codebygarrysingh
diff --git a/‎README.md‎
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diff --git a/‎lin-algebra-2var-lin-system/Linear systems with 2 variables.ipynb‎ renamed to ‎linear-algebra-py/Linear_systems_with_2_variables.ipynb‎ b/‎lin-algebra-2var-lin-system/Linear systems with 2 variables.ipynb‎ renamed to ‎linear-algebra-py/Linear_systems_with_2_variables.ipynb‎
diff --git a/‎lin-algebra-2var-lin-system/Python matrices and numpy.ipynb‎ renamed to ‎linear-algebra-py/Python_matrices_and_numpy.ipynb‎ b/‎lin-algebra-2var-lin-system/Python matrices and numpy.ipynb‎ renamed to ‎linear-algebra-py/Python_matrices_and_numpy.ipynb‎
diff --git a/‎lin-regression-costfn-gradientdesc-ml/Cost Function 2D & 3D plotting (Housing Prices).ipynb‎ renamed to ‎linear-regression-ml-py/Cost_function_2d_&_3d_plotting.ipynb‎ b/‎lin-regression-costfn-gradientdesc-ml/Cost Function 2D & 3D plotting (Housing Prices).ipynb‎ renamed to ‎linear-regression-ml-py/Cost_function_2d_&_3d_plotting.ipynb‎
diff --git a/‎lin-regression-costfn-gradientdesc-ml/Feature scaling and learning rate optimization.ipynb‎ renamed to ‎linear-regression-ml-py/Feature_scaling_and_learning_rate_optimization.ipynb‎ b/‎lin-regression-costfn-gradientdesc-ml/Feature scaling and learning rate optimization.ipynb‎ renamed to ‎linear-regression-ml-py/Feature_scaling_and_learning_rate_optimization.ipynb‎
diff --git a/‎lin-regression-costfn-gradientdesc-ml/Gradient Decent for optimization of cost function.ipynb‎ renamed to ‎linear-regression-ml-py/Gradient_descent_for_optimizing_cost_function.ipynb‎ b/‎lin-regression-costfn-gradientdesc-ml/Gradient Decent for optimization of cost function.ipynb‎ renamed to ‎linear-regression-ml-py/Gradient_descent_for_optimizing_cost_function.ipynb‎
diff --git a/‎lin-regression-costfn-gradientdesc-ml/Multi feature linear regression.ipynb‎ renamed to ‎linear-regression-ml-py/Multi_feature_linear_regression.ipynb‎ b/‎lin-regression-costfn-gradientdesc-ml/Multi feature linear regression.ipynb‎ renamed to ‎linear-regression-ml-py/Multi_feature_linear_regression.ipynb‎
diff --git a/‎lin-regression-costfn-gradientdesc-ml/Unilateral Linear Regression (Housing Prices).ipynb‎ renamed to ‎linear-regression-ml-py/Unilateral_linear_regression.ipynb‎ b/‎lin-regression-costfn-gradientdesc-ml/Unilateral Linear Regression (Housing Prices).ipynb‎ renamed to ‎linear-regression-ml-py/Unilateral_linear_regression.ipynb‎
diff --git a/‎lin-regression-costfn-gradientdesc-ml/Vectorization using python numpy.ipynb‎ renamed to ‎linear-regression-ml-py/Vectorization_using_python_numpy.ipynb‎ b/‎lin-regression-costfn-gradientdesc-ml/Vectorization using python numpy.ipynb‎ renamed to ‎linear-regression-ml-py/Vectorization_using_python_numpy.ipynb‎
diff --git a/‎lin-regression-toolkit-py/regression_toolkit.py‎ renamed to ‎linear-regression-toolkit-py/regression_toolkit.py‎ b/‎lin-regression-toolkit-py/regression_toolkit.py‎ renamed to ‎linear-regression-toolkit-py/regression_toolkit.py‎
@@ -1,2 +1,40 @@
-# linear-ml-notebooks
-Repository contains Jupiter notebooks covering work on linear algebra and linear regression ml models
+# Machine Learning Fundamentals
+
+Welcome to the Machine Learning Fundamentals repository! This collection of Jupyter notebooks covers essential concepts and techniques in machine learning, designed to help you grasp the foundations of this exciting field.
+
+## Table of Contents
+
+1. [Linear Regression](linear-regression-ml-py)
+
+   - Introduction to linear regression and its applications
+   - Implementing linear regression from scratch
+   - Understanding cost functions and their role in optimization
+   - Examples of common cost functions
+   - Deep dive into the gradient descent optimization algorithm
+   - Practical examples and variations of gradient descent
+   - The importance of feature scaling in preprocessing
+   - Standardization and normalization techniques
+   - Techniques to create informative features from raw data
+   - Real-world examples and best practices
+   - Strategies for optimizing the learning rate in machine learning
+   - Balancing convergence speed and stability
+
+2. [Linear Algebra](linear-algebra-py)
+
+   - Solving systems of linear equations
+   - Linear algebra methods for equation solving
+   - Exploring key linear algebra concepts in machine learning
+   - Matrix operations, vector spaces, and more
+   - Leveraging vectorization for efficient computation
+   - Performance improvements in machine learning
+   
+3. [Linear Regression Toolkit](linear_regression_toolkit)
+    - Predefined implementations of common linear regression methods
+    - Easily apply linear regression to your datasets
+
+## Getting Started
+
+To get the most out of these notebooks, make sure you have Jupyter Notebook installed on your local machine. You can install it using the following command:
+
+```bash
+pip install notebook