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Data science is a groundbreaking tool in the healthcare industry for improving patient outcomes, enhancing efficiency, and reducing costs. With the vast amounts of data generated from electronic health records, medical devices, clinical trials, and other sources, healthcare organizations can leverage data science to analyze and make sense of this data in ways that were previously impossible. These are some of the reasons that the demand for data scientists is surging. This is also supported by, well, data: According to the U.S. Bureau of Labor Statistics, nearly 13,500 openings for data scientists are predicted each year from 2023 to 2031. So, if you are looking to build a lucrative career in this space, then this blog is for you. Learning the fundamentals of data science in healthcare, its benefits, and its challenges will be a good start.What is Data Science?
Simply put, data science is the study of massive amounts of data to extract meaningful insights to make informed decisions. It uses modern tools and techniques to identify patterns and derive meaningful information from raw, structured, and unstructured data. Data science today is implemented across a variety of industries such as healthcare, e-commerce, finance, transport, and gaming.Applications of Data Science in Healthcare
The health industry generates massive amounts of data. Therefore, being able to handle, manage, and organize it to help in patient care, improve the efficiency of processes, etc, is very important. This is where data science in healthcare can make a difference. Here are 10 ways in which data science can make healthcare better.Medical Image Analysis
The process of obtaining meaningful information from medical images is called medical image analysis. Imaging techniques include Magnetic Resonance Imaging (MRI) scans, Computed Tomography (CT) scans, and X-rays. The involvement of data science in these imaging techniques has revolutionized the healthcare industry as it helps extract complex information from numerous images in a short period. This, in turn, helps to save a lot of time and money.Research and Development Improved Patient Outcomes
Today, people extensively use smartwatches and physical fitness monitors, all thanks to the Internet of Things (IoT). These gadgets can track and manage people’s health. They also generate a lot of data, which data scientists then scan to identify patterns and extract meaningful information. Doctors use this information to treat their patients more effectively.Cost-Effectiveness
Data scientists can analyze Electronic Health Records (EHRs) to identify patients’ health patterns. Doing so can help prevent unnecessary treatment or hospitalization. Conversely, doctors can catch any change in the pattern in time and ensure people get treated on time.Decoding Genomes
Before the arrival of data science tools and powerful computation, healthcare organizations spent a small fortune on genomics (the study of sequencing and interpretation of genomes). Now, data science makes it possible to derive insights from the gene at a much lower cost and in a much shorter period.Undiagnosed Disease Discovery Predictive Analytics
The predictive analytics model uses data to analyze and search for patterns and correlations. For instance, experts can look at data gathered from multiple sources for patterns and correlations and can try to identify many things. These can be the symptoms of a disease, the extent of damage, and the stages of the disease among other things. The predictive analytics model uses all of this information to diagnose a patient’s condition and strategize the appropriate treatment.Diagnostics
According to CNN, more than seven million patients are misdiagnosed annually in emergency rooms in the U.S. To fix such diagnostic failures, startups such as Enlitic and Bruxlab have started using data science to increase the efficiency and accuracy of diagnostics and create better patient outcomes.Automated Pharmacies Virtual Assistance for Patients
Data scientists have developed AI platforms and chatbots to help people get virtual medical assistance during emergencies. Furthermore, people who suffer from mental illnesses such as depression, anxiety, and Alzheimer’s can leverage virtual applications to complete their daily tasks. Ada and Woebot are some of the most popular examples of virtual assistants.
