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ROC-AUC-for-Multi-Class-Classification-Release Highlights for Scikit-learn 0.22

Posted on June 10, 2020August 10, 2020 by Dexlab

Today we are going to learn about the new releases from Scikit-learn version 0.22, a machine learning library in Python. We, through this video tutorial, aim to learn about the much talked about new release wherein ROC-AUC curve supports Multi Class Classification. Prior to this version, Scikit-learn did not have a function to plot the ROC curve.

To access our previous tutorial on the plotting of the ROC curve, click here.

The ROC-AUC score function can also be used in multi-class classification. Two averaging strategies are currently supported: the one-vs-one (OvO) algorithm computes the average of the pairwise ROC AUC scores and the one-vs-rest (OvR) algorithm computes the average of the ROC AUC scores for each class against all other classes.

In both cases, the multiclass ROC AUC scores are computed from probability estimates that a sample belongs to a particular class according to the model. The OvO and OvR algorithms support weighting uniformly (average=’macro’) and weighting by prevalence (average=’weighted’).

To begin with, we import multi classification, SVC and roc_auc_score. Then we specify the number of classes we want in the multi-classification function. Then we apply the SVC function and finally the roc_auc_score one. This function will give us the probable prediction for all the classes and we will then choose the one that has the highest probability. When we run it we get a ROC_AUC score of 0.99.

The code sheet is provided in a Github repository here.

For more on this do watch the video attached herewith. This tutorial was brought to you by DexLab Analytics. DexLab Analytics is a premiere Machine Learning institute in Gurgaon.

Watch the video here.

ROC-Curve-New-Plotting-API-Release Highlights for Scikit-learn 0.22

Posted on June 3, 2020August 10, 2020 by Dexlab

Today we are going to learn about the new releases from Scikit-learn version 0.22, a machine learning library in Python. We, through this video tutorial, aim to learn about the much talked about new release called Plotting API. Prior to this version, Scikit-learn did not have a function to plot the ROC curve.

A new plotting API is available for creating visualizations. The new API allows for quickly adjusting the visuals of a plot without involving any recomputation. It is also possible to add different plots to the same figure. In this tutorial we are going to study the plotting of the ROC curve.

The code sheet is provided in a Github repository here.

We will attempt to plot the ROC curve on two different algorithms and compare which one is a better function. First we choose to make a classification data. Then we go on to plot the ROC curve using SVC classifier and then further plot the curve using a random forest classifier.

Fig. 1

Fig. 1

For more on this do watch the video attached herewith. This tutorial was brought to you by DexLab Analytics. DexLab Analytics is a premiere Machine Learning institute in Gurgaon.

Stacking Regressor – Latest Releases of Scikit-Learn 0.22

Posted on May 18, 2020August 10, 2020 by Dexlab

Today we are going to learn about the new releases from Scikit-learn version 0.22, a machine learning library in Python. First we learn how to install it on our systems. Then, we come to the much talked about new release called stacking regression.

Now, how does stacking regression work? Well, you have been using machine learning algorithms like Decision Tree or Random Forest. Have you heard of Voter Classifier? It is an algorithm in Scikit-learn. Ensemble algorithm is a combination of two or more algorithms to make it stronger.

When working on a set of data, we must apply all these algorithms to get predicted values. Then we vote out classified predicted values in Voter Classifier. Stacking Classifier is different. What we are doing in it is stacking together the predicted values to make a new input.

Initially, we make prediction by using various algorithms separately. Their results or output are then concatenated together. Then we use this output as a new input and apply the algorithms to it to get target variable. This method is known as stacking regression.

We try this out on a data set that can be taken from a github repository the link to which is given below.

Then we use two algorithms as estimators. Then we use stacking regression to build a model. For more on this do watch the video attached herewith. This tutorial was brought to you by DexLab Analytics. DexLab Analytics is a premiere Machine Learning institute in Gurgaon.

The Impact of latitude on The Spread of COVID-19 (Part-I)

Posted on April 29, 2020May 4, 2020 by Dexlab

The COVID-19 pandemic has hit us hard as a people and forced us to bow down to the vagaries of nature. As of April 29, 2020, the number of persons infected stands at 31,39,523 while the number of persons dead stands at 2,18,024 globally.

