Data Science Practical:Real world Machine Learning Projects

Duration: 3h 12m | Video: .MP4 1280×720, 30 fps(r) | Audio: AAC, 48000Hz, 2ch | Size: 1.79 GB
Genre: eLearning | Language: English
In this course you will build real world data science and machine learning projects

What you’ll learn
Build machine learning models

Requirements
Knowledge of machine learning

Description
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.

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.

Who this course is for:
Interest in machine learning

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