Step by Step Learn To Build & Deploy Machine Learning, Data Science Web App Projects (Python,Flask,Django Heruko Cloud)
What you'll learn
The Impacts Machine Learning and Data Science is having on society.
To know what problems Machine Learning can solve, and how the Machine Learning Process works.
To really understand computer technology has changed the world, with an appreciation of scale.
How to avoid problems with Machine Learning, to successfully implement it without losing your mind!
Requirements
Interest To Learn
Description
In This Course, Solve Business Problems Using Data Science Practically. Learn To Build & Deploy Artificial Intelligence, Machine Learning, Data Science, Auto Ml, Deep Learning, Natural Language Processing (NLP) Web Applications Projects With Python (Flask, Django, Heruko, Streamlit Cloud).
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What Is Machine Learning?
A subset of artificial intelligence (AI), machine learning (ML) is the area of computational science that focuses on analyzing and interpreting patterns and structures in data to enable learning, reasoning, and decision-making outside of human interaction. Simply put, machine learning allows the user to feed a computer algorithm an immense amount of data and have the computer analyze and make data-driven recommendations and decisions based on only the input data. If any corrections are identified, the algorithm can incorporate that information to improve its future decision-making.
Why Is Machine Learning Important?
Data is the lifeblood of all businesses. Data-driven decisions increasingly make the difference between keeping up with the competition or falling further behind. Machine learning can be the key to unlocking the value of corporate and customer data and enacting decisions that keep a company ahead of the competition.
Machine Learning Use Cases
Machine learning has applications in industries, including manufacturing, retail, healthcare and life sciences, travel and hospitality, financial services, energy, feedstock, and utilities. Use cases include:
Manufacturing. Predictive maintenance and condition monitoring
Retail. Upselling and cross-channel marketing
Healthcare and life sciences. Disease identification and risk satisfaction
Travel and hospitality. Dynamic pricing
Financial services. Risk analytics and regulation
Energy. Energy demand and supply optimization
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