ALSO READ: What is Data Science? Why is This Career Path in Demand? Find Out Now!Challenges of Data Science in Healthcare
Although data science in healthcare has the potential to improve patient outcomes and reduce costs, it also has several challenges that need to be addressed. Some of the challenges are as follows:Privacy and Security
Healthcare data can often be sensitive and subject to privacy regulations. Therefore, protecting such data from misuse is key to earning and maintaining patients’ trust. Data breaches in healthcare can also involve legal complications.Translating Insights Into Clinical Practice
One of the main challenges of data science in the healthcare sector is translating findings into clinical practice. It needs to be integrated into clinical workflows to make sure the findings generated can be put into practice to improve patient outcomes.Ethical Concerns
There are several ethical concerns when it comes to leveraging data science tools in healthcare. Patients’ consent, for example, is one of the most important ethical considerations to be aware of when using this technology in the healthcare industry.Gain Insights on Data Science in Healthcare with Emeritus
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Data science internships can help students provide critical industry knowledge and skills
is a rising phenomenon among modern business owners. More and more companies are adopting
and analytics technologies to ensure complete business transparency and also make sure that the basic demand and supply needs of customers are met without any hindrances. This phenomenon has led to an increase in data science and b
ig data job opportunities
in major tech companies. Students from both tech and non-tech backgrounds are now seeking to realize their
careers in data science.
And what better way is there to gain experience in this domain other than utilizing the real industry resources through volunteer projects and internships in the top companies. Yes!
Data science internships
can help you gain the needed industry guidance and skills that you need to ace your first data science job interview. In this article, we have listed the top
data science internship
s in February that can help students gain massive amounts of industry exposure in 2023.
ZeoMinds IT Solutions Pvt Ltd
The interested candidates for this job profile should possess a minimum of 0-4 years of experience in any related domain and should have strong knowledge in Python, statistics, SQL, machine learning, deep learning, and other related technologies. But aspirants who are ready to commute on a regular basis should only apply for this internship since it does not allow working remotely.
As an Our Hiraya intern, the aspirants will be responsible for developing and documenting security for the company’s security ecosystem, using the company’s own SaaS system that is hosted with Azure cloud services, hosting hundreds of customer cloud services. With the help of the candidates’ skills and experience in various languages and tech stacks, the interns will have to discover and fix security vulnerabilities.
Oracuz Infotech Pvt. Ltd
The company is urgently hiring candidates and students who are entirely freshers and have knowledge in data science, AI, Python, PHP, manual testing, and digital marketing for client projects. The interns will be subjected to company training with live projects and placement with the same company.
This is a 6-month long internship where the interns will be self-learning on Google concepts like pub/sub, looker, Dataflow, and such other topics. They will be required to give technical service to students troubleshooting their queries, working on real-time projects on data science and big data to create and maintain technical documentation.
Precision Agriculture for Development
The candidate will be responsible for developing a new data science/ML program to be launched by the company, working with SMEs and other experts in areas like NLP, computer vision, and such others, working on case studies, class materials, and projects for the data science/ML course, research and develop end-to-end project prototypes and automation of business problems, introduce and optimize program structures for fluent workflow and delivery.
The candidates will be working on existing BI projects and on data analytics product development support. The primary aim of the company is to aid professionals across the globe in acquiring the required skill sets to remain relevant in the fast-evolving digital landscape. EC Analytics Consulting possesses expertise in leading business analytics tools and technologies like Tableau, Microsoft Power BI, and open-source business intelligence tools.
Splisys IT Consulting Private Limited
The candidate should possess a degree of BE, MCA, BCA, or other related degrees, with basic programming skills, and relevant experience in Python, R, statistics, inferential statistics, regression and ANOVA, exploratory data analysis, and such others.
Innovate Networks E-Services Private Limited
With internet penetration at its peak, theTop 10 data science companies of 2023
Oracle is one of the largest vendors in the enterprise IT market and the shorthand name of its flagship product, a relational database management system (RDBMS) that’s formally called Oracle Database. Headquartered in Austin, Texas, the American multinational company sells database software and technology, cloud engineered systems, and enterprise software products, particularly, its own brands of database management systems. Oracle was founded by engineers Larry Ellison, Bob Miner, and Ed Oates as a software development laboratory in 1977. Later, the company went public in 1986.
SPEC INDIA is a custom software development company with proven capabilities in providing accelerated and cost-effective enterprise software development solutions to a large portfolio of customers across the globe. The company showcases end-to-end business transformation. Headquartered in Ahmedabad, India, SPEC INDIA strives to serve its clients with focused collaboration, cutting-edge technologies, immaculate user experience, well-tested solutions and round-the-clock support.