This essay is on the phenomenon of detecting geographical variations in the mortality rate of the COVID-19 epidemic. This essay explores a specific range of latitudes along which a rapid spread of the infection has been detected with the help of data sets on Kaggle. The findings are Dexlab Analytics’ own. Dexlab Analytics is a premiere institute that trains professionals in python for data analysis.

For the code sheet and data used in this study, click below.

The instructor has imported all Python libraries and the visualisation of data hosted on Kaggle has been done through a heat map. The data is listed on the basis of country codes and their latitudes and there is a separate data set based on the figures from the USA alone.

Fig. 1.

The instructor has compared data from amongst the countries in one scenario and among states in the USA in another scenario. Data has been prepared and structured under these two heads.

Fig. 2.

The instructor has prepared the data according to the mortality rate of each country and it is updated to the very day of working on the data, i.e. the latest updated figures are presented in the study. When the instructor runs the program, a heat map is produced.

For more on this, do go through the half-an-hour long program video attached herewith. The rest of the essay will be featured in subsequent parts of this series of articles.

Why Python is Preferred in AI and Machine Learning?

Posted on March 11, 2020March 12, 2020 by Dexlab

Why Python is Preferred in AI and Machine Learning?

Python has become one of the leading coding languages across the globe and for more reasons than one. In this article, we evaluate why Python is beneficial in the use of Machine Learning and Artificial Intelligence applications.

Artificial intelligence and Machine Learning are profoundly shaping the world we live in, with new applications mushrooming by the day. Competent designers are choosing Python as their go-to programming language for designing AI and ML programs.

Artificial Intelligence enables music platforms like Spotify to prescribe melodies to users and streaming platforms like Netflix to understand what shows viewers would like to watch based on their tastes and preferences. The science is widely being used to power organizations with worker efficiency and self-administration.

Machine-driven intelligence ventures are different from traditional programming languages in that they have innovation stack and the ability to accommodate an AI-based experiment. Python has these features and more. It is a steady programming language, it is adaptable and has accessible instruments.

Here are some features of Python that enable AI engineers to build gainful products.

An exemplary library environment

“An extraordinary selection of libraries is one of the primary reasons Python is the most mainstream programming language utilized for AI”, a report says. Python libraries are very extensive in nature and enable designers to perform useful activities without the need to code them from scratch.

Machine Learning demands incessant information preparation, and Python’s libraries allows you to access, deal with and change information. These are libraries can be used for ML and AI: Pandas, Keras, TensorFlow, Matplotlib, NLTK, Scikit-picture, PyBrain, Caffe, Stats models and in the PyPI storehouse, you can find and look at more Python libraries.

Basic and predictable

Python has on offer short and decipherable code. Python’s effortless built allows engineers to make and design robust frameworks. Designers can straightway concentrate on tackling an ML issue rather concentrating on the subtleties of the programming language.

Moreover, Python is easy to learn and therefore being adopted by more and more designers who can easily construct models for AI. Also, many software engineers feel Python is more intuitive than other programming languages.

A low entry barrier

Working in the ML and AI industry means an engineer will have to manage tons of information in a prodigious way. The low section hindrance or low entry barrier allows more information researchers to rapidly understand Python and begin using it for AI advancement without wasting time or energy learning the language.

Moreover, Python programming language is in simple English with a straightforward syntax which makes it very readable and easy to understand.

Conclusion

Thus, we have seen how advantageous Python is as a programming language which can be used to build AI models with ease and agility. It has a broad choice of AI explicit libraries and its basic grammar and readability make the language accessible to non-developers.

It is being widely adopted by developers across institutions working in the field of AI. It is no surprise then that artificial intelligence courses in Delhi and Machine Learning institutes in Gurgaon are enrolling more and more developers who want to be trained in the science of Python.

Skills Data Scientists Must Master in 2020

Posted on March 10, 2020May 23, 2020 by Dexlab

Skills Data Scientists Must Master in 2020

Big data is all around us, be it generated by our news feed or the photos we upload on social media. Data is the new oil and therefore, today, more than ever before, there is a need to study, organize and extract knowledgeable and actionable insights from it. For this, the role of data scientists has become even more crucial to our world. In this article we discuss the various skills, both technical and non-technical a data scientist needs to master to acquire a standing in a competitive market.

