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NLP and Deep Learning for Data Scientists

Deep learning and natural language processing (NLP and Deep Learning)are as busy as they’ve always been. The most in-demand technologies are deep learning and natural language processing (NLP). Advances in natural language processing and deep learning (NLP and Deep Learning) are produced nearly every day. Despite the fact that quarantine regulations in many nations have hampered numerous businesses, the machine learning industry continues to advance.

Aside from the fact that the Covid-19 has caused problems for a number of organizations, new-age tech skills such as Machine Learning (ML), Artificial Intelligence (AI), and Natural Language Processing (NLP and Deep Learning) is in high demand. For budding Data Scientists, here are some must-read publications. In this article, we at Learnbay try to go over some of the most crucial and current breakthroughs.

How Deep Learning can keep you safe

Nukanj Aggarwal, ML Lead at Citizen, compiled a list of instances of how deep learning is being utilized to produce life-changing technology in his article that written. This article, ideally written by Citizen’s Machine Learning Lead, that shows how deep learning is being utilized to produce life-changing (or life-saving) technology.

  • Citizen is nothing but a company that analyses first and foremost responder radio frequencies that using speech-to-text engines as well as convolutional neural networks.
  • Citizen is a real-time emergency as well as safety alert app that quite notifies users of occurrences and crimes that have specially occurred in their neighborhood.
  • The company has been able to expand its apps to a number of US cities.
  • In the coming years, this technology could signify a significant shift in the police and first responder infrastructure.
  • The NLP-driven has the ability to transform the police and response infrastructure dramatically.

The Publication of the Open AI API

The publication of GPT-3 by Open AI was arguably the most significant development in the field of natural language processing this year. The API allows businesses and individuals to integrate OpenAI’s new AI technologies into their products and services. The publication of Open AI’s API, on the other hand, may have gone unnoticed by many.

  • The API’s goal is technically keeps their focus on to provide users with access to future models built by the corporation, such as GPT-3.
  • The API is general-purpose and can be used on nearly any natural language work; its success is inversely proportional to the task’s complexity.
  • This is significant since it represents a departure from the company’s usual practice of open-sourcing its models (as they did with GPT-2)
  • The company discusses why they opted to produce a commercial product this time, why they avoided open-source this time, and how they will manage any API misuse in the post.
  • This official blog discusses how the corporation moved away from open source in order to prevent API exploitation

 

IBM will no longer offer, develop, or research facial recognition technology

This official blog discusses how the corporation moved away from open source in order to prevent API exploitation. The CEO of IBM publicly indicated in a letter to Congress that the business would certainly be ceasing development as well as service offers of general-purpose facial recognition technologies and methodologies.

  • Artificial intelligence advancements have substantially enhanced facial recognition software during the last decade.
  • This was a significant step forward for the organisation, as well as a strong message to the data science community at large.
  • Face recognition technology will no longer be developed or researched by IBM, according to the company.
  • IBM’s decision to prioritise ethics and safety may have influenced other large IT firms (including Microsoft) to follow suit.
  • They feel that now is the right time to start a national conversation about whether and how domestic law enforcement organisations should use facial recognition methodologies.

Conversational AI: Neural Approaches

It examines neural approaches to conversational AI that have been developed in recent times as well. Audiences are interested in Natural Language Processing and Information Retrieval.

  • The researchers divided into three categories: question answering agents, task-oriented dialogue agents, and chatbots in this paper.
  • It offers a complete overview of the various approaches to conversational AI that have been developed in recent years, including quality assurance, task-oriented, and social bots, as well as a unified view of optimum decision-making.
  • An overview of state-of-the-art neural techniques is offered for each category, along with a comparison of them to traditional approaches, as well
  • Its a discussion of progress made and obstacles still faced, using specific systems and models as case studies and sets.

It offers a coherent perspective as well as a full presentation of the key concepts and insights required to comprehend and develop modern dialogue agents that will be critical in making world knowledge and services accessible to millions of people in natural and intuitive ways.

Language Models Are Unsupervised Multitask Learners

Question answering, machine translation, reading comprehension, and summarization are all examples of natural language processing (NLP) problems that are often ideally tackled using supervised learning on task-specific data models as well.

  • When trained on a new dataset of millions of online pages called WebText, the authors proved that language models began to learn these tasks without any explicit administration as well.
  • The language model’s capacity is nothing but critical to zero-shot task transfer’s effectiveness just because of the increase whilst it certainly enhances performance in a log-linear pattern-wise across tasks.
  • These findings point to a possible avenue for developing language processing algorithms that learn to fulfill tasks based on natural demonstrations.

 

Generative Pre-Training Improves Language Understanding

The researchers discussed natural language processing and how discriminatively trained models can struggle to perform effectively in this paper published by OpenAI.

  • Most deep learning approaches necessitate a large amount of manually labelled data, which limits their usefulness in many sectors where annotated resources are few.
  • The approach’s effectiveness was technically proved on a numeric of natural language processing criteria, as according to the specific researchers.
  • These target tasks do not have to be in the same domain as the unlabeled corpus in our configuration.

They suggested a broad task-agnostic model that beat discriminatively trained models that use architectures specifically generated for each specific task in around 9 of the 12 tasks that studied, greatly outperforming the state-of-the-art. Their goal is to learn a universal representation that can be used for a variety of tasks with minimum change.

Deep Learning Generalization

Many difficult research areas, like image recognition and natural language processing, have seen considerable success using deep learning.

  • Deep learning has had a substantial impact on the conceptual foundations of machine learning and artificial intelligence and has achieved significant practical success.
  • They would demonstrate in this certain Deep Learning Generalization article that deep learning technology nowadays is a strong contender for increasing sensing abilities.

The Model Card Toolkit for Easier Model Transparency Reporting

Transparency in machine learning (ML) models is crucial in a range of sectors that affect people’s lives, including healthcare, personal finance, and implementation as well. It gets more difficult to convey the intended use cases and other information to consumers downstream whenever larger and also possibly more and more intricate deep learning models are developed.