Numerator is a data technology company reinventing market research. Headquartered in Chicago, Illinois, Numerator develops and provides market intelligence solutions that bring together omnichannel marketing, merchandising and sales data for brand, retail, and agency clients. In a fast-paced environment that is centred on delivering client value through strong market analytics, Numerator associates and leverages the opportunity to work with top brands and companies on the planet.
Teradata Corporation is a provider of database and analytics-related software, products and services. Founded in 1979, the company serves as an open-source Database Management System for developing large-scale data warehousing applications. The idea was actually born out of research at the California Institute of technology. Teradata is headquartered in Brentwood, California. The company delivers real-time, intelligent answers by leveraging relevant data, regardless of volume of query.
Evalueserve is a global professional services provider offering research, analytics and data management services. Headquartered at Schaffhausen, Switzerland, the company is powered by mind and machine, a unique combination of human expertise and best-in-class technologies that use smart algorithms to simplify key tasks. This approach enables Evalueserve to design and manage process that can generate and harness insights on a large scale, significantly cutting costs and timescales and helping businesses that partner with us to overtake the competition.
SPINS is a provider of retail consumer insights, analytics and consulting for the natural, organic and speciality products industry. The company strives to transform raw data into intelligent and actionable business solutions. From SPINS founding in 1997 to now, the company and industry definitions have become the common language of the natural products industry, helping brands communicate their growth to retailers to scale their distribution and helping retailers understand and maintain their points of difference.
Fractal Analytics is one of the leading global providers of artificial intelligence-led solutions. Founded in 2000 by Srikanth Velamakanni, Pranay Agrawal, Nirmal Palaparthi, Pradeep Suryanarayan and Ramakrishna Reddy, the company aims to power every human decision in the enterprise and combines AI, Engineering & Design to solve complex problems for Fortune 500 clients. Headquartered in New York, Fractal has company presence across fifteen global locations including the United Kingdon, Ukraine and India.
Sigma Data Systems is a public sector applicant tracking and test management software provider. Founded in 1978, the company understands the criticality of each piece of data in today’s world and the next generation. Sigma has pre-defined workshop patterns to understand the problem and based on this, the company provides unique solutions to every customer by using various tools and framework.
You can apply for these government data science jobs across different fields.
Data scientists are not limited to the private sector. They are widely being employed in the public sector as well.Data Analyst at Reserve Bank of India
The Reserve Bank of India is looking to hire a data analyst. The job location is Delhi with candidates having an age limit of 23-37 years. The selection process is through an online examination interview. Qualifications
Post Graduate Degree in Statistics / Econometrics / Mathematics / Mathematical Statistics / Finance / Economics / Computer Science or B E / B Tech in Computer Science from an Indian institute.Jr. Data Scientist at Assam State ARIAS
Location: Guwahati The Jr. Data scientist so hired will be working in the Market Intelligence Cell (MIC). Responsibilities
The candidate will be responsible for data gathering and dissemination.
One will also carry out Diagnostic studies, surveys, gap analysis, etc.
The candidate will conduct Price forecasts for APART commodities.
One will also provide related project information, market and weather intelligence.
The candidate will collect historical wholesale price and volume data of the Project’s Agri commodities from Regulated markets and rural haats.
Masters in Statistics/ Agri Statistics/ Economics/ Agricultural Economics or a closely related field.
At least 4 years of experience in the Agri field.
Skilled in data mining and analytical skills.
Fluency in the English Language
Apply hereData Analyst at Bank of Baroda
Location: Mumbai Bank of Baroda has a good number of openings for the role of a data analyst. The job location is Mumbai with candidates having an age limit of 25-40 years. For the selection process, you need to visit the official website and download the application form, fill it and then submit it. Qualifications
Post Graduate Degree in Statistics / Econometrics / Mathematics / Mathematical Statistics / Finance / Economics / Computer Science or B E / B Tech in Computer Science from an Indian institute.
Prior experience in data handling in the banking field.
