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As we work with different versions of Python, as well as working with different projects with different versions of python libraries, it is important to be aware of the virtual environments and to work with them.

https://deepblade.com/programming/python-programming/virtual-environments-python/

In some Machine Learning Algorithms, we have to measure the distance between data points.

For example, in Supervised Machine Learning algorithms like K-Nearest Neighbours, we have to select the nearest data points using any kind of Distance Metric. And in Unsupervised Machine Learning algorithms like K-Means Clustering, Hierarchical Clustering, we have to use any type of Distance Metric to determine the similarities and differences between the data points.

Learn more: https://deepblade.com/artificial-intelligence/machine-learning/measure-distance-between-data-points-in-machine-learning/

Hierarchical Clustering algorithm is another popular unsupervised Machine Learning algorithm. It is able to group unlabeled datasets into clusters. It means this algorithm is allowed to identify similarities and differences in data points and divide them into several groups.

As you may remember, in K-Means clustering, we have to decide the number of clusters(K) first. But here we do not need to decide the number of clusters first. That is an advantage of Hierarchical Clustering.

In this tutorial, you will learn,

Different approaches of Hierarchical Clustering

Agglomerative Clustering

Divisive Clustering

How does Hierarchical Clustering work?

How do choose the Number of Clusters in Hierarchical Clustering?

Applications of Hierarchical Clustering

Practical implementation of Hierarchical Clustering

Algorithms can be classified based on the problem they are trying to solve, such as sorting algorithms, searching algorithms, and so on, or they can be classified based on the problem-solving approach.

Here we are just clustering together the algorithms that use a similar problem-solving approach. Based on these criteria, algorithm types can be,

Recursive Algorithms

Divide and Conquer Algorithms

Dynamic Programming Algorithms

Greedy Algorithms

Brute Force Algorithms

Randomized Algorithms

Learn more: https://deepblade.com/data-structures-algorithms/different-types-of-algorithms/

K-Means clustering algorithm is a popular unsupervised Machine Learning algorithm. In Unsupervised Machine Learning, we use Unlabeled data and those are we can use to solve clustering problems.

In this tutorial, you will learn,

Introduction to K-Means Clustering algorithm

How does K-Means Clustering algorithm work

How to choose the right number of Clusters

Applications of K-Means Clustering

Practical implementation of K-Means Clustering

In Deep Learning we can use Convolutional Neural Networks for computer vision tasks. When we work with a large dataset at that time, it can take a long time for that model to train. It can take days or even weeks.

So, in such cases, we can use Transfer learning. There we use a pre-trained model. At the moment there are many popular pre-trained models that we can use for computer vision tasks. VGG16. VGG19, Resnet 50 are some of them.

In this tutorial, you will learn,

What is Transfer Learning?

Popular pre-trained models used in Computer Vision

It is a challenging task to choose the best hyperparameters for our model. We can accomplish that using GridSearchCV or RandomizedSearchCV. Those are very useful methods that can be used for hyperparameter optimization.

In this tutorial, you will learn,

What is Hyperparameter optimization

What is GridSearchCV

What is RandomizedSearchCV

Implement Hyperparameter tuning

Using GridSearchCV

Using RandomizedSearchCV

What is Hyperparameter optimization?

To customized a machine learning model, we can use hyperparameters. Hyperparameters are set manually to guide the learning process. Values taken as hyperparameters can vary from dataset to dataset. So, selecting the best hyperparameters for a particular dataset is called hyperparameter optimization or hyperparameter tuning.

Support vector machine also known as SVM is a supervised machine learning algorithm that can be used for both regression and classification problems. However, SVM is mostly used for classification problems in the machine learning world.

The goal of SVM is to find a hyperplane in N-dimensional space. The hyperplane is a boundary that classifies data points into classes.

In this tutorial, you will learn,

Types of Support Vector Machine

How works support vector machine algorithm?

Advantages and Disadvantages of support vector machine

Applications of support vector machine

Implement SVM using Python

Naive Bayes is a supervised machine learning algorithm, which is used for classification problems. Naive Bayes algorithm based on Bayes theorem. It is a simple algorithm. But also fast, accurate, and most effective classification algorithm.

You know there are a set of features in a dataset that we used in machine learning. Here those features are considered independent and that’s why the word ‘Naive’ has been added to this. The word ‘Bayes’ has been added because of the use principle of Bayes’ Theorem.

In this tutorial, you will learn,

What is Bayes Theorem

How works Naive Bayes Classifier

Types of Naïve Bayes

Advantages of Naive Bayes

Disadvantages of Naive Bayes

Applications of Naïve Bayes

Naive Bayes Classifier using Python

What is Transfer Learning

The simple idea of transfer learning is, After Neural Network learned from one task, apply that knowledge to another related task. It is a powerful idea in Deep Learning. You all know in Computer vision and Natural Language Processing tasks required high computational costs and time. So, we can simplify those tasks using Transfer Learning. For example, after we trained a model using images to classify Cars, then that model we can use to recognize other vehicles like trucks.

How works Transfer Learning

For Example, in computer vision tasks, for instance, neural organizations ordinarily attempt to distinguish edges in the previous layers, shapes in the center layer, and some undertaking explicit elements in the later layers. In transfer learning, the early and center layers are utilized and we just drop the last layers. Then we can apply new layers to our CNN model corresponding to our new task.

Data Augmentation for images

Data augmentation is one of the techniques used to improve performance in computer vision systems. Most computer vision tasks could use more data. Using data augmentation, we can increase the size of our data set.Data augmentation can apply to various machine learning domains. In this tutorial, we will talk about how to use data augmentation in Computer Vision tasks. We can increase the size of the dataset by following methods.

Horizontal Shifting

Vertical Shifting

Rotation

Zooming

Brightness

Add noise

Cropping

Scaling

Saturation

Artificial Intelligence (AI) has grown to be very popular nowadays. It is the simulation of human intelligence processes by Computer Systems.

Decision tree is another supervised machine learning algorithm that can use for both regression and classification problems. But mostly used for classification problems. Decision tree algorithm is a tree-structured classifier. So, it has nodes and edges. There are two different nodes, which are the Decision node and the Leaf node.

In this tutorial, you will learn,

Important terminologies in Decision Tree

How works Decision Tree algorithm

Advantages of Decision Tree algorithm

Disadvantages of Decision Tree algorithm