  • The details that developers need to assess whether or not a model is appropriate for their use case may vary, as will the information required by downstream users.
  • To help and assess that how to tackle this particular difficulty, as Google researchers ideally developed the “Model Card Toolkit,” which particularly simplifies the creation of model transparency reports.

The Complete Guide to Deep Learning Algorithms

This article, written by Sergios Karagiannakos, the founder of AI Summer, provides a comprehensive guide to deep learning.

  • Deep Learning is getting a lot of traction in both the scientific and corporate worlds.
  • Sergios Karagiannakos, certainly the founder of AI Summer, who has written a comprehensive handbook.
  • More and more businesses are incorporating them into their regular operations. It covers far too many topics, ranging from various types of neural networks to deep learning baselines.

Deepfake Detection Tools and AI-Generated Text

With the widespread dissemination of misinformation on social media, I was alarmed when I noticed it had reached my own inner surrounding. The consequences of such deepfakes have been disastrous, with hacked videos of public personalities circulating, putting their reputations at risk. I wanted to help counteract the nefarious use of these technologies as it has become easier to make deepfakes and manufacture fake articles using AI.

  • Given the catastrophic consequences of deepfakes, many attempts to develop relevant tools to detect them have been attempted, with variable degrees of success.
  • Furthermore, the digital behemoth unveiled a new tool that can detect doctored information and ensure readers of its veracity.
  • This article explains a few easy strategies and browser plugins for detecting deepfakes and AI-generated text.
  • Binghamton University and Intel researchers developed a method that goes beyond deepfake identification to identify the deepfake model behind the hacked video.

GPT-3 Philosophers (updated with replies by GPT-3)

This is a fascinating thinking piece in which nine philosophers go into Open AI’s GPT-3. It’s not only a matter of correcting the linguistic biases that have arisen (or used in training.) This is an intriguing thinking article from Daily Nous, in which nine philosophers delve into Open AI’s GPT-3.

  • It isn’t a case of discovering a technological panacea to eliminate bias.
  • The thought leaders ponder the ethical and moral challenges that technology may raise, as well as the remaining questions that it may raise.

Bridging The Gap Between Training & Inference For Neural Machine Translation

This paper is one of the top NLP papers that published from the premier conference, Association for Computational Linguistics (ACL). Neural Machine Translation (NMT) generates target words sequentially in the way of predicting the next word conditioned on the context words.

  • This paper bridging The Gap Between Training & Inference For Neural Machine Translation talks about the error accumulation.
  • The researchers certainly addressed such specific problems by sampling context words, not only from the ground truth sequence. But also from the predicted sequence particularly by the model during training, whereas the predicted sequence is technically selected with a sentence-level optimum.
  • In this paper, they address these issues by sampling context words not only from the ground truth sequence but also from the predicted sequence.
  • According to the specific researchers, this approach can technically achieve significant improvements in multiple datasets.

The Matrix Calculus You Need For Deep Learning

This document attempts to teach all of the matrix mathematics required to comprehend deep neural network training. Using the automatic differentiation built into modern deep learning libraries. This certainly explains how to become a world-class and relevant deep learning practitioner with only a basic understanding of scalar calculus.

  • They presume you know nothing about arithmetic beyond what you studied in calculus 1 and provide resources to assist you refresh your math skills if necessary.
  • This material is for those who are already familiar with the basics of neural networks and want to deepen their understanding of the underlying math.

You do not need to understand this material before learning to train and use deep learning in practise; rather, this material is for those who are already familiar with the basics of neural networks and want to deepen their understanding of the underlying math.

Final lines

We hope that these articles and instructions on natural language processing and NLP and Deep Learning helped you keep up with some of the major developments in machine learning this year. Increased focus with NLP and Deep Learning means more internet materials are available. But a good article is sometimes required to gain a solid understanding of such a complicated and multi-faceted subject. Articles can help you improve your overall data literacy by providing basic background information, such as an introduction to deep learning and natural language processing (NLP) or clarification on significant ideas and real-world illustrations very well. Keep growing, my fellow members of the A.I. community.

Different Job Roles After A Data Science Course

Data Science Is Not The Future; It Is The Present!

Data science has existed since the 1990s. However, its significance was only realised when firms were unable to make decisions based on massive amounts of data. Most firms out there collect and analyze a large amount of particular data in their everyday operations in this age of technology and today we will discuss different job roles after the data science course.

Data science has aided firms in expanding beyond the traditional data aggregation rules. Data is exchanged in practically every encounter with technology. It quite enables organizations to have access to more and more specific information and so also allows seeing new things in a finest and better way, from a different perspective. The role of a data scientist is to evaluate this data and interpret the conclusions in order to put them into practice for organisational advantage. Apart from data scientists, there are many other different job roles that you can get after completing a data science course

Data Scientists not only play a vital key role in business analysis, but they are also responsible for building relevant data products as well as software platforms. Data Science encloses many breakthrough technologies like Artificial Intelligence (AI), the Internet of Things (IoT), and Deep Learning to name a few. Data science is, in fact, a mix of computer science, statistics, and mathematics. Data science’s advances and technological advancements have increased its impact across all industries. With advanced technologies, different job roles have been generated which you can check further related to the data science courses.

Considering all this, it is a good idea to think of a career in this dynamically expanding industry. The article below simply discusses the scope and job opportunities out there in the field of Data Science.

Why choose Data Science?

Every day, around 3.6 quintillion bytes of data are processed and generated in the modern world. The volume of data has increased as contemporary technology has facilitated the creation and storage of ever-increasing amounts of data. A data scientist can gather and analyze this massive amount of relevant data in such a way that it can be used to run a lucrative business. The tremendous amount of data collected and saved by modern technologies has the potential to revolutionise businesses and communities all around the world, but only if we can comprehend it. That’s where data science and its world enters into the picture.

Do you know why data science is in high demand: Different Job Roles?

This is a simple question with a simple response. Experts in data science are quite required in practically every industry out there, from government security to dating apps nowadays. Millions of businesses and government agencies rely on big data to flourish and better serve their customers. When evaluating whether or not a job in data science is right for you, it’s more than just a question of whether or not you enjoy dealing with numbers.