Strong experience in data collection and management.
Apply HereSenior Data Economist at Indian Overseas Bank
Location: Chennai The Indian Overseas Bank is looking for a Senior Economist in Chennai. The selection process is through an online application and then a personal interview. Responsibilities
The candidate will be responsible to collect and analyze data to provide strategic inputs to the top management of the Bank.
One will analyze financial and economic indicators of domestic and international levels and measure their impact on various markets through data.
Post Graduate in economics with a specialization in monetary or financial econometrics.
At least 10 years of experience in the banking industry.
Knowledge of different data analytical techniques.
Strong communication skills
Apply hereChief Data Officer at Bank of Baroda
Bank of Baroda is looking for a Chief Data Officer in Mumbai. The chief data officer will lead the bank’s data organization. The process of selection is through an application form which can be checked on their official website. Though the opening is in April, the job is still open to apply. Qualifications
A Degree (Graduation) in any discipline from a University recognized by the Govt. Of India, Govt. Bodies or AICTE etc.
Minimum 15 years of experience of which 10 years in technology and 5 years in data management and security.
Experience in Executive or Strategic projects, driving modern data-related initiatives across the enterprise is preferred.
Experience in the BFSI sector is a bonus.
Data scientists are not limited to the private sector. They are widely being employed in the public sector as well. Government data science jobs are filled in areas such as weather prediction, fraud detection, intelligence, tax compliance, etc. Data science jobs in the government sector have a lot of perks. In the same way as other government jobs, data scientists in government are serving the public interest. One reason individuals may renounce a more lucrative job for a private organization is for the opportunity to have a positive impact on the nation and their community. The ability to shape policies that help people is referred to as perhaps the most compensating part of working in government jobs in data analytics , and they presently have the chance. Let’s look at the top data science jobs in PSUs in July chúng tôi Reserve Bank of India is looking to hire a data analyst. The job location is Delhi with candidates having an age limit of 23-37 years. The selection process is through an online examination interview.: Guwahati The Jr. Data scientist so hired will be working in the Market Intelligence Cell (MIC).: Mumbai Bank of Baroda has a good number of openings for the role of a data analyst. The job location is Mumbai with candidates having an age limit of 25-40 years. For the selection process, you need to visit the official website and download the application form, fill it and then submit it.: Chennai The Indian Overseas Bank is looking for a Senior Economist in Chennai. The selection process is through an online application and then a personal chúng tôi of Baroda is looking for a Chief Data Officer in Mumbai. The chief data officer will lead the bank’s data organization. The process of selection is through an application form which can be checked on their official website. Though the opening is in April, the job is still open to apply.
Here is the list of the top 10 remote data science jobs in India to apply for right now
Data science is an essential part of many industries today, that deals with vast volumes of data using modern tools and techniques to find unseen patterns. Remote data scientist jobs help organizations identify patterns and trends in their data to provide information about lucrative opportunities, necessary improvements, and potential innovations. Here is the list of the top 10 remote data science jobs in India to apply for right now.Remote Data Scientist Jobs- Turing
This company handles millions of tasks for businesses looking to scale their ML development. These job responsibilities are doing end-to-end product analytics workflow, including formulating success metrics, socializing them across the organization, and creating reports. Identify, diagnose, and recommend projects to scale performance and operational efficiencies to the business to boost sustainable growth.
Apply hereData Scientist Jobs in Bangalore- Turing
Turing’s mission is to unleash the world’s untapped human potential. The job responsibilities are identifying the business challenges and product improvement opportunities and finding solutions to business issues utilizing analytics and suggesting strategies. Develop models of user behaviors to analyze or power production systems.
Apply hereWFH Data Scientist Jobs in Hyderabad- Turing
This data scientist job requires you to coordinate with engineers and analysts to formulate guided, multifaceted analytic studies against a large volume of data and collaborate in research and indulge in analytic activities utilizing both unstructured and structured data points; work closely with cross-functional business units, and engineering teams to build a long-term data platform architecture strategy.
