor, Curse of dimensionality, dimensionality reduction, feature scaling, feature selection methods.

Regression: Linear regression with one variable, linear regression with multiple variables, gradient descent, logistic regression, overfitting, regularization. performance evaluation metrics, validation methods.

Classification: Decision trees, Naive Bayes classifier, k-nearest neighbor classifier, perceptron, multilayer perceptron, neural networks, back-propagation algorithm, Support Vector Machine (SVM), Kernel functions.

Clustering: Approaches for clustering, distance metrics, K-means clustering, expectation-maximization, hierarchical clustering, performance evaluation metrics, validation methods.

 

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Machine Learning Handwritten Lecture Notes PDF

Machine Learning Handwritten Notes PDF
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Machine Learning Handwritten Lecture Notes PDF

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Machine Learning Lecture Notes PDF

Machine Learning Handwritten Notes PDF
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Machine Learning Lecture Notes PDF

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Machine Learning Lecture Notes PDF

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Machine Learning Lecture Notes PDF

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Machine Learning Lecture Notes PDF

Machine Learning Notes PDF
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Machine Learning Handwritten Notes PDF FAQs

What is Machine Learning ?

Machine Learning is the field of study that gives computers the capability to learn without being explicitly programmed.

What are the types of Machine Learning ?

  1. Supervised Learning, in which the training data is labeled with the correct answers, e.g., “spam” or “ham.” The two most common types of supervised learning are classification (where the outputs are discrete labels, as in spam filtering) and regression (where the outputs are real-valued).
  2. Unsupervised learning, in which we are given a collection of unlabeled data, which we wish to analyze and discover patterns within. The two most important examples are dimension reduction and clustering.
  3. Reinforcement learning, in which an agent (e.g., a robot or controller) seeks to learn the optimal actions to take based the outcomes of past actions.

What are the Applications of Machine Learning ?

  1. Image recognition
  2. Speech recognition
  3. Medical diagnosis
  4. Statistical Arbitrage
  5. Learning Associations
  6. Classification
  7. Prediction
  8. Extraction
  9. Regression
  10. Financial services

What is Artificial Neural Networks?

ANN learning is robust to errors in the training data and has been successfully applied for learning real-valued, discrete-valued, and vector-valued functions containing problems such as interpreting visual scenes, speech recognition, and learning robot control strategies.

What are the advantage of Using Artificial Neural Networks?

  1. Problem in ANNs can have instances that are represented by many attribute-value pairs.
  2. ANNs used for problems having the target function output may be discrete-valued, real-valued, or a vector of several real- or discrete-valued attributes.
  3. ANN learning methods are quite robust to noise in the training data. The training examples may contain errors, which do not affect the final output.
  4. It is used generally used where the fast evaluation of the learned target function may be required.


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