Technical Skills

Python and R

Knowledge of these two is imperative for a data scientist to operate. Though organisations might want knowledge of only one of the two programming languages, it is beneficial to know both. Python is becoming more popular with most organisations. Machine Learning using Python is taking the computing world by storm.

GitHub

Git and GitHub are tools for developers and data scientists which greatly help in managing various versions of the software. “They track all changes that are made to a code base and in addition, they add ease in collaboration when multiple developers make changes to the same project at the same time.”

Preparing for Production

Historically, the data scientist was supposed to work in the domain of machine learning. But now data science projects are being more often developed for production systems. “At the same time, advanced types of models now require more and more compute and storage resources, especially when working with deep learning.”

Cloud

Cloud software rules the roost when it comes to data science and machine learning. Keeping your data on cloud vendors like AWS, Microsoft Azure or Google Cloud makes it easily accessible from remote areas and helps quickly set up a machine learning environment. This is not a mandatory skill to have but it is beneficial to be up to date with this very crucial aspect of computing.

Deep Learning

Deep learning, a branch of machine learning, tailored for specific problem domains like image recognition and NLP, is an added advantage and a big plus point to your resume. Even if the data scientist has a broad knowledge of deep learning, “experimenting with an appropriate data set will allow him to understand the steps required if the need arises in the future”. Deep learning training institutes are coming up across the globe, and more so in India.

Math and Statistics

Knowledge of various machine learning techniques, with an emphasis on mathematics and algebra, is integral to being a data scientist. A fundamental grounding in the mathematical foundation for machine learning is critical to a career in data science, especially to avoid “guessing at hyperparameter values when tuning algorithms”. Knowledge of Calculus linear algebra, statistics and probability theory is also imperative.

SQL

Structured Query Language (SQL) is the most widely used database language and a knowledge of the same helps data scientist in acquiring data, especially in cases when a data science project comes in from an enterprise relational database. “In addition, using R packages like sqldf is a great way to query data in a data frame using SQL,” says a report.

AutoML

Data Scientists should have grounding in AutoML tools to give them leverage when it comes to expanding the capabilities of a resource, which could be in short supply. This could deliver positive results for a small team working with limited resources.

Data Visualization

Data visualization is the first step to data storytelling. It helps showcase the brilliance of a data scientist by graphically depicting his or her findings from data sets. This skill is crucial to the success of a data science project. It explains the findings of a project to stakeholders in a visually attractive and non-technical manner.

Non-Technical Skills

Ability to solve business problems

It is of vital importance for a data scientist to have the ability to study business problems in an organization and translate those to actionable data-driven solutions. Knowledge of technical areas like programming and coding is not enough. A data scientist must have a solid foundation in knowledge of organizational problems and workings.

Effective business communication

A data scientist needs to have persuasive and effective communication skills so he or she can face probing stakeholders and meet challenges when it comes to communicating the results of data findings. Soft skills must be developed and inter personal skills must be honed to make you a creatively competent data scientist, something that will set you apart from your peers.

Agility

Data scientist need to be able to work with Agile methodology in that they should be able to work based on the Scrum method. It improves teamwork and helps all members of the team remain in the loop as does the client. Collaboration with team members towards the sustainable growth of an organization is of utmost importance.

Experimentation

The importance of experimentation cannot be stressed enough in the field of data science. A data scientist must have a penchant for seeking out new data sets and practise robustly with previously unknown data sets. Consider this your pet project and practise on what you are passionate about like sports.

8 Skills a Python Programmer Should Master

Posted on February 3, 2020May 23, 2020 by Dexlab

8 Skills a Python Programmer Should Master

Python has become the lingua franca of the computing world. It has come to become the most sought after programming language for deep learning, machine learning and artificial intelligence. It is a favourite with programmers because it is easy to understand and learn and it achieves a lot more in terms of productivity as compared to other languages.

Python is a dynamic, high-level, general-purpose programming language that is useful for developing desktop, web and mobile applications that can also be used for complex scientific and numeric applications, data science, AI etc. Python focuses a lot on code readability.

From web and game development to machine learning, from AI to scientific computing and academic research, Data science and analysis, python is regarded as the real deal. Python is useful in domains like finance, social media, biotech etc. Developing large software applications in Python is also simpler due to its large amount of available libraries.

