Quick Start

Welcome

Welcome to the Mathematics note section of Mindect. You can learn various topics along with it's code in this section. Here's a brief tour of what you will learn in this sections.

Linear Algebra

The first topic you can explore is Linear Algebra in which you can learn the following topics.

• Introduction to Numpy Arrays
• Linear Systems as Matrices
• Introduction to the Numpy.lanalg sub-library
• Gaussian Elimination
• Vector Operations: Scalar Multiplication, Sum and Dot Product of Vectors
• Matrix Multiplication
• Linear Transformation
• Linear Transformatins and Neural Networks
• Interpreting Eigenvalues and Eigenvectors
• Application of Eigenvalues and Eigenvectors: Webpage navigation model and PCA

Calculus

The second topic you can learn is Calculus in which you can the following topics are being covered.

• Differentiation in Python: Symbolic, Numerical and Automatic
• Optimizing Functions of One Variable: Cost Minimization
• Optimization Using Gradient Descent in One Variable
• Optimization Using Gradient Descent in Two Variables
• Optimization Using Gradient Descent: Linear Regression
• Regression with Perceptron
• Classification with Perceptron
• Optimization Using Newton's Method
• Neural Network with Two Layers

Probability and Statistics

The final topic to explore is P & S, here a summary of topics you can learn about

• Four Birthday Problems
• Monty Hall Problem
• Exploratory Data Analysis: Intro to pandas
• Exploratory Data Analysis: Exploring your data
• Naive Bayes
• Summary statistics and visualization of Data Sets
• Exploratory Data Analysis: Data Visualization and Summary
• Simulating Dice Rolls with Numpy
• Loaded
• Sampling data from different distribution and studying the distribution of sample mean
• Exploratory Data Analysis: Linear Regression
• Exploratory Data Analysis: Confidence Intervals with Hypoothesis Testing
• A/B Testing

Links

Here are link to learn each item