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DexLab Analytics Presents Mega Artificial Intelligence Course In Python: An Online Demo

Posted on August 17, 2020August 17, 2020 by Dexlab

Dexlab Analytics is undoubtedly a leading name in the field of the Big Data Analytics industry. The latest offering from this institute is a course that is remarkable in so many ways. The course is Mega Artificial Intelligence Course In Python, which aims to cover everything you ever need to learn regarding artificial intelligence. To help you get a better grasp of the course we have also prepared an online demo and the demo video is attached at the end of the blog do check that out to clear away any confusion you might have.

Before getting into the course details, there are certain features of the course that we think you should know about. To begin with, you do not need any special educational background, you can hail from any stream and can still pursue the course because here we will teach you from scratch. Just having some mathematical knowledge is fine. We have kept things flexible here, so you can repeat the course if and when necessary. The notes that you will be needing for the course including the code sheets, will be provided to you in the beginning so that you do not have to waste precious time in class taking notes.

However, the nature of the course will be online, because due to COVID 19 situation offline classes are temporarily not possible. You will be given all the classroom videos, furthermore, there will be guidelines regarding Kaggle.com where we will teach you how to participate in this pioneering data science website, how to compete over there and offer you tips to increase your ranking. All in all the course aims to transform you into a super data scientist.

You can find the detailed course information, the online demo and brochure in the PPT format at

The course will be divide into three sections starting with PYTHON PROGRAMMING for Data Science. Throughout the sessions, you will get familiar with the language, its libraries. You will be taught to use Plotly and handle projects before moving onto the second section which is AI( Artificial Intelligence) comprising three components namely Statistics, Machine Learning, and Deep Learning. Along with picking up the nuances, you would handle mega projects including one on self driving cars. Moving on to the next segment of Big Data get introduced to PySpark. Handling a growing amount of data could be tough, so, an introduction to Quantum Computing seems necessary before wrapping things up.

Do check out the course details in the video attached below that gives you a thorough tour of the entire course and also check out the course brochure. Our contact number is provided there along with our website address, feel free to contact us regarding any query.

A Quick Guide to Data Mining

Posted on July 23, 2020July 23, 2020 by Dexlab

Data mining refers to processing mountainous amount of data that pile up, to detect patterns and offer useful insight to businesses to strategize better. The data in question could be both structured and unstructured datasets containing valuable information and which if and when processed using the right technique could lead towards solutions.

Enrolling in a Data analyst training institute, can help the professionals involved in this field hone their skills. Now that we have learned what data mining is, let’s have a look at the data mining techniques employed for refining data.

Data cleaning

Since the data we are talking about is mostly unstructured data it could be erroneous, corrupt data. So, before the data processing can even begin it is essential to rectify or, eliminate such data from the data sets and thus preparing the ground for the next phases of operations. Data cleaning enhances data quality and ensures faster processing of data to generate insight. Data Science training is essential to be familiar with the process of data mining.

Classification analysis

Classification analysis is a complicated data mining technique which basically is about data segmentation. To be more precise it is decided which category an observation might belong to. While working with various data different attributes of the data are analyzed and the class or, segments they belong to are identified, then using algorithms further information is extracted.

Regression analysis

Regression analysis basically refers to the method of deciding the correlation between variables. Using this method how one variable influences the other could be decided. It basically allows the data analyst to decide which variable is of importance and which could be left out. Regression analysis basically helps to predict.

Anomaly detection

Anomaly detection is the technique that detects data points, observations in a dataset, that deviate from an expected or, normal pattern or behavior. This anomaly could point to some fault or, could lead towards the discovery of an exception that might offer new potential. In fields like health monitoring, or security this could be invaluable.

Clustering

This data mining technique is somewhat similar to classification analysis, but, different in the way that here data objects are grouped together in a cluster. Now objects belonging to one particular cluster will share some common thread while they would be completely different from objects in other clusters. In this technique visual presentation of data is important, for profiling customers this technique comes in handy.

Association

This data mining technique is employed to find some hidden relationhip patterns among variables, mostly dependent variables belonging to a dataset. The recurring relationships of variables are taken into account in this process. This comes in handy in predicting customer behavior, such as when they shop what items are they likely to purchase together could be predicted.

