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01. Matrix Vector Dot Product
01. Matrix Vector Dot Product
 
 
02. Transpose of a Matrix
02. Transpose of a Matrix
 
 
03. Reshape Matrix
03. Reshape Matrix
 
 
04. Calculate Mean by Row or Column
04. Calculate Mean by Row or Column
 
 
05. Scalar Multiplication of a Matrix
05. Scalar Multiplication of a Matrix
 
 
06. Calculate 2x2 Matrix Inverse
06. Calculate 2x2 Matrix Inverse
 
 
07. Calculate Covariance Matrix
07. Calculate Covariance Matrix
 
 
08. Linear Regression Using Normal Equation
08. Linear Regression Using Normal Equation
 
 
09. Linear Regression Using Gradient Descent
09. Linear Regression Using Gradient Descent
 
 
10. Feature Scaling Implementation
10. Feature Scaling Implementation
 
 
11. Sigmoid Activation Function Understanding
11. Sigmoid Activation Function Understanding
 
 
12. Softmax Activation Function Implementation
12. Softmax Activation Function Implementation
 
 
13. Single Neuron
13. Single Neuron
 
 
14. Transformation Matrix from Basis B to C
14. Transformation Matrix from Basis B to C
 
 
15. Random Shuffle of Dataset
15. Random Shuffle of Dataset
 
 
16. Batch Iterator for Dataset
16. Batch Iterator for Dataset
 
 
17. One-Hot Encoding of Nominal Values
17. One-Hot Encoding of Nominal Values
 
 
18. Convert Vector to Diagonal Matrix
18. Convert Vector to Diagonal Matrix
 
 
19. Calculate Accuracy Score
19. Calculate Accuracy Score
 
 
20. Implementation of Log Softmax Function
20. Implementation of Log Softmax Function
 
 
21. Implement ReLU Activation Function
21. Implement ReLU Activation Function
 
 
22. Implement Ridge Regression Loss Function
22. Implement Ridge Regression Loss Function
 
 
23. Leaky ReLU Activation Function
23. Leaky ReLU Activation Function
 
 
24. Linear Kernel Function
24. Linear Kernel Function
 
 
25. Implement Precision Metric
25. Implement Precision Metric
 
 
26. Implement Recall Metric in Binary Classification
26. Implement Recall Metric in Binary Classification
 
 
27. Implement F-Score Calculation for Binary Classification
27. Implement F-Score Calculation for Binary Classification
 
 
28. Implement Gini Impurity Calculation for a Set of Classes
28. Implement Gini Impurity Calculation for a Set of Classes
 
 
29. Calculate R-squared for Regression Analysis
29. Calculate R-squared for Regression Analysis
 
 
30. Calculate Root Mean Square Error (RMSE)
30. Calculate Root Mean Square Error (RMSE)
 
 
31. Min-Max Scaling of Feature Values
31. Min-Max Scaling of Feature Values
 
 
32. Label Encoding for Ordinal Variables
32. Label Encoding for Ordinal Variables
 
 
33. Implement Compressed Row Sparse Matrix (CSR) Format Conversion
33. Implement Compressed Row Sparse Matrix (CSR) Format Conversion
 
 
34. Calculate Jaccard Index for Binary Classification
34. Calculate Jaccard Index for Binary Classification
 
 
README.md
README.md
 
 

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Solving Daily Machine Learning Problems

Overview

Welcome to my journey of learning and practicing Machine Learning fundamentals through consistent daily problem-solving. This repository serves as a collection of problems, solutions, explanations, and test cases related to core machine learning concepts. The main goal of this journey is to revisit and strengthen my understanding of machine learning foundations and improve my problem-solving and implementation skills through regular practice.

Learning Goals

  • Build strong fundamentals in machine learning and linear algebra
  • Understand and implement matrix and vector operations
  • Improve logical thinking and algorithmic problem-solving

What This Repository Contains

  • Daily ML Problems: One machine learning–related problem solved and shared every day
  • Solutions: Clear and correct Python implementations
  • Explanations: Simple, beginner-friendly explanations of concepts and logic
  • Test Cases: Multiple test cases to validate each solution

Progress

As I work through different machine learning problems, I will regularly update this repository with new solutions and explanations. This is an ongoing learning project, and the difficulty of problems may increase gradually over time.

About

A journey of learning and practicing machine learning fundamentals through daily problem-solving.

Topics

artificial-intelligencecalculusdeep-learninglinear-algebramachine-learningmlmodeltrainingpythonstatistics

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