A beginner guide to Data Science and Machine learning Course

Learn about data science and machine learning algorithm their importance and the use of Machine learning in world fields

Because of new computing technologies, machine learning today is not like machine learning of the past. It was born from pattern recognition and the theory that computers can learn without being programmed to perform specific tasks; researchers interested in artificial intelligence wanted to see if computers could learn from data. The iterative aspect of machine learning is important because as models are exposed to new data, they are able to independently adapt. They learn from previous computations to produce reliable, repeatable decisions and results. It’s a science that’s not new – but one that has gained fresh momentum.

What you’ll learn

  • After this course Students will have intermediate Knowledge of Machine learning concepts..
  • After this course Students will have intermediate Knowledge of Data science concepts..
  • Applications of Data science in Real world..
  • complete guide for starting the journey of data science.

Course Content

  • Introduction of Machine Learning –> 7 lectures • 42min.
  • Introduction of Data Science –> 4 lectures • 30min.

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Requirements

  • Basic Knowledge of Data.
  • No coding Experience.

Because of new computing technologies, machine learning today is not like machine learning of the past. It was born from pattern recognition and the theory that computers can learn without being programmed to perform specific tasks; researchers interested in artificial intelligence wanted to see if computers could learn from data. The iterative aspect of machine learning is important because as models are exposed to new data, they are able to independently adapt. They learn from previous computations to produce reliable, repeatable decisions and results. It’s a science that’s not new – but one that has gained fresh momentum.

While many machine learning algorithms have been around for a long time, the ability to automatically apply complex mathematical calculations to big data – over and over, faster and faster – is a recent development. Here are a few widely publicized examples of machine learning applications you may be familiar with:

The heavily hyped, self-driving Google car?. The essence of machine learning.

Online recommendation offers such as those from Amazon and Netflix?. Machine learning applications for everyday life.

Knowing what customers are saying about you on Twitter?. Machine learning combined with linguistic rule creation.

Fraud detection?. One of the more obvious, important uses in our world today.

Data science is the field of applying advanced analytics techniques and scientific principles to extract valuable information from data for business decision-making, strategic planning and other uses.

It’s increasingly critical to businesses.

Data science is the study of data. It involves developing methods of recording, storing, and analyzing data to effectively extract useful information.

The goal of data science is to gain insights and knowledge from any type of data — both structured and unstructured.

The insights that data science generates help organizations increase operational efficiency, identify new business opportunities and improve marketing and sales programs, among other benefits. Ultimately, they can lead to competitive advantages over business rivals.

Data science incorporates various disciplines — for example, data engineering, data preparation, data mining, predictive analytics, machine learning and data visualization, as well as statistics, mathematics and software programming.

It’s primarily done by skilled data scientists, although lower-level data analysts may also be involved. In addition, many organizations now rely partly on citizen data scientists, a group that can include business intelligence (BI) professionals, business analysts, data-savvy business users, data engineers and other workers who don’t have a formal data science background.

This comprehensive guide to data science further explains what it is, why it’s important to organizations, how it works, the business benefits it provides and the challenges it poses. You’ll also find an overview of data science applications, tools and techniques, plus information on what data scientists do and the skills they need.

Throughout the guide, there are hyperlinks to related TechTarget articles that delve more deeply into the topics covered here and offer insight and expert advice on data science initiatives.

Data science is related to computer science, but is a separate field. Computer science involves creating programs and algorithms to record and process data, while data science covers any type of data analysis, which may or may not use computers.

Data science is more closely related to the mathematics field of Statistics, which includes the collection, organization, analysis, and presentation of data.

Because of the large amounts of data modern companies and organizations maintain, data science has become an integral part of IT. For example, a company that has petabytes of user data may use data science to develop effective ways to store, manage, and analyze the data. The company may use the scientific method to run tests and extract results that can provide meaningful insights about their users.

Why is data science important?

Data science plays an important role in virtually all aspects of business operations and strategies. For example, it provides information about customers that helps companies create stronger marketing campaigns and targeted advertising to increase product sales. It aids in managing financial risks, detecting fraudulent transactions and preventing equipment breakdowns in manufacturing plants and other industrial settings. It helps block cyber-attacks and other security threats in IT systems.

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