# Steps in Building a Machine Learning Project

In this post, we will be discussing the basic steps in building a machine learning project. The process of building an ML project includes collecting the dataset, cleaning it, processing the data, training and testing of the model and deploying it. All the steps are explained briefly below.

# Data collection

Data collection is the first step in developing any machine learning model. The source and type of data play an important role in determining whether the upcoming steps will be easy or complex. …

# any() and all() in Python

In this post, we will be looking into any() and all() functions in Python. First, let us discuss the any() function.

# 👉 any()

The any() function takes an iterable as an argument: any(iterable).

The iterable can be a list, tuple or dictionary.

The any() function returns ‘True’ if any element in the iterable is true. However, it returns ‘False’ if the iterable passed to the function is empty.

This function is similar to the code block below

`def any(iterable):    for element in iterable:        if element:            return True    return FalseCOPY`

Below is an example of using any for returning ‘True’ for numbers…

# What is Logistic Regression

As you may be knowing Logistic Regression is a Machine Learning algorithm. It is used for binary classification problems. We also have multiclass logistic regression, where we basically re-run the binary classification multiple times.

It is a linear algorithm with a non-linear transform at the output. The input values are combined linearly using weights to predict the output value. The output value which is being modelled is a binary value rather than a numeric value.

Suppose we have the results of a set of students, where the criteria for passing is that the student should score 50% or more. …

We have seen the basic operation of convolution in the previous post.

Knowing Convolution Basics

In this post, we will be discussing padding in Convolutional Neural Networks. Padding is the number of pixels that are added to an input image. Padding allows more space for the filter to cover the image and it also helps in improving the accuracy of image analysis.

It implies no padding at all. That is input image is fed into the filter as it is. So if we consider the…

# Knowing Convolution Basics

In this article, we are going to learn about the grayscale image, colour image and the process of convolution.

# Grayscale image

A grayscale image where the image is represented as only the shades of grey. The intensity of the various pixels of the image is denoted using the values from 0 to 255. i.e., from black to white in terms of an 8-bit integer. It uses only one channel.

# Colour image

Coloured images are constructed by combining red, green and blue (RGB) colours in variable proportions. These 3 colours and hence they are called the primary colours. …

# What is Gradient descent algorithm?

The gradient descent algorithm is an approach to find the minimum point or optimal solution for a given dataset. It follows the steepest descent approach. That is it moves in the negative gradient direction to find the local or global minima, starting out from a random point. We use gradient descent to reach the lowest point of the cost function.

In Machine Learning, it is used to update the coefficients of our model. It also ensures that the predicted output value is close to the expected output

For example, in Linear Regression, where we separate the output using a linear…

# Confusion Matrix

Today we are going to discuss some of the metrics in classification.

Let us first look at the confusion matrix.

The Accuracy is calculated as:-

# Population and Sample

Today let’s look at the topic population and sample.

Population refers to the collection of all the elements in a dataset. It is usually a large number and hence the processing of data of the whole population is a tedious task.

Population data is hard to observe and to deal with. So we use what is called a sample for the collection of data. Time and resources required are minimised to a great extent when we use sample.

So we consider a small part of the population as our dataset. This small portion of the population is called a sample.

# Gaussian Distribution

It is a type of continuous probability distribution for a random variable. It is also called as Normal distribution.

The general form of its probability density function is;

Here the parameter µ is the mean or expectation of the distribution and σ is the standard deviation.

When the mean becomes 0 and variance becomes 1 in a Gaussian distribution, it becomes a Standard Normal Distribution.

Gaussian distribution follows a bell-shaped curve and hence generally known as a bell curve.

# Knowing Probability Distributions…

A probability distribution is a function that describes the chances of finding a random variable over a defined range.

Below is the list of some of the most used probability distribution functions.

• Normal(Gaussian) distribution.
• Log-Normal distribution.
• Bernoulli distribution.
• Binomial distribution.
• Poisson distribution.

Today let’s see what are Bernoulli, Binomial and Poisson distribution.

Bernoulli distribution:-

The distribution of random variables which can take up two values, i.e. either success(1) or failure(0) is called a Bernoulli distribution. Some examples are coin flip, whether a patient tested has cancer or not, etc.

Consider we have a random variable X, which follows Bernoulli distribution… 