  • Data science jobs are in very high demand nowadays, and this trend is unlikely to change in the near future, if at all.
  • Businesses and industries are now embracing the potential of the particular data rather than relying on age-old data calculating approaches as well.
  • It’s all about generating and determining whether you enjoy working on complex, those confusing situations and whether you have the particular and needy talent and perseverance to expand your skillset.

Pursuing an advanced degree programme in your field of interest is one method to gain such abilities and expertise. Regardless of the vertical, the massive digitization of promotion platforms is increasingly based on data insights. With zillions of bytes of data generated every day, the role of data scientists is so vital and critical, as they are certainly responsible for providing intelligent and specific solutions to help their businesses make better decisions and grow as well.

Data Analyst

Data analysts are responsible for a wide range of duties, including data visualisation, munging, and processing. Although not all data analysts are junior, and compensation can vary greatly, this is often regarded as an “entry-level” role in the data science industry.

  • They must also run different queries against particular databases from time to time. Optimization is one of a data analyst’s most significant talents.
  • The primary responsibility of a data analyst is to examine corporate or industry data and use it to answer business issues, then convey those answers to other departments within the organisation for action.
  • This is due to the fact that they must develop and modify algorithms that can be utilised to extract data from some of the world’s largest databases without causing data corruption.
  • Data analysts frequently collaborate with a range of teams inside a firm over time; for example, you might focus on marketing analytics for one month and then help the CEO utilise data to uncover reasons for the company’s growth.

Infographics explaining about job roles after a data science course

Data Scientist

Data scientists must technically comprehend business difficulties and doubts as well as provide the finest and better solutions through data analysis and solve it.

  • Many of the same tasks are performed by data analysts, but data scientists additionally use machine learning models to generate and analyze accurate predictions about the future based on historical data.
  • A data scientist has more leeway to experiment and explore their own ideas in order to uncover fascinating patterns and trends in the data that management may not have considered.
  • They can also do so by spotting different trends and specific patterns that might aid businesses in making better and finest judgments.

Lets us check other different job roles in which you can upgrade your career after data science course.

Data Engineers

The data infrastructure of a specific corporation is certainly managed by a data engineer. Data engineers create and test scalable Big Data ecosystems for businesses so that data scientists may run their algorithms on robust, well-optimized data platforms.

  • Their job necessitates a lot more software development and programming competence than statistical analysis.
  • To boost database performance, data engineers also update existing systems with newer or improved versions of current technologies.
  • A data engineer may be in charge of designing data pipelines that transmit the most up-to-date sales, marketing, and revenue data to data analysts and scientists quickly and in a usable format in a corporation with a data team.

Machine Learning Engineer

Engineers who specialise in machine learning are in high demand right now. Between a machine learning engineer and a data scientist, there is a lot of overlap. However, the work profile has its own set of difficulties.

  • Aside from having extensive knowledge of some of the most powerful technologies, the different relevant term simply refers to a data scientist with machine learning outcomes.
  • Regardless of the specifics, almost all machine learning engineer positions will necessitate at least data science programming skills and a somewhat deep understanding of machine learning algorithms.

Data Architect

Basically, this is a sub-field of data engineering for people who want to be in control of a company’s data storage systems. A data architect builds data management plans so that databases may be readily connected, consolidated, and safeguarded with the greatest security methods possible.

  • SQL abilities are a must for this job, but you’ll also need a strong command of a variety of other tech skills, which will vary depending on the employer’s tech stack.
  • They have nothing but the most up-to-date and new modernized tools and with those systems with which to operate.
  • Although you won’t get hired exclusively on the basis of your data science talents, the SQL skills and data management knowledge you’ll gain through mastering data science make it a position worth considering if you’re interested in the data engineering side of the organization.

Statistician

A statistician, unlike a data scientist, will not be expected to know how to develop and train machine learning models. As the name implies here, a statistician is finite well-versed in statistical theory as well as in data organisation. Before the keyword data scientist was invented in this era, then it was 1st referred to as “statisticians.”

  • They not only extract and give valuable and particular insights from data clusters, but also they help the different department developers design new techniques.
  • The skills necessary vary greatly depending on the job, but they always require a solid grasp of probability and statistics.

Business Analyst

Business analysts have a slightly distinct role from other data scientists. The word “business analyst” refers to a wide range of positions, but in the broadest sense, a business analyst assists firms in answering questions and solving problems.

  • They understand how data-oriented technologies function and how to handle massive volumes of data, but they also technically know how to distinguish and analyze high-value data from low-value data.
  • However, many business analyst roles certainly involve the analyst collecting and making suggestions based on a company’s data, and having data skills will almost certainly make you a more appealing candidate for nearly any business analyst position.
  • To put it another way, they figure out how Big Data can be linked to valuable business insights to help companies grow.

Market Research Analyst

Promote research experts to analyse customer behaviour to assist firms in determining how to design, market, and commercialise their services. To review and improve the efficacy of marketing campaigns, marketing analysts examine sales and marketing data.

  • Several market research analysts work for consulting businesses that are employed on a contract basis.
  • Market research experts gather and analyse data about customers and competitors.
  • Analysts of market research technically do examine different market dynamics to forecast future product or service sales as well.

In addition, a marketing analyst whose research has a big influence can aim for a Chief Marketing Officer post, which earns an average of $157,960 per year. They assist businesses with identifying and producing things that people desire.

Database Administrator

Working for financial and medical institutions, social media firms, research institutes, legal firms, and other organisations.

  • A database administrator’s job description is fairly self-explanatory: they are responsible for the proper operation of all of an enterprise’s databases.
  • They also do work like backup and restore.

Final Words

In an unpredictable world, data is more vital than ever. Data science has been applied in practically every area in recent years, resulting in a strong 45 per cent increase in total data science-related employment or different job roles related to data science. Businesses will be searching for personnel with data science and analytical abilities to assist them to maximise resources and making data-driven choices as they continue to evolve. The growing prominence of data scientists in the data analyst career path will indicate data science’s future potential and will generate different job roles.

