CloudxLab Blog | Page 3 of 13 | Learn AI, Machine Learning, Deep Learning, Devops & Big Data

Summer Sale 2022

The Summer Sale is here!

The world in the future is complex, every aspect of services that we use will be AI based (most of them already are). The world of Data and AI. This thought often appears scary to our primitive brains and more so to people who see programming as Egyptian hieroglyphs but may I suggest an alternate approach to this view, instead of looking at how the technologies in the future are going to take away our job, we should learn to harness the power of AI and BIG DATA to be better equipped for the future.

At CloudxLab, We believe in providing Quality over Quantity and hence each one of our courses is highly rated by our learners, the love shown by our community has been tremendous and makes us strive for improvement, we strive to ensure that education does not feel like a luxury but a basic need that everybody is entitled to. Keeping this in mind, we bring forth the “#NoPayApril” where you can access some of the most sought after and industry-relevant courses completely free of cost. During #NoPayApril anybody who is signing up at CloudxLab will be able to access the contents of all the self-paced courses. This offer will be running from April 3 till April 30, 2022.

CloudxLab provides an online learning platform where you can learn and practice Data Science, Deep Learning, Machine Learning, Big Data, Python, etc.

When the highly competitive and commercialized education providers have cluttered the online learning platform, CloudxLab tries to break through with a disruptive change by making upskilling affordable and accessible and thus, achievable.

Happy Learning!

Classification metrics and their Use Cases

In this blog, we will discuss about commonly used classification metrics. We will be covering Accuracy Score, Confusion Matrix, Precision, Recall, F-Score, ROC-AUC and will then learn how to extend them to the multi-class classification. We will also discuss in which scenarios, which metric will be most suitable to use.

First let’s understand some important terms used throughout the blog-

True Positive (TP): When you predict an observation belongs to a class and it actually does belong to that class.

True Negative (TN): When you predict an observation does not belong to a class and it actually does not belong to that class.

False Positive (FP): When you predict an observation belongs to a class and it actually does not belong to that class.

False Negative(FN): When you predict an observation does not belong to a class and it actually does belong to that class.

All classification metrics work on these four terms. Let’s start understanding classification metrics-

Skin Cancer prediction by image processing through CNN

The objective of this problem is to classify skin cancer detections, around 1.98% of people in the world are affected due to skin cancer and this would help the community diagnose it in early stages where there is limited clinical expertise.

Complete code for this project can be found here: https://github.com/sudeepgarg86/DatascienceProjects

This is a HAM10000 dataset containing 10012 images, classifying 7 types of cancer, and each instance has been resized to 64*64 RGB image for this problem, associated with label.

How to use Numpy Meshgrid to generate data?

When you are generating data, the Meshgrid function of Numpy helps us to generate the coordinates data from individual arrays.

Say, you have a set of values of x 0.1, 0.2, 0.3, 0.4. You want to generate all possible points by combining these four values with say three values for y: 4, 5, 6.

This can be done very easily by using meshgrid function of Numpy as follows:

import numpy as np
x, y = np.meshgrid([0.1, 0.2, 0.3, 0.4], [4, 5, 6])
import matplotlib.pyplot as plt
plt.scatter(x, y)

The Scatter plot generated after meshgrid

To learn more about it, please visit Numpy Meshgrid Reference

DevOps: An Introduction

Learn the basic concepts of DevOps. You will also learn the benefits of using DevOps practices in your application.

What is DevOps?

DevOps is essentially cultural philosophies, practices, and tools to help deliver your applications and services and tools to your huge number of users.

Benefits of DevOps

MLOps (Machine Learning Operations) – A Complete Hands-On Guide with Case Study

Learn about MLOps with a case study and guided projects. This will also cover DevOps, Software Engineering, System Engineering in the right proportions to ensure your understanding is complete.

You will learn about MLOps with a case study and guided projects. This will also cover DevOps, Software Engineering, System Engineering in the right proportions to ensure your understanding is complete.

