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Short Notes.

The document outlines key concepts and applications of machine learning, including various regression techniques, decision trees, support vector machines, and boosting methods. It emphasizes practical implementation steps, unsupervised learning methods, and the importance of choosing the right algorithms based on specific criteria. Additionally, it highlights tools and libraries for model implementation and debugging in machine learning tasks.

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13 views2 pages

Short Notes.

The document outlines key concepts and applications of machine learning, including various regression techniques, decision trees, support vector machines, and boosting methods. It emphasizes practical implementation steps, unsupervised learning methods, and the importance of choosing the right algorithms based on specific criteria. Additionally, it highlights tools and libraries for model implementation and debugging in machine learning tasks.

Uploaded by

brokenbottle571
Copyright
© © All Rights Reserved
We take content rights seriously. If you suspect this is your content, claim it here.
Available Formats
Download as PDF, TXT or read online on Scribd
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Machine Learning Applications - Summary Notes

1. Linear & Multivariate Linear Regression

Linear Regression models the relationship between a dependent variable and one or more independent variables using

a straight line. Multivariate Linear Regression involves multiple features (X1, X2, ..., Xn).

Formula: Y = 0 + 1X1 + 2X2 + ... + nXn +

2. Logistic Regression and Regularization

Logistic Regression is used for classification problems. It uses the sigmoid function to output probabilities.

Regularization (L1/L2) helps prevent overfitting by penalizing large coefficients.

3. Practical Implementation Aspects

Steps: Data cleaning Feature scaling Model selection Training Evaluation Tuning.

Tools: scikit-learn, pandas, matplotlib.

4. Decision Trees and Pruning

Decision Trees split data based on feature values.

Pruning removes branches to avoid overfitting. Two types: Pre-pruning (early stopping) and Post-pruning (removing

after full growth).

5. Support Vector Machines (SVMs)

SVMs find the hyperplane that maximally separates classes. Kernel trick allows for non-linear boundaries. Important

parameters: C (penalty), kernel, gamma.

6. Boosting with Decision Trees

Boosting combines weak learners to form a strong one. Examples: AdaBoost, Gradient Boosting, XGBoost. Each new

tree corrects errors made by the previous ones.

7. Setting Up & Debugging ML Tasks


Machine Learning Applications - Summary Notes

Define the problem clearly. Clean and preprocess data. Try different models and tune hyperparameters. Use metrics

and visualizations to debug.

8. Unsupervised Learning: K-Means, PCA, Hierarchical Clustering

K-Means: Cluster based on nearest centroid.

PCA: Reduce dimensions while keeping max variance.

Hierarchical Clustering: Builds a tree (dendrogram) of clusters.

9. Implementing Clustering Algorithms

Libraries: scikit-learn, scipy, matplotlib.

Fit the model, predict clusters, visualize using scatter plots or dendrograms.

10. Parallelizing Learning Algorithms

Use parallel processing to speed up training. Tools: Joblib, Dask, Spark, XGBoost (n_jobs=-1 for all cores).

11. Applications of Machine Learning

Fields: Healthcare, Finance, Retail, Security, Transportation.

Tasks: Classification, Regression, Clustering, Recommendation.

12. Choosing Algorithms What Will Work?

Base on problem type, data size, feature type, training time, and accuracy needs.

Use trial-and-error, GridSearchCV, and metrics for evaluation.

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