Apply hereLead Data Analyst(FinTech)- Tide
If selected for this job, the day-to-day responsibilities will include identifying valuable data sources and automating collection processes, and combining models through ensemble modeling. It includes presenting information using data visualization techniques, improvement of product development, marketing techniques, business strategies, etc.
Apply hereAnalyst – Data Foundation-General Mills:
The Digital and Technology team of General Mills India Centre is looking for a passionate and enthusiastic individual to work in General Mills’ DnA organization. Job role responsibilities include maintaining and enhancing the supply chain GCP project. Understand the end-to-end supply chain business processes, data, and DnA technology • Effective verbal and written communication and influencing skills are a must.
Apply hereRemote Data Wrangling/Data Science Expert Jobs-Turing
For this job, the person should have expertise in statistical analysis as well as pattern and anomaly detection and expertise in data cleaning, data wrangling, quantitative analysis, and data mining. Responsibilities include collaborating with stakeholders to understand business requirements and analyze dashboards, manually convert, map, and organize data for convenient data consumption.
Apply hereRemote Machine Learning Data Scientist Jobs -Turing
A leading company active in the environmental sector and assisting battery manufacturers in making rapid progress ahead of the competition is looking for a Machine Learning Data Scientist. Job responsibilities are to collaborate and deliver against an ambitious product roadmap, improve the engineering processes to increase team effectiveness, and manage individual project requirements, priorities, and deliverables promptly.
Apply hereRemote Data Analytics/Operations Engineer Jobs-Turing
This job promotes the DataOps approach to the delivery process to automate data provisioning, and testing, and collaborate with data architects to build a data pipeline and optimize it for data solutions including data science products. Make sure to implement DataOps and automation initiatives for solution deployment in production.
Apply hereSenior Product Manager – Data Platform- Tide
This role is an excellent opportunity for anyone interested in building internal products in a rapidly scaling environment and ensuring stakeholders are updated and informed about changes in our data platform Building and tracking metrics for the performance of our data platform team. You will be able to influence their roadmap, learn about best practices and be able to quickly see the impact of your work.
Here is a list of 15 Free Data Science Courses to get you going initially
These are well-curated courses. Please probe the resources attached to these free data science courses to understand them betterIntroduction
It is Data Science, not Rocket Science.
Due to the democratization of AI and ML, the data science field is undergoing massive growth. A lot of long shot applications like self-driven cars, smart AI assistants have come to life. It is really exciting!
I have come across hundreds of data science aspirants who really want to pursue this field but aren’t able to navigate their way through this uncertain path. It is not their fault. The majority of people haven’t graduated in this field. So getting back to the main question – How do build a successful career in data science and more importantly, what are the necessary resources to do so?
In this article, I am listing down 15 free courses, starting with beginner courses that will help you navigate your way through a data science career and then jump into each important machine learning algorithm. I have also mentioned a few project-based courses, this will surely help you in practical learning.
However, These free data science courses are not a substitute for a well-guided course. The AI and ML Blackbelt+ program is the leading industry course for data science. Along with 14+ courses and 39+ projects, it offers you –
1:1 Mentorships with Industry Practitioners
Comprehensive & Personalised Learning Path
Dedicated Interview Preparation & Support
You can check the entire program here.List of Free Data Science Courses
Introduction to AI and ML
Python for Data Science
Pandas for Data Analysis
Introduction to NLP
Getting started with Neural Networks
Loan Prediction Problem
Winning Data Science Competitions
“The AI revolution is here – are you prepared to integrate it into your skillset? How can you leverage it in your current role? What are the different facets of AI and ML?”
Artificial Intelligence and Machine Learning have become the centerpiece of strategic decision making for organizations. They are disrupting the way industries and roles function – from sales and marketing to finance and HR, companies are betting big on AI and ML to give them a competitive edge.
And this, of course, directly translates to their hiring. Thousands of vacancies are open as organizations scour the world for AI and ML talent. There hasn’t been a better time to get into this field!
This course helps you answer all the conceptual questions you might have about building a successful career in data science and machine learning.
You can find the course material here.
