4.35 (26 reviews)
☑ Make robust Machine Learning models
☑ Build machine learning models
☑ Data Science Application in Health Care
☑ Building Machine Learning Models in Health care
☑ Building Classification Models
☑ Understand the intuition behind ML models
Data Science is rapidly growing to occupy all the industries of the world today.
Medicine and healthcare are two of the most important part of our human lives. Traditionally, medicine solely relied on the discretion advised by the doctors. For example, a doctor would have to suggest suitable treatments based on a patient’s symptoms. However, this wasn’t always correct and was prone to human errors. However, with the advancements in computers and in particular, Data Science, it is now possible to obtain accurate diagnostic measures.
A groundbreaking study in 2020 reported 90% of the entirety of the world’s data has been created within the previous two years. Let that sink in. In just two years, we've collected and processed 9x the amount of information than the previous 92,000 years of humankind combined. And it isn’t slowing down. It’s projected we’ve already created 2.7 zettabytes of data, and by 2025, that number will balloon to an astounding 44 zettabytes.
What do we do with all of this data? How do we make it useful to us? What are it's real-world applications? These questions are the domain of data science.
Every company will say they’re doing a form of data science, but what exactly does that mean? The field is growing so rapidly, and revolutionizing so many industries, it's difficult to fence in its capabilities with a formal definition, but generally data science is devoted to the extraction of clean information from raw data for the formulation of actionable insights.
Commonly referred to as the “oil of the 21st century," our digital data carries the most importance in the field. It has incalculable benefits in business, research and our everyday lives. Your route to work, your most recent Google search for the nearest coffee shop, your Instagram post about what you ate, and even the health data from your fitness tracker are all important to different data scientists in different ways. Sifting through massive lakes of data, looking for connections and patterns, data science is responsible for bringing us new products, delivering breakthrough insights and making our lives more convenient.A groundbreaking study in 2013 reported 90% of the entirety of the world’s data has been created within the previous two years. Let that sink in. In just two years, we've collected and processed 9x the amount of information than the previous 92,000 years of humankind combined. And it isn’t slowing down. It’s projected we’ve already created 2.7 zettabytes of data, and by 2020, that number will balloon to an astounding 44 zettabytes.
In this course, we are going to provide students with knowledge of key aspects of state-of-the-art classification techniques. We are going to build 5 projects of the Healthcare or Medical industry from scratch using the real-world dataset, here’s a sample of the projects we will be working on:
Mortality Prediction In ICU Using ANN
Kyphosis Disease Classification
Blood Donation Analysis
Suicide Rate Trend Analysis
DNA Classification of Humans And Chimpanzee
Download the code
Exploratory data analysis
Checking the correlations
This course is extremely useful. It explains many advanced topics from the field of statistics, data science and machine learning in an easy way.
It is a good course that helps the understanding of data science topics in a pre-intermediate level. Python code explanations are really helpful, in the same way, the instructors do the best to provide familiar understanding to those students who have notions of programming.
Basics of the course are great, got a nice overview of the Data Science field and basic Data Science practical skills.
Model was explained in a nut-shell, sometimes it is good to understand basic commands and then jump further.