Learnbay has a path for you whether you want to learn about data science for the first time, obtain valuable analytics skills that can be used in a variety of sectors or earn a degree. It’s no wonder that Data Science professions are becoming increasingly popular, thanks to high compensation and intriguing work. Our programmes ensure that you obtain the needy skills to develop a rewarding career. You can choose different job roles related to data science after studying from Learnbay which is considered as best institute of data science.

Clustering & Types Of Clustering

Clustering & Types Of Clustering is the process of finding similar groups in data, called a cluster. It groups data instances that are similar to each other in one cluster and data instances that are very different(far away) from each other into different clusters. A cluster is, therefore, a collection of objects which are “similar” between them and are “dissimilar” to the objects belonging to other clusters.

Cluster graph

The method of identifying similar groups of data in a dataset is called clustering. It is one of the most popular techniques in data science. Entities in each group and is comparatively more similar to entities of that group than those of the other groups. In this article, I will be taking you through the types of clustering, different clustering algorithms and a comparison between two of the most commonly used clustering methods.

Steps involved in Clustering analysis:

1. Formulate the problem – select variables to be used for clustering.

2. Decide the clustering procedure whether it will be Hierarchical or Non-Hierarchical.

3. Select the measure of similarity or dissimilarity.

4. Choose clustering algorithms.

5. Decide the number of clusters.

6. Interpret the cluster output(profile the clusters).

7. Validate the clusters.

Types of clustering technique:

Broadly speaking, clustering can be divided into two subgroups :

  • Hard Clustering: In hard clustering, each data point either belongs to a cluster completely or not. For example, in the above example, each customer is put into one group out of the 10 groups.
  • Soft Clustering: In soft clustering, instead of putting each data point into a separate cluster, a probability or likelihood of that data point to be in those clusters is assigned. For example, from the above scenario, each customer is assigned a probability to be in either of 10 clusters of the retail store.

Types of clustering are:

k-means clustering:

k-means clustering is a method of vector quantization, originally from signal processing, that is popular for cluster analysis in data mining. k-means clustering aims to partition n observations into k clusters in which each observation belongs to the cluster with the nearest mean, serving as a prototype of the cluster. This results in a partitioning of the data space into Voronoi cells. k-Means minimizes within-cluster variances (squared Euclidean distances), but not regular Euclidean distances, which would be the more difficult Weber problem: the mean optimizes squared errors, whereas only the geometric median minimizes Euclidean distances. Better Euclidean solutions can, for example, be found using k-medians and k-medoids.

K-Means Clustering example

K means is an iterative clustering algorithm that aims to find local maxima in each iteration. This algorithm works in these 5 steps :

  1. Specify the desired number of clusters K : Let us choose k=2 for these 5 data points in 2-D space.
  2. Randomly assign each data point to a cluster: Let’s assign three points in cluster 1 shown using red color and two points in cluster 2 shown using grey color.
  3. Compute cluster centroids: The centroid of data points in the red cluster is shown using a red cross and those in a grey cluster using the grey cross.
  4. Re-assign each point to the closest cluster centroid: Note that only the data point at the bottom is assigned to the red cluster even though its closer to the centroid of the grey cluster. Thus, we assign that data point into a grey cluster
  5. Re-compute cluster centroids: Now, re-computing the centroids for both the clusters.
  6. Repeat steps 4 and 5 until no improvements are possible: Similarly, we’ll repeat the 4th and 5th steps until we’ll reach global optima. When there will be no further switching of data points between two clusters for two successive repeats. It will mark the termination of the algorithm if not explicitly mentioned.


from pandas import DataFrame
Data = {'x': [25,34,22,27,33,33,31,22,35,34,67,54,57,43,50,57,59,52,65,47,49,48,35,33,44,45,38,43,51,46],
'y': [79,51,53,78,59,74,73,57,69,75,51,32,40,47,53,36,35,58,59,50,25,20,14,12,20,5,29,27,8,7] }
df = DataFrame(Data,columns=['x','y'])
print (df) 

k-means for cluster=3


from pandas import DataFrame
import matplotlib.pyplot as plt
from sklearn.cluster import KMeans
Data = {'x': [25,34,22,27,33,33,31,22,35,34,67,54,57,43,50,57,59,52,65,47,49,48,35,33,44,45,38,43,51,46],
'y': [79,51,53,78,59,74,73,57,69,75,51,32,40,47,53,36,35,58,59,50,25,20,14,12,20,5,29,27,8,7] }
df = DataFrame(Data,columns=['x','y'])
kmeans = KMeans(n_clusters=3).fit(df)
centroids = kmeans.cluster_centers_
print(centroids)
plt.scatter(df['x'], df['y'], c= kmeans.labels_.astype(float), s=50, alpha=0.5)
plt.scatter(centroids[:, 0], centroids[:, 1], c='red', s=50) 
Hierarchical Clustering: 

Hierarchical clustering, as the name suggests is an algorithm that builds the hierarchy of clusters. This algorithm starts with all the data points assigned to a cluster of their own. Then two nearest clusters are merged into the same cluster. In the end, this algorithm terminates when there is only a single cluster left.

The results of hierarchical clustering can be shown using the dendrogram. The dendrogram can be interpreted as:

The results of hierarchical clustering the dendrogram.