The Python developer usually deals with backend components, apps connection with third-party web services and giving support to frontend developers in web applications. Of course, one might create applications with use of different languages but pretty often Python is the language chosen for it – and there are several reasons for that.

In this article, we will walk through a structured approach to top 8 skills required to become a Python Developer. These skills are:

Core Python
Good grasp of Web Frameworks
Front-End Technologies
Data Science
Machine Learning and AI
Python Libraries
Multi-Process Architecture
Communication Skills

Core Python

This is the foundation of any Python developer. If one wants to achieve success in this career, he/she needs to understand the core python concepts. These include the following:

Iterators
Data Structures
Generators
OOPs concepts
Exception Handling
File handling concepts
Variables and data types

However, learning the core language (as mentioned above) is only the first step in mastering this language and becoming a successful Python developer.

Good grasp of Web Frameworks

By automating the implementation of redundant tasks, frameworks cut development time and enable developers to focus greatly on application logic rather than routine elements.

Because it is one of the leading programming languages, there is no scarcity of frameworks for Python. Different frameworks have their own set of advantages and issues. Hence, the selection needs to be made on the basis of project requirements and developer preference. There are primarily three types of Python frameworks, namely full-stack, micro-framework, and asynchronous.

A good Python web developer has incredible honing over either of the two web frameworks Django or Flask or both. Django is a high-level Python Web Framework that encourages a good, clean and pragmatic design and Flask is also widely used Python micro web framework.

Front-End Technologies (JavaScript, CSS3, HTML5)

Sometimes, Python developers must work with the frontend team to match together the server-side and the client-side. This means Python developers need a basic understanding of how the frontend works, what’s possible and what’s not, and how the application will appear.

While there is likely a UX team, SCRUM master, and project or product manager to coordinate the workflow, it’s still good to have a basic understanding of front-end tasks.

Data Science

Data science offers a world of new opportunities. Being a Python developer, there are several prerequisites you need to know starting with things you learn in high school mathematics, such as statistics, probability, etc. Some of the other parts of data science you need to understand, and use include SQL knowledge; the use of Python packages, data wrangling and data cleanup, analysis of data, and visualization of data.

Artificial Intelligence and Machine Learning

Artificial Intelligence and Machine Learning (as well as Deep Learning) are constantly growing. Python is the perfect programming language which is used in all the frameworks of Machine Learning and Deep Learning. This will be a huge plus for someone if he/she knows about this domain. If someone is into data science, then definitely digging in the Machine Learning topic would be a great idea.

Python Libraries

Python libraries certainly deserve a place in every Python Developer’s toolbox. Python has a massive collection of libraries, both native and third-party libraries. With so many Python libraries out there, though, it’s no surprise that some don’t get all the attention they deserve. Plus, programmers who work exclusively in one domain don’t always know about the goodies available to them for other kinds of work.

Python libraries are extensively used in simplifying everything from file system access, database programming, and working with cloud services to building lightweight web apps, creating GUIs, and working with images, ebooks, and Word files—and much more.

Multiprocessing Architecture

Multiprocessing refers to the ability of a system to support more than one processor at the same time. Applications in a multiprocessing system are broken to smaller routines that run independently. The operating system allocates these threads to the processors improving performance of the system. As a Python-Developer one should definitely know about the MVC (Model View Controller) and MVT (Model View Template) Architecture. Once you understand the Multi-Processing Architecture you can solve issues related to the core framework etc.

Communication Skills

In best software development firms the teams are made out of amazing programmers which work together to achieve the final goal – no matter if it means to finish the project, to create a new app or maybe to help a startup. However, working in a team means that a developer has to communicate well – not only to get the stuff done but also to keep the documentation clear so others can easily read and follow the thinking path to fully understand the idea.

Conclusion

In this write-up, we have elaborated on the top skills one needs to have to be a successful Python Developer. One must have a working knowledge of Core Python and a good grasp of Web Frameworks, Front-End Technologies, Data Science, Machine Learning and AI, Python Libraries, Multi-Process Architecture and Communication skills. Though there are a few more skills not listed in this blog, one can achieve success in developing large software applications by mastering all the above skills only.

As delineated in the article, Python is the new rage in the computing world. And it is no surprise then that more and more professionals are opting to take up courses teaching Machine learning using Python and python for data analysis.