Do you want to enter the field of Data Science? Are you intimidated by the coding you would need to learn? Are you looking to learn Python to switch to a data science career?
You have come to just the right place!
Most industry experts recommend starting your Data Science journey with Python
Across the biggest companies and startups, Python is the most used language for Data Science and Machine Learning Projects
Stackoverflow survey for 2023 had Python outrank Java in the list of most loved languages
Python is a very versatile language since it has a wide array of functionalities already available. The sheer range of functionalities might sound too exhaustive and complicated, you don’t need to be well-versed with them all.
Python has rapidly become the go-to language in the data science space and is among the first things recruiters search for in a data scientist’s skill set.
It consistently ranks top in global data science surveys and its widespread popularity will only keep on increasing in the coming years.
Over the years, with strong community support, this language has obtained a dedicated library for data analysis and predictive modeling.
You can find the course material here.
Now that we have the basics cleared up – Let’s move to specialized courses for machine learning and its libraries in Python.
Pandas is one of the most popular Python libraries in data science. In fact, Pandas is among those elite libraries that draw instant recognition from programmers of all backgrounds, from developers to data scientists.
According to a recent survey by StackOverflow, Pandas is the 4th most used library/framework in the world!
This free course will introduce you to the world of Pandas in Python, how you can use Pandas to perform data analysis and data manipulation. The perfect starting course for Python and Pandas beginners!
Scikit-learn, or sklearn for short, is the first Python library we turn to when building machine learning models. Sklearn is unanimously the favorite Python library among data scientists. As a newcomer to machine learning, you should be comfortable with sklearn and how to build ML models, including:
Linear Regression using sklearn
Logistic Regression using sklearn, and so on.
There’s no question – scikit-learn provides handy tools with easy-to-read syntax. Among the pantheon of popular Python libraries, scikit-learn (sklearn) ranks in the top echelon along with Pandas and NumPy.
We love the clean, uniform code, and functions that scikit-learn provides. The excellent documentation is the icing on the cake as it makes a lot of beginners self-sufficient with building machine learning models using sklearn.
In short, sklearn is a must-know Python library for machine learning. Whether you want to build linear regression or logistic regression models, decision tree,s or a random forest, sklearn is your go-to library.
You can find the course material here.
K-Nearest Neighbor (KNN) is one of the most popular machine learning algorithms. As a newcomer or beginner in machine learning, you’ll find KNN to be among the easiest algorithms to pick up.
And despite its simplicity, KNN has proven to be incredibly effective at certain tasks in machine learning.
The KNN algorithm is simple to understand, easy to explain, and perfect to demonstrate to a non-technical audience (that’s why stakeholders love it!). That’s a key reason why it’s widely used in the industry and why you should know how the algorithm works.
You can find the course material here.
Linear regression and logistic regression are typically the first algorithms we learn in data science. These are two key concepts not just in machine learning, but in statistics as well.
Due to their popularity, a lot of data science aspirants even end up thinking that they are the only forms of regression! Or at least linear regression and logistic regression are the most important among all forms of regression analysis.
The truth, as always, lies somewhere in between. There are multiple types of regression apart from linear regression:
Stepwise regression, among others.
Linear regression is just one part of the regression analysis umbrella. Each regression form has its own importance and a specific condition where they are best suited to apply.
Regression analysis marks the first step in predictive modeling. The different types of regression techniques are widely popular because they’re easy to understand and implement using a programming language of your choice.
You can find the course material here.
Bonus: This free course comes with a degree as well.
A Decision Tree is a flowchart like structure, where each node represents a decision, each branch represents an outcome of the decision, and each terminal node provides a prediction/label.
This course covers the following topics –
The different splitting criterion for decision tree-like Gini, chi-square
Implementation of the decision tree in Python
You can access the course here.
Ensemble learning is a powerful machine learning algorithm that is used across industries by data science experts. The beauty of ensemble learning techniques is that they combine the predictions of multiple machine learning models. You must have used or come across several of these ensemble learning techniques in your machine learning journey:
These ensemble learning techniques include popular machine learning algorithms such as XGBoost, Gradient Boosting, among others. You must be getting a good idea of how vast and useful ensemble learning can be!