Two important things that you should know about hierarchical clustering are:

  • This algorithm has been implemented above using a bottom-up approach. It is also possible to follow the top-down approach starting with all data points assigned in the same cluster and recursively performing splits till each data point is assigned a separate cluster.
  • The decision of merging two clusters is taken on the basis of closeness of these clusters. There are multiple metrics for deciding the closeness of two clusters :
    • Euclidean distance: ||a-b||2 = √(Σ(ai-bi))
    • Squared Euclidean distance: ||a-b||22 = Σ((ai-bi)2)
    • Manhattan distance: ||a-b||1 = Σ|ai-bi|
    • Maximum distance:||a-b||INFINITY = maxi|ai-bi|
    • Mahalanobis distance: √((a-b)T S-1 (-b))   {where, s : covariance matrix}

import numpy as np
X = np.array([[5,3],
[10,15],
[15,12],
[24,10],
[30,30],
[85,70],
[71,80],
[60,78],
[70,55],
[80,91],])
import matplotlib.pyplot as plt
labels = range(1, 11)
plt.figure(figsize=(10, 7))
plt.subplots_adjust(bottom=0.1)
plt.scatter(X[:,0],X[:,1], label='True Position')
for label, x, y in zip(labels, X[:, 0], X[:, 1]):
plt.annotate(
label,
xy=(x, y), xytext=(-3, 3),
textcoords='offset points', ha='right', va='bottom')
plt.show()

 

Data point plot

from scipy.cluster.hierarchy import dendrogram, linkage
from matplotlib import pyplot as plt

linked = linkage(X, 'single')
labelList = range(1, 11)
plt.figure(figsize=(10, 7))
dendrogram(linked,
orientation='top',
labels=labelList,
distance_sort='descending',
show_leaf_counts=True)
plt.show()

Dendrogram plot

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Data Science for working professionals

To secure a job in any domain one has to give it a lot of preparation, should be trained for the role and should have absolute knowledge about the field, usually people will dedicate years in preparing for their desired roles. Shifting from a prepared role of domain to a different domain will not usually be easy, strong gust of skepticism would surely haunt. The process of shifting from one domain to another is hard, it gets harder to learn data science for working professionals because they will have to prepare for the new job role while maintaining their current one.

If and only if you plan the whole process of domain shifting in an organised and rational way, you can have a win-win situation.

Have a vision and plan your strategy

You must win in both the games of learning and working, for that you will have to strategize in such a way that your time in learning data science should not in any way collide with your work life and vice-versa. Because both of the activities are equally important as they require immense attention and individual preference.

let us start from the scratch, here are some possible concerns of a working professional:

  1. Time management
  2. Balancing the energy between two activities
  3. Scheduling
  4. Risk of affording a wrong move
  5. Risk of inefficient or improper execution

As a working professional you will have to manage your responsibilities in a way that you will have control over every single thing that happens to exist. With proper planning and the right way of approach, the above mentioned concerns could be easily tamed.

Firmly state your purpose of learning data science
Why do you want to change your domain into Data Science while you already have a job? firmly define the purpose. You should know that by shifting to data science everything will change, you will have to develop new skill sets for the role that you are targeting, processing of workflow will be different, your future job role will have different goals, purpose and aim. Act consciously when you are risking to give up on the comfort and expertise you have in your current job, be very sure about the purpose of doing so. Doing this will eliminate the skepticism about the risk of getting out of your comfort zone. The efforts that you put over learning Data Science will never go in vain because you will learn about the currently trending technologies and tools, that will help you survive not only in data science but anywhere in the IT firm.

Have a soft target
People think only the role of ‘data scientist’ matters the most but the fact is that there are several other roles in data science which significantly matter in the field, choose one role that which you want to become and start preparing for it. Doing this should be good for the starters, because you do not have to be a scholar in every tool that has ever been used in the field, smartly target those topics that are the essentials in Data Science. When you specifically work on a targeted role you will have the chance to completely know about it and its importance in the field. This way of approach will be a very smart move because you will not be confused regarding what exactly to study in the vast field of data science and the field generally prioritizes those who holds master expertise in specified field. So be very sure about the role you want to serve in, in data science.

Plan the execution
To perfectly plan the execution part you will first have to design the implementation part, do it wise and rationally. Revise your daily-life activities, reschedule it for the sake of balancing between learning and working.

Exercise on the way you spend time on everyday things, revise it according to your daily schedules. Practice to make a note of your tasks everyday, according to that plan on how much time you would invest on the things and try your best to act as decided. In other words, this way of dealing with the things is called as discipline, to have a structured day you will have to practice discipline in all possible ways. Revise your activities from sleeping habits to break sessions, reschedule them in such a way that the things will itself fall in the right place. Set targets, set your own deadlines and design the way that you want things to work in.

Networking and understanding the field
Involve with the people that come from the field of Data Science, know about the insider story of the field and about how it works. Having field knowledge is very much necessary, remember that when you get into data science you will have to work in teams, so practice skills in communication and confidence. Get interactive with the people by asking them about the ways to reach to the field, this way you will build good connections and will get great suggestions as well. Start associating yourself with the people who belongs to Data science, you will need to get used to that.

A good course
Everything that you do and every effort that you put is only to learn Data Science, but if you make the mistake of choosing a wrong course every effort of yours will go in vain. Your purpose of learning Data Science is to shift your domain into that of Data science, you cannot do this without the help of a good course. The course that you choose should not only help you to have fine knowledge in data science but also should help you to manage your planned schedules. There are many data science courses that are specially built for working professionals, it will greatly help if you choose the right one among them.

Conclusion
With the right approach and proper planning you can triumph in learning Data Science while maintaining a full time job. Stick to your plans and preparations, seek help from a good course, practice as much as you could and start involving yourself with the field. If you manage to everyday execute the plans you will surely reach your destination in ease.

Learnbay could help you
The data science course of Learnbay is specially designed for working professionals, the benefits provided in the course will help you balance your scheduling. Learnbay powered by IBM will help you throughout the journey of learning and experiencing data science.

Introduction to Python Programming

What exactly is Python?

Python is a general-purpose, interpreted, and dynamic programming language that belongs to high-level programming language divisions. Python is commonly used for application development because it supports object-oriented programming approach.

Why is Python so well-likely?

The one-line response is ‘widely accessible’ and has a ‘simple syntax’. Yes, not a single programming language has the same level of accessibility and ease of use as Python. Python’s syntaxes give natural language a lot of weight, making it easier to understand and work with, and making it sound more like human communication language. As a result, it has stayed in the top five programming languages preferred by software engineers, application developers, and other techies for the past few decades.
But, since the last few years, crossing the circumference of popularity, Python has become a global craze irrespective of demographic, professional, and generation-related limitations.