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Netflix develops in own data science management tool and open sources it

Posted on January 28, 2020May 23, 2020 by Dexlab

Netflix in December last year introduced its own python framework called Metaflow. It was developed to apply to data science with a vision to make scalability a seamless proposition. Metaflow’s biggest strength is that it makes running the pipeline (constructed as a series of steps in a graph) easily movable from a stationary machine to cloud platforms (currently only the Amazon Web Services (AWS)).

What does Metaflow really do? Well, it primarily “provides a layer of abstraction” on computing resources. What it translates to is the fact that a programmer can concentrate on writing/working code while Metaflow will handle the aspect which ensures the code runs on machines.

Metaflow manages and oversees Python data science projects addressing the entire data science workflow (from prototype to model deployment), works with various machine learning libraries and amalgamates with AWS.

Machine learning and data science projects require systems to follow and track the trajectory and development of the code, data, and models. Doing this task manually is prone to mistakes and errors. Moreover, source code management tools like Git are not at all well-suited to doing these tasks.

Metaflow provides Python Application Programming Interfaces (APIs) to the entire stack of technologies in a data science workflow, from access to the data, versioning, model training, scheduling, and model deployment, says a report.

Netflix built Metaflow to provide its own data scientists and developers with “a unified API to the infrastructure stack that is required to execute data science projects, from prototype to production,” and to “focus on the widest variety of ML use cases, many of which are small or medium-sized, which many companies face on a day to day basis”, Metaflow’s introductory documentation says.

Metaflow is not biased. It does not favor any one machine learning framework or data science library over another. The video-streaming giant deploys machine learning across all aspects of its business, from screenplay analysis, to optimizing production schedules and pricing. It is bent on using Python to the best limits the programming language can stretch. For the best Data Science Courses in Gurgaon or Python training institute in Delhi, you can check out the Dexlab Analytics courses online.

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Studies Show Indian Employers Prefer Experienced Workers Over Freshers

Posted on January 21, 2020May 23, 2020 by Dexlab

Studies Show Indian Employers Prefer Experienced Workers Over Freshers

Employability and the scramble for top Jobs in India

Looking to hire new talent or searching for a job? Well, some insights several studies and surveys provide about the job scenario in India might interest you.

The Millenial

Indian millennials, aged between 18 and 35 years, according to studies ( wheebox.com/assets/pdf/ISR_Report_2020.pdf ) makes nearly half the Indian workforce and looks likely to remain so for the next decade. This generation of workers are not only working hands but likely consumers as well, strong in their opinions, with access to the internet and social media across urban and rural areas. What they are most ardently looking for are jobs that respect their talent, pay them adequately and improve their employability in the market.

Employability in India

Employability has remained stagnant for several years now with around 46 per cent candidates job-ready. Of those employed, trends revealed

MBA’s in India are now projecting a rate of 54 per cent employability, acquiring the highest paying jobs
Employers prefer candidates with work experience, especially 1-5 years. Freshers are least preferred at 15 per cent.
The AI industry is showing promise wherein some reports pegged the number of job openings in AI and Machine learning sector at almost 1million in India last year.
Employability for pass-outs of B.Pharma, B.com, BA and Polytechnics showed an increase of around 15% since 2019.
Prospective workers from Maharashtra, Tamil Nadu and Uttar Pradesh were found to be most employable
While women are as employable as men, women’s participation in the workforce remains at a low 25 per cent vis a vis that of men.

What employers seek

Domain knowledge
Adaptability to the work environment
Learning ability and agility
Positive attitude

What employees seek

Majority of Students, around 88 per cent of those surveyed, sought internship opportunities though the supply did not meet demand in most cases
Maharashtra, Tamil Nadu and Andhra Pradesh were preferred and most sought after in terms of work opportunity
Over 55% students expect the annual salary to be above Rs. 2.6 lacs, a figure which has remained constant for the past few years

Ways to improve employability

Most students or potential candidates, surveys show, seek proper guidance and training and internship opportunities as varied as customer market analysis courses to customer marketing analysis training and courses teaching retail analytics using Python. While most universities lack the wherewithal to skill their outgoing students, students prefer to sign up for short courses online to equip themselves with the requisite knowledge specific to their industry. All this done with a view to increase their employability in a market deeply customer driven.

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