Tracking patterns

This technique is especially useful while sorting out data for the businesses. In this process while working with big datasets, certain trends or, patterns are recognized and these patterns are then monitored to draw a conclusion. This pattern tracking technique could also aid in identifying some sort of anomaly in the dataset that might otherwise go undetected.

Big data is accumulating every day and the more efficiently the datasets get processed and sorted, the better would be the chances of businesses and other sectors be accurate in predicting trends and be prepared for it. The field of data science is full of opportunities now, learning Data science using python training could help the younger generation make it big in this field.

Top Python Libraries to Know About in 2020

Posted on July 3, 2020July 3, 2020 by Dexlab

Python today is one of the most sought after programming languages in the world. As per Python’s Executive Summary, “Python is an interpreted, object-oriented, high-level programming language with dynamic semantics. Its high-level built in data structures, combined with dynamic typing and dynamic binding, make it very attractive for Rapid Application Development, as well as for use as a scripting or glue language to connect existing components together. Python’s simple, easy to learn syntax emphasizes readability and therefore reduces the cost of program maintenance.”

The most advantageous facet of Python is the richness of its library sources and the myriad uses and applications of these libraries in programming. This essay is dedicated to studying some of the best Python libraries available.

Tensor Flow

Tensor Flow is a highly popular open source library built by Google and Brain Team. It is used in almost all Google projects for machine learning. Tensor Flow

works as a computational library for writing fresh algorithms that require vast amounts of tensor operations.

Scikit-learn

Unarguably one of the most competent libraries for working with complex data, Scikit-learn is a python library associated with Numpy and SciPy. This library facilitates cross validation or the ability to use more than one metric.

Keras

Keras is one of the most revolutionary libraries in Python in that it makes it easy to express neural networks. Keras provides some of the most competent utilities for compiling models, processing datasets and more.

PyTorch

It is the largest machine learning library that permits developers to perform tensor computation, create dynamic graphs and calculate gradients automatically. Also, it offers a rich repository of APIs for solving applications related to neural networks.

Light GBM

Gradient Boosting is one of the best machine learning libraries that helps developers build new algorithms using elementary models like decision trees. This library is highly scalable and optimal for fast implementation of gradient boosting.

Eli5

This library helps overcome the problem of inaccuracy in machine learning model predictions. It is used in mathematical operations that consume less computational time and it is important when it comes to depending on other Python libraries.

SciPy

This library is built using Numpy and it is used in high-level computations in data science. It is used extensively for scientific and computations, solving differential equations, linear algebra and optimization algorithms.

Pandas

Python Data Analysis or Pandas is another highly popular library that is crucial to a data science life cycle in a data science project. Pandas provides super fast and flexible data structures such as data frame CDs that are specifically designed to work with structured data intuitively.

There are many more libraries like Theano and Librosa that are lesser known but very very important for machine learning, the most revolutionary scientific development of our century. To know more on the subject, do peruse the DexLab Analytics website today. DexLab Analytics is a premier Machine Learning institute in Gurgaon.

Why Learning Python is Important for Data Scientists Today

Posted on July 2, 2020July 2, 2020 by Dexlab

Data Science is the new rage and if you are looking to make a career, you might as well choose to become a data scientist. Data Scientists work with large sets of data to draw valuable insights that can be worked upon. Businesses rely on data scientists to sieve through tonnes of data and mine out crucial information that becomes the bedrock of business decisions in the future.

With the growth of AI, machine learning and predictive analytics, data science has come to be one of the favoured career choices in the world today. It is imperative for a data scientist to know one of more programming languages from any of those available – Java, R, Python, Scala or MATLAB.

However, Data Scientists prefer Python to other programming languages because of a number of reasons. Here we delve into some of them.

Popular

Python is one of the most popular programming languages used today. This dynamic language is easy to pick up and learn and is the best option for beginners. Secondly, it interfaces with complex high performance algorithms written in Fortran or C. It is also used for web development, data mining and scientific computing, among others.

Preferred for Data Science

Python solves most of the daily tasks a data scientist is expected to perform. “For data scientists who need to incorporate statistical code into production databases or integrate data with web-based applications, Python is often the ideal choice. It is also ideal for implementing algorithms, which is something that data scientists need to do often,” says a report.