Why should you learn Python in 2021?

Python programming is now the fifth fundamental need to live in this real world, after food, water, air, and shelter.
It may sound diplomatic or even crazy, but it’s the fact. Let’s have an inside look at why it’s so important to learn Python?

    • Use of Python is industry-independent:-Every industry needs Python. Most industries like BFSI, healthcare, sales and marketing, even education and research industries are becoming highly dependent on python programming.The example I will give here will certainly make you realize how powerful Python programming industrial reaches.
      Skilled growth marketers, who once relied solely on simple analytical skills and advanced Excel expertise, now have little value in the marketing industry unless they know Python.
      Yes, nowadays, python-driven researches are used to make the majority of growth marketing decisions. As a result, Python is clearly embedded in every area of the industrial sector, regardless of domain.
      You can visit python.org’s Success story page to the ultimate power of Python across multiple industries.
      But why the sudden popularity?
    • Python is a foundational language of data science:-Data science- the major cause of Python’s cross-industrial demand surge.
      Initially, R used to be the backbone of data science. Still, with the advancement of data science and its applicability, the complexity of R became a sound barrier for the data science field. Simplicity and a wider range of accessibilities promoted Python as the successful replacement of R in the world of data science.
      With the advancement of AI and Machine Learning, Python achieved more credibility within the last few years. The most widely used software and application packages like ‘pandas’, ‘NumPy’, ‘Matplotlib’, ‘pyspark’, ‘Keras’, ‘Scikit-learn’, ‘PyTorch’, etc., all are developed using python programming.
      Such applications and libraries are very efficient for handling larger amounts of data, even by non programmers. As these are inbuilt libraries within Python, other than the application usage knowledge, no core programming or coding proficiency is needed. These applications have strong algorithmic abilities, making a person with only basic knowledge of statistics and complex mathematics impressively eligible to do a data analytics job. This is why Python is called ‘A programming language built for everyone’ and is making the future of data science.
  • It’s going to be most demanding skills in the future job market:-While data science has become the hottest topic in the global job market, the demand for python programmers are increasing silently.
    Why so?
    Well, everybody is now focusing on the data science career switch. Professionals without technical backgrounds target the basics of python programming and jump into the data science tools and technologies. But from where are these tools technologies coming?
    Yes, these are the output of so many python programmer’s hardships. Hence, while the key focus is now on data scientist and data scientist courses, the hidden job opportunities are very high for python programmers. On LinkedIn.com, at present, almost 528,242 python jobs are available worldwide. The number is increasing by at least 10% every day.So, suppose you hold a technical background or a student. In that case, it’s best for you that you focus on earning experience (for working professionals) or degrees in core python programming (for students).
  • Python programmers are well compensated.
    Python developers earn around 7 to 18 lakhs/year (6 t0 ten years of experience) as base salary with additional compensation of almost the same amounts in India. This is quite high in comparison to other programming developers like PHP, Java, C++, etc. Even SMEs are offering an average of 2 to 3 lakhs salary package to freshers.

What are the uses of python Software?

Python software can be used in a variety of ways, as previously mentioned.The fields and tasks for which python software is widely used are listed below.

  • Web development
  • 3D CAD application
  • Machine learning
  • Scripting
  • Analysis data
  • Processing of image
  • Artificial Intelligence
  • Speech Recognition
  • Development of Software
  • Data mining
  • Creation of Desktop and mobile application
  • Development of Games

Features of Python

  • It’s simple to understand, read, and write.
    Syntaxes used in python programming are more like natural language/ human language (like English). For example, to print ‘Learnbay- The Data Science & AI Institute, ‘we have to just type print(“Learnbay– The Data Science & AI Institute”) in python editor or any IDLE.
  • Smart in memory management
    Python offers you a stress-free programming experience concerning memory. This programming software comes with efficient auto-memory management features that periodically clean the memory by itself.
  • Free to use and open source
    No purchasing cost or subscription cost is required for downloading and using Python.you can use Python at zero cost for a lifetime. And due to its open-source licensing features, you can share this software, among others. Even, can modify the source code as per your project requirement.
  • Cross-platform performance
    Python has compatibility with most operating systems like Windows, Linux, Mac, Unix, etc.
    Until now, you have learned plenty of basic information about python programming. So, you might have a question in mind when you are aware of the ease of use.

Can I learn Python on my own?

Yes, of course. Plenty of python learning videos are now available over the internet. A few of the reliable options are Codecademy, Learnpython.org, LinkedIn python learning courses, etc. you can download Python for free here .
These are only for gathering a basic stage of knowledge. If you have data science career transition planning, you must choose a creditable course featured with real-time industrial projects.

You can check our sample python programming class for AI video.

Win the COVID-19

If you slightly change your perspective towards the lock-down situation you can find hope of this pandemic to end and can hope of a brighter than ever future. Go for Data Science, it will be worth it.

Data Science at Intern Level

As an intern your focus must be on following the patterns of how the activity works, analyse which language will be appropriate to learn because ones journey in Data Science will not end until they get a job, but it will start from there.

Exploratory Data Analysis on Iris dataset

What is EDA?

Exploratory Data Analysis refers to the critical process of performing initial investigations on data so as to discover patterns, spot anomalies, to test hypotheses and to check assumptions with the help of summary statistics and graphical representations.

It is always good to explore and compare a data set with multiple exploratory techniques. After the exploratory data analysis, you will get confidence in your data to point where you’re ready to engage a machine learning algorithm and another benefit of EDA is to the selection of feature variables that will be used later for Machine Learning.
In this post, we take Iris Dataset to get the process of EDA.