Introduction

As part of this series of blogs, I want to help you understand MLOps. MLOps stands for Machine Learning Ops. It is basically a combination of Machine Learning, Software Development, and Operations. It is a vast topic. I want to first establish the value of MLOps and then discuss the various concepts to learn by way of guided projects in a very hands-on manner. If you are looking for a theoretical foundation on MLOps, please follow this documentation by Google.

Case Study

Objective

When I was working with an organization, we wanted to build and show the recommendation. The main idea was that we need to show something interesting to the users in a small ad space that people would want to use. Since we had the anonymized historical behavior of the users, we settled down to show recommendations of apps that they would also like based on their usage of various apps at the time when the ads were being displayed.

Practice questions on Data Structures and Algorithms for Software Engineer Roles

Welcome!

You might have seen many people getting anxious for coding interviews. Mostly you are tested for Data Structures and Algorithms in a coding interview. It can be quite challenging and stressful considering the vastness of the topic.

Software Engineers in the real world have to do a lot of problem-solving. They spend enough time understanding the problem before actually coding it. The main reason to practice Data Structures and Algorithms is to improve your problem-solving skills. So a Software Engineer must have a good understanding of both. But where to practice?

ClouldxLab offers a solution. We have come up with some amazing questions which would help you practice Data Structures and Algorithms and make you interview-ready.

So what are you waiting for? Encourage the aspiring Software Engineer in you, by waking up the problem solver in you. Practice the following questions: https://cloudxlab.com/assessment/playlist-intro/566/data-structures-and-algorithms-questions

All the best!

Practice questions for Machine Learning Engineer Roles

Welcome!

You might have come across several posts which focus only on the theoretical questions for you to prepare for a machine learning engineer role. But is the theoretical preparation enough?

The ML Engineers in the real world do much more than just making models. They spend enough time understanding the data before actually building a model. For this, they should be able to perform different operations on the data, make intuitions and manipulate the data as per the needs. So an ML Engineer must be able to how to play with data and tell some intuition stories.

Pandas is a library for Python to perform various operations on data. Numpy is a famous Python library for numerical computations. It is often expected that an ML Engineer is well-versed with both of these libraries. But where to practice?

ClouldxLab offers a solution. We have come up with some amazing questions which would help you practice Python, Pandas and Numpy hands-on and make you interview ready.

So what are you waiting for? Encourage the aspiring ML Engineer in you, by waking up the problem solver in you. Practice the following questions: https://cloudxlab.com/assessment/playlist-intro/862/machine-learning-prerequisite-mains-10th-july-2021.

All the best!

Getting Started with various tools at CloudxLab

Welcome!

We are happy to announce that we have come up with a new consolidated playlist, which summaries about various tools present at CloudxLab environment, how to use them and where to learn about them.

This would be incrementally improved as new technologies keep getting installed on the lab.

You may find the playlist here.

In this playlist, there is a dedicated slide for each technology. For example, if you want to understand how to use Pandas on the lab, go to the slide named Pandas.

Upon clicking on Pandas, you would be able to see the Pandas guide as follows:

As you could see, this slide contains all the basic information needed such as:

the purpose of the library
link for the official home page
link for the official documentation
related resources you could use to learn about the library.
instructions on how to use it on the CloudxLab environment.
1-2 lines of sample examples to use it, such as how to inport the library and how to check the version.

We hope that this will be a great starting guide for our users and makes their job of getting started easier.

Happy learning!

When to use While, For, and Map for iterations in Python?

Python has a really sophisticated way of handling iterations. The only thing it does not have “GOTO Labels” which I think is good.

Let us compare the three common ways of iterations in Python: While, For and Map by the way of an example. Imagine that you have a list of numbers and you would like to find the square of each number.

nums = [1,2,3,5,10]
result = []
for num in nums:
    result.append(num*num)
print(result)

It would print [1, 4, 9, 25, 100]