You can find the course material here.
Naive Bayes ranks in the top echelons of the machine learning algorithms pantheon. It is a popular and widely used machine learning algorithm and is often the go-to technique when dealing with classification problems.
The beauty of Naive Bayes lies in its incredible speed. You’ll soon see how fast the Naive Bayes algorithm works as compared to other classification algorithms. It works on the Bayes theorem of probability to predict the class of unknown datasets. You’ll learn all about this inside the course!
So whether you’re trying to solve a classic HR analytics problem like predicting who gets promoted, or you’re aiming to predict loan default – the Naive Bayes algorithm will get you on your way.
You can find the course material here.
Want to learn the popular machine learning algorithm – Support Vector Machines (SVM)? Support Vector Machines can be used to build both Regression and Classification Machine Learning models.
This free course will not only teach you the basics of Support Vector Machines (SVM) and how it works, it will also tell you how to implement it in Python and R.
This course on SVM would help you understand hyperplanes and Kernel tricks to leave you with one of the most popular machine learning algorithms at your disposal.
You can find the course material here.
Evaluation metrics form the backbone of improving your machine learning model. Without these evaluation metrics, we would be lost in a sea of machine learning model scores – unable to understand which model is performing well.
Wondering where evaluation metrics fit in? Here’s how the typical machine learning model building process works:
We build a machine learning model (both regression and classification included)
Get feedback from the evaluation metric(s)
Make improvements to the model
Use the evaluation metric to gauge the model’s performance, and
Continue until you achieve a desirable accuracy
Evaluation metrics, essentially, explain the performance of a machine learning model. An important aspect of evaluation metrics is their capability to discriminate among model results.
If you’ve ever wondered how concepts like AUC-ROC, F1 Score, Gini Index, Root Mean Square Error (RMSE), and Confusion Matrix work, well – you’ve come to the right course!
You can find the course material here.
Natural Language Processing is expected to be worth 30 Billion USD by 2024 with the past few years seeing immense improvements in terms of how well it is solving industry problems at scale.
This free course will guide you to take your first step into the world of natural language processing with Python and build your first sentiment analysis Model using machine learning.
From classifying images and translating languages to building a self-driving car, neural networks are powering the world around us.
Neural networks are the present and the future. The different neural network architectures like convolutional neural networks (CNN), recurrent neural networks (RNN), and others have altered the deep learning landscape.
This free course will give you a taste of what a neural network is, how it works, what are the building blocks of a neural network, and where you can use neural networks.
Do you need a free course which can help you solve data science problems practically? This amazing course will guide you in solving a real-life project.
This course is designed for people who want to solve binary classification problems. Classification is a skill every Data Scientist should be well versed in. In this course, you will get to solve a real-life case study of Dream Housing Finance.
There is no substitute for experience. And that holds true in Data Science competitions as well. These cut-throat hackathons require a lot of trial-and-error, effort, and dedication to reach the ranks of the elite.
This course is an amalgamation of various talks by top data scientists and machine learning hackers, experts, practitioners, and leaders who have participated and won dozens of hackathons. They have already gone through the entire learning process and they showcase their work and thought process in these talks.
This course features top data science hackers and experts, including Sudalai Rajkumar (SRK), Dipanjan Sarkar, Rohan Rao, Kiran R, and many more!
From effective feature engineering to choosing the right validation strategy, there is a LOT to learn from this course so get started today!
You can find the course material here.End Notes
It is exciting to be in the data science industry. These free courses cover almost all the basics you will require to kickstart your career in data science.
I hope this helps you clear all the concepts. If you want to learn data science comprehensively then I have a great suggestion for you guys! The AI and ML Blackbelt+ program the industry leader in data science programs. Here you will not only get access to 14+ courses and 39+ projects but 1:1 mentorship sessions. The mentor will help you customize the learning path according to your career goals and make sure that you achieve them!
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