Importing libraries:

import numpy as np
import pandas as pd
import matplotlib.pyplot as plt Loading the Iris data iris_data= pd.read_csv("Iris.csv")  Understand the data: iris_data.shape
(150,5)
iris_data['Species'].value_counts()
setosa        50
virginica     50
versicolor    50
Name: species, dtype: int64 iris_data.columns() Index(['sepal_length', 'sepal_width', 'petal_length', 'petal_width','species'],dtype='object') 1D scatter plot of the iris data: iris_setso = iris.loc[iris["species"] == "setosa"];
iris_virginica = iris.loc[iris["species"] == "virginica"];
iris_versicolor = iris.loc[iris["species"] == "versicolor"];
plt.plot(iris_setso["petal_length"],np.zeros_like(iris_setso["petal_length"]), 'o')
plt.plot(iris_versicolor["petal_length"],np.zeros_like(iris_versicolor["petal_length"]), 'o')
plt.plot(iris_virginica["petal_length"],np.zeros_like(iris_virginica["petal_length"]), 'o')
plt.grid()
plt.show()   2D scatter plot: iris.plot(kind="scatter",x="sepal_length",y="sepal_width")
plt.show()  2D scatter plot with the seaborn library : import seaborn as sns
sns.set_style("whitegrid");
sns.FacetGrid(iris,hue="species",size=4) \
.map(plt.scatter,"sepal_length","sepal_width") \
.add_legend()
plt.show()  

 Conclusion

  • Blue points can be easily separated from red and green by drawing a line.
  • But red and green data points cannot be easily separated.
  • Using sepal_length and sepal_width features, we can distinguish Setosa flowers from others.
  • Separating Versicolor from Viginica is much harder as they have considerable overlap.

Pair Plot:

A pairs plot allows us to see both the distribution of single variables and relationships between two variables. For example, let’s say we have four features ‘sepal length’, ‘sepal width’, ‘petal length’ and ‘petal width’ in our iris dataset. In that case, we will have 4C2 plots i.e. 6 unique plots. The pairs, in this case, will be :

  •  Sepal length, sepal width
  • sepal length, petal length
  • sepal length, petal width
  • sepal width, petal length
  • sepal width, petal width
  • petal length, petal width

So, here instead of trying to visualize four dimensions which are not possible. We will look into 6 2D plots and try to understand the 4-dimensional data in the form of a matrix.

sns.set_style("whitegrid");
sns.pairplot(iris,hue="species",size=3);
plt.show()

Conclusion:

  1. petal length and petal width are the most useful features to identify various flower types.
  2. While Setosa can be easily identified (linearly separable), virginica and Versicolor have some overlap (almost linearly separable).
  3. We can find “lines” and “if-else” conditions to build a simple model to classify the flower types.

Cumulative distribution function:

iris_setosa = iris.loc[iris["species"] == "setosa"];
iris_virginica = iris.loc[iris["species"] == "virginica"];
iris_versicolor = iris.loc[iris["species"] == "versicolor"];
counts, bin_edges = np.histogram(iris_setosa['petal_length'], bins=10, density = True)
pdf = counts/(sum(counts))
print(pdf);
>>>[0.02 0.02 0.04 0.14 0.24 0.28 0.14 0.08 0.   0.04]
print(bin_edges);
>>>[1.   1.09 1.18 1.27 1.36 1.45 1.54 1.63 1.72 1.81 1.9 ]
cdf = np.cumsum(pdf)
plt.grid()
plt.plot(bin_edges[1:],pdf);
plt.plot(bin_edges[1:], cdf) 

Mean, Median, and Std-Dev:

print("Means:")
print(np.mean(iris_setosa["petal_length"]))
print(np.mean(np.append(iris_setosa["petal_length"],50)));
print(np.mean(iris_virginica["petal_length"]))
print(np.mean(iris_versicolor["petal_length"]))
print("\nStd-dev:");
print(np.std(iris_setosa["petal_length"]))
print(np.std(iris_virginica["petal_length"]))
print(np.std(iris_versicolor["petal_length"])) OutPut: - Means: 1.464 2.4156862745098038 5.5520000000000005 4.26

Std-dev:
0.17176728442867112
0.546347874526844
0.4651881339845203

Learnbay provides industry accredited data science courses in Bangalore. We understand the conjugation of technology in the field of Data science hence we offer significant courses like Machine learning, Tensor Flow, IBM Watson, Google Cloud platform, Tableau, Hadoop, time series, R and Python. With authentic real-time industry projects. Students will be efficient by being certified by IBM. Around hundreds of students are placed in promising companies for data science roles. Choosing Learnbay you will reach the most aspiring job of present and future.
Learnbay data science course covers Data Science with Python, Artificial Intelligence with Python, Deep Learning using Tensor-Flow. These topics are covered and co-developed with IBM.

Data Science is Important!

Data Science is not only required to only become Data scientists but also to become eligible for the other technical jobs outside the field of DS. Know how!

Human Activity Recognition With Smart Phone

Human Activity recognition:

In this case study, we design a model by which a smartphone can detect its owner’s activity precisely. Human activity recognition with a smartphone is a very famous ML project. It is a wellness approach for a human.  Human activity is a very exciting project for AI.

Most of the smartphones have two smart sensors accelerometer and gyroscope, which is an IoT sensor. With the help of the IoT devices captures the activity of a human. The data of human activity collected through the IoT sensor. The two smartphone sensors are accelerometer and gyroscope. Accelerometer collects the data of mobile movement such as move landscape and portrait when playing mobile games and gyroscope measure the rotational movement.

An example that a smartphone has an android app that reads the accelerometers and gyroscope which can predict the human activity that he/she walking normally, walking upstairs, walking downstairs, laying down, sitting all these are the human activities.  Some of the accelerometer and gyroscope measures heart rate, calories burned, etc. by reading all the human activities these tells how much work have done in a day by the human this is also the area of the internet of things(IoT).

Working of Human task project:

1. Human activity recognition: With the help of sensors we collect the data of body movement which is captured by the smartphone. Movements are often indoor activities such as walking, walking upstairs, walking downstairs, lying down, sitting and standing. The data have recorded for the prediction of the data.

2. Data set collection of activity: The data was collected from the 30 volunteers aged between 19 to 48 performing the activities mentioned above while wearing a smartphone on waist. The example video is given below to understand Subject performing the activities and the movement data was labeled manually.