Packages

Python has a number of very useful packages tailored for specific functions, including pandas, NumPy and SciPy. Data Scientists working on machine learning tasks find scikit-learn useful and Matplotlib is a perfect solution for graphical representation and data visualization in data science projects.

Easy to learn

It is easy to grasp and that is why not only beginners but busy professionals also choose to learn Python for their data science needs. Compared to R, this programming language shows a sharper learning curve for most people choosing to learn it.

Scalability

Unlike other programming languages, Python is highly scalable and perceptive to change. It is also faster than languages like MATLAB. It facilitates scale and gives data scientists multiple ways to approach a problem. This is one of the reasons why Youtube migrated to Python.

Libraries

Python offers access to a wide range of data science and data analysis libraries. These include pandas, NumPy, SciPy, StatsModels, and scikit-learn. And Python will keep building on these and adding to these. These libraries have made many hitherto unsolvable problems seem easy to crack for data scientists.

Python Community

Python has a very robust community and many data science professionals are willing to create new data science libraries for Python users. The Python community is tight-knit one and very active when it comes to finding a solution. Programmers can connect with community members over the Internet and Codementor or Stack Overflow.

So, that is why data scientists tend to opt for Python over other programming languages. This article was brought to you by DexLab Analytics. DexLab Analytics is premiere data science training institute in Gurgaon.

Machine Learning Algorithms – With Python (Part I)

Posted on June 17, 2020June 22, 2020 by Dexlab

Our industry experts introduce beginners to Machine Learning Algorithms with Python. In this blog, we will go through various Machine Learning Algorithms to understand the concepts better. This is the first part of a series.

Machine Learning, a subset of Artificial Intelligence, is a process of data analysis that automates analytical model building. It is a branch of artificial intelligence based on the idea that computing systems can learn from data, identify patterns in them and make intelligent decisions with minimal human intervention.

Parametric and Non-Parametric ML Algorithms

We first divide the mathematical methods for decision making in to sections – parametric and non-parametric algorithms. Parametric has a functional form while non-parametric has no functional form.

Functional form comprises a simple formula like 2+2=4 or Y=F(X). So if you input a value, you are to get a fixed output value. That means, if the data set is changed or being changed, there is not much variation in the results. But in non-parametric algorithms, a small change in data sets can result in a large change in the results.

But we do not desire this. We do not want this massive change in results in investments, for instance. We have various ways to solve this difficulty. For example, in statistics, you must have learnt the Central Limit Theorem – As the number of samples increase, the data will start following the normal distribution.

Here is an experiment on decision making with the help of non-parametric algorithm. We first take a random sample, and we apply an algorithm to it to get a result. We repeat this process several times and get an average of the results. In this way, the variation in our results goes down considerably. We will get a central tendency.

Take for example stock market data where prices are totally random. There is no fixed pattern to it. It is a manmade phenomenon. In the same way, we can make predictions in data sets only when there is a particular pattern. It becomes that much more difficult to make predictions in the absence of a clear pattern. In such a case, we take thousands of samples and work them to get a result before investing. We can use a Decision Tree like Random Forest for this.

Supervised and Unsupervised Algorithms

Now, secondly, we can term ML algorithms as supervised or unsupervised algorithms. Suppose we have data under sub-heads – Name, Age, Gender and Salary and Period of Service. Now, consider the model wherein we are asked to predict the period of service of an employee based on data provided under the rest of the sub-heads based on existing employee data.

Now, in this example, the period of service is the Target. The data sets on the basis of which the prediction will be made – Name, Age, Gender, Salary – is the Input. In such a model, where the target variable is specified, we term it as supervised machine learning algorithm. We do this according to a formula – Y=B0 + B1X1.

In unsupervised learning, the target variable is not provided and all we can do is divide the historical data in clusters. For example, Google Translate runs on a supervised model as do chatbots. Data is not only the new oil, it is everything. And there will come a time of data colonisation whereby the organisation with the best data will rule. The better the date, the better our ML models. Who has the best data sets in the world? Google and Amazon, among others, do.

So this is it, about supervised and unsupervised machine learning. For more on this, do watch our intensive video tutorial on ML algorithms.

(Translated till first 28:00 minutes)

This is the first blog of the series, stay tuned with Dexlab Analytics to read through the whole video we’ll covering in our upcoming blogs!

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.

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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