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In this article, we will build the most basic machine learning model called the Linear regression and we will implement it using just python NumPy. First, we will look at our dataset, then talk about a general Supervised Learning algorithm process followed by hypothesis representation, loss function, and Gradient Descent algorithm. After that, we will write the LinReg
class and test it out on our data.
Simply put, we have a dataset with X(features) and y(labels) and we want to fit a straight line to it. Let’s use the following dataas the motivating example:
Suppose you are a Physics student and you want to know how much you can score on the test (out of 100) given the number of hours you study.
## Imoprting required libraries.
import numpy as np
import matplotlib.pyplot as plt
import pandas as pd
## Reading the csv file.
df = pd.read_csv('data.csv')
## Displayinng the first five elements of the dataframe.
df.head(10)
Table 1 dataset
In this data Hours is X(feature) (remember we have only one feature in this data i.e hours, although there can be multiple features like how many problems have you solved for practice etc.). Scores are y(labels/target) for each example we have. This is what the data looks like when we plot it.
#data-science #python #artificial-intelligence #machine-learning
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Welcome to my Blog , In this article, you are going to learn the top 10 python tips and tricks.
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#python #python hacks tricks #python learning tips #python programming tricks #python tips #python tips and tricks #python tips and tricks advanced #python tips and tricks for beginners #python tips tricks and techniques #python tutorial #tips and tricks in python #tips to learn python #top 30 python tips and tricks for beginners
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Welcome to my Blog, In this article, we will learn python lambda function, Map function, and filter function.
Lambda function in python: Lambda is a one line anonymous function and lambda takes any number of arguments but can only have one expression and python lambda syntax is
Syntax: x = lambda arguments : expression
Now i will show you some python lambda function examples:
#python #anonymous function python #filter function in python #lambda #lambda python 3 #map python #python filter #python filter lambda #python lambda #python lambda examples #python map
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Let’s begin our journey with the truth — machines never learn. What a typical machine learning algorithm does is find a mathematical equation that, when applied to a given set of training data, produces a prediction that is very close to the actual output.
Why is this not learning? Because if you change the training data or environment even slightly, the algorithm will go haywire! Not how learning works in humans. If you learned to play a video game by looking straight at the screen, you would still be a good player if the screen is slightly tilted by someone, which would not be the case in ML algorithms.
However, most of the algorithms are so complex and intimidating that it gives our mere human intelligence the feel of actual learning, effectively hiding the underlying math within. There goes a dictum that if you can implement the algorithm, you know the algorithm. This saying is lost in the dense jungle of libraries and inbuilt modules which programming languages provide, reducing us to regular programmers calling an API and strengthening further this notion of a black box. Our quest will be to unravel the mysteries of this so-called ‘black box’ which magically produces accurate predictions, detects objects, diagnoses diseases and claims to surpass human intelligence one day.
We will start with one of the not-so-complex and easy to visualize algorithm in the ML paradigm — Linear Regression. The article is divided into the following sections:
Need for Linear Regression
Visualizing Linear Regression
Deriving the formula for weight matrix W
Using the formula and performing linear regression on a real world data set
Note: Knowledge on Linear Algebra, a little bit of Calculus and Matrices are a prerequisite to understanding this article
Also, a basic understanding of python, NumPy, and Matplotlib are a must.
Regression means predicting a real valued number from a given set of input variables. Eg. Predicting temperature based on month of the year, humidity, altitude above sea level, etc. Linear Regression would therefore mean predicting a real valued number that follows a linear trend. Linear regression is the first line of attack to discover correlations in our data.
Now, the first thing that comes to our mind when we hear the word linear is, a line.
Yes! In linear regression, we try to fit a line that best generalizes all the data points in the data set. By generalizing, we mean we try to fit a line that passes very close to all the data points.
But how do we ensure that this happens? To understand this, let’s visualize a 1-D Linear Regression. This is also called as Simple Linear Regression
#calculus #machine-learning #linear-regression-math #linear-regression #linear-regression-python #python
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Take your current understanding and skills on machine learning algorithms to the next level with this article. What is regression analysis in simple words? How is it applied in practice for real-world problems? And what is the possible snippet of codes in Python you can use for implementation regression algorithms for various objectives? Let’s forget about boring learning stuff and talk about science and the way it works.
#linear-regression-python #linear-regression #multivariate-regression #regression #python-programming
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Today we will implement one of the most famous machine learning algorithms, Simple Linear Regression from scratch and then with scikit-learn module and then compare the two models. The code for this can be found on this Github Link.
#linear-regression-python #linear-regression #data-science #python #machine-learning