3. Human Activity Recognition Using Smartphones Data Set: The experiments have been carried out with a group of 30 volunteers within an age bracket of 19-48 years. Each person performed six activities (WALKING, WALKING_UPSTAIRS, WALKING_DOWNSTAIRS, SITTING, STANDING, LAYING) wearing a smartphone (Samsung Galaxy S II) on the waist. Using its embedded accelerometer and gyroscope, we captured 3-axial linear acceleration and 3-axial angular velocity at a constant rate of 50Hz. The experiments have been video-recorded to label the data manually. The obtained dataset has been randomly partitioned into two sets, where 70% of the volunteers were selected for generating the training data and 30% the test data. The sensor signals (accelerometer and gyroscope) were pre-processed by applying noise filters and then sampled in fixed-width sliding windows of 2.56 sec and 50% overlap (128 readings/window). The sensor acceleration signal, which has gravitational and body motion components, was separated using a Butterworth low-pass filter into body acceleration and gravity. The gravitational force is assumed to have only low-frequency components, therefore a filter with 0.3 Hz cutoff frequency was used. From each window, a vector of features was obtained by calculating variables from the time and frequency domain.

4.Download the Dataset:

  • There are “train” and “test” folders containing the split portions of the data for modeling (e.g. 70%/30%).
  • There is a “txt” file that contains a detailed technical description of the dataset and the contents of the unzipped files.
  • There is a “txt” file that contains a technical description of the engineered features.

The contents of the “train” and “test” folders are similar (e.g. folders and file names), although with differences in the specific data they contain.

Load  set data and process it:

Important libraries to import for data processing

#start with some necessary imports
import numpy as np
import pandas as pd
from google.colab import files
uploaded = files.upload()

google.colab used to fetch the data from the collaborator files.


train_data = pd.read_csv("train.csv")
train_data.head()

we select the training data set for the modeling.

train_data.Activity.value_counts()
train_data.shape

The above function defines how many rows and columns the dataset have.


train_data.describe()  

It describes that there are (8 rows and 563 columns) with all the features of the data. For numeric data, the result’s index will include countmeanstdminmax as well as lower, 50 and upper percentiles. By default the lower percentile is 25 and the upper percentile is 75. The 50 percentile is the same as the median.


uploaded = files.upload()
test_data = pd.read_csv('test.csv')
test_data.head()

Here we read the csv file to analyze the data set and the operation which is supposed to be programmed. head()
shows the first 5 rows with their respective columns so here we have (5 rows and 563 columns).

# suffling data
from sklearn.utils import shuffle

# test = shuffle(test)
train_data = shuffle(train_data)

Shuffling data serves the purpose of reducing variance and making sure that models remain general and overfit less.
The obvious case where you’d shuffle your data is if your data is sorted by their class/target. Here, you will want to shuffle to make sure that your training/test/validation sets are representative of the overall distribution of the data.

# separating data inputs and output lables
trainData = train_data.drop('Activity' , axis=1).values
trainLabel = train_data.Activity.values

testData = test_data.drop('Activity' , axis=1).values
testLabel = test_data.Activity.values
print(testLabel)

By using the above code we separate the input and output, here it determines the human activities which are captured by the IoT device. The human activities walking, standing, walking upstairs, walking downstairs, sitting and lying down are got separated to optimize the result.

# encoding labels
from sklearn import preprocessing

encoder = preprocessing.LabelEncoder()
# encoding test labels
encoder.fit(testLabel)
testLabelE = encoder.transform(testLabel)

# encoding train labels
encoder.fit(trainLabel)
trainLabelE = encoder.transform(trainLabel)

Holds the label for each class. encode categorical features using a one-hot or ordinal encoding scheme. It can also be used to transform non-numerical labels (as long as they are hashable and comparable) to numerical labels.

# applying supervised neural network using multi-layer preceptron
import sklearn.neural_network as nn
mlpSGD = nn.MLPClassifier(hidden_layer_sizes=(90,) \
, max_iter=1000 , alpha=1e-4 \
, solver='sgd' , verbose=10 \
, tol=1e-19 , random_state=1 \
, learning_rate_init=.001) 

mlpADAM = nn.MLPClassifier(hidden_layer_sizes=(90,) \
, max_iter=1000 , alpha=1e-4 \
, solver='adam' , verbose=10 \
, tol=1e-19 , random_state=1 \
, learning_rate_init=.001)
nnModelSGD = mlpSGD.fit(trainData , trainLabelE)
y_pred = mlpSGD.predict(testData).reshape(-1,1)
#print(y_pred)
from sklearn.metrics import classification_report
print(classification_report(testLabelE, y_pred))
 

import matplotlib.pyplot as plt
import seaborn as sns
fig = plt.figure(figsize=(32,24))
ax1 = fig.add_subplot(221)
ax1 = sns.stripplot(x='Activity', y=sub_01.iloc[:,0], data=sub_01, jitter=True)
ax2 = fig.add_subplot(222)
ax2 = sns.stripplot(x='Activity', y=sub_01.iloc[:,1], data=sub_01, jitter=True)
plt.show() 

 

fig = plt.figure(figsize=(32,24))
ax1 = fig.add_subplot(221)
ax1 = sns.stripplot(x='Activity', y=sub_01.iloc[:,2], data=sub_01, jitter=True)
ax2 = fig.add_subplot(222)
ax2 = sns.stripplot(x='Activity', y=sub_01.iloc[:,3], data=sub_01, jitter=True)
plt.show()

 

Click here to watch the video:

Learnbay provides industry accredited data science courses in Bangalore. We understand the conjugation of technology in the field of Data science hence we offer significant courses like Machine learning, Tensor Flow, IBM Watson, Google Cloud platform, Tableau, Hadoop, time series, R and Python. With authentic real-time industry projects. Students will be efficient by being certified by IBM. Around hundreds of students are placed in promising companies for data science roles. Choosing Learnbay you will reach the most aspiring job of present and future.
Learnbay data science course covers Data Science with Python, Artificial Intelligence with Python, Deep Learning using Tensor-Flow. These topics are covered and co-developed with IBM.

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