Machine Learning With Python

Table of Contents

• Materials
• Machine Learning Introduction
  • Important Definitions in Machine Learning
  • Machine Learning Applications
  • Machine Learning Prerequisites
  • Machine Learning Overview
• Machine Learning : Regression
  • Linear Regression

Materials

• Pattern recognition and machine learning Book.

Machine Learning Introduction

Important Definitions in Machine Learning

• What is Artificial intelligence?
  • Artificial intelligence (AI) is the ability of a computer or a robot controlled by a computer to do tasks that are usually done by humans.
• What is Machine Learning?
  • Machine learning is a subfield of artificial intelligence, which is broadly defined as the capability of a machine to imitate intelligent human behavior.
• What is Big data?
  • big data is larger, more complex data sets, especially from new data sources.
• What is Data Science?
  • preparing data for analysis, including cleaning, aggregating, and manipulating the data to perform advanced data analysis. After data analytics we could predict or draw insights from data.

Machine Learning Applications

• Facial Recognition
• Recommendation Engines
• Optical Character Recognition
• Advertising and business intellgince
• Filtering News Feed
• Self Driving “Autonomous” Vehicles
• Fraud Detection

Machine Learning Prerequisites

• Mathematical Knowledge: Linear Algebra, Probability and Statistics, Multivariant Calculas
• Programming with Python

Machine Learning Overview

1. ML Introduction
2. Supervised Machine Learning
   1. Regression
   2. Classisfication
   3. Neural Networks
   4. Support vector Machine
3. Unsupervised Machine Learning

---

Machine Learning : Regression

Supervised Learning develop predictive model based on both input and output data.

Unsupervised learning group and interpret data based on input data only.

Linear Regression

• Important Equations:
  • Slope: (y2-y1)/(x2-x1), slope can be zero, +ve, or -ve.
  • Linear Regression: Y'= aX + b
    • a is the slope, and b is the y-intercept when X = 0
  • Hypothesis function: h(x) = theta_0 + theta_1*X

• Important Definitions:
  • X = inputs
  • Y = output
  • m = number of rows
  • n = numbe of features
  • h(x) = predectied values
  • cost J = error value
  • Theta = X parameters
• Big Learning Rate vs. Small Learning Rate (Alpha)
  • choosing the learning rate wether being small or large will compensate between accuracy and speed of the algorithm.
• Linear Regression for Multiple Features (With Multivariables)
  • Multivariant Linear Regression equation

  

  • Multivariant Gradient For linear regression equation