Learn to create machine learning algorithms in Python for students and professionals
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What you'll learn
- Learn Python programming and Scikit learn applied to machine learning regression
- Understand the underlying theory behind simple and multiple linear regression techniques
- Learn to solve regression problems (linear regression and logistic regression)
- Learn the theory and the practical implementation of logistic regression using sklearn
- Learn the mathematics behind decision trees
- Learn about the different algorithms for clustering
Requirements
- Experience with the basics of Python
- Readiness, flexibility, and passion for learning
- Basic mathematical skills
Description
To understand how organizations like Google, Amazon, and even Udemy use
machine learning and artificial intelligence (AI) to extract meaning and
insights from enormous data sets, this machine learning course will provide
you with the essentials. According to Glassdoor and Indeed, data scientists
earn an average income of $120,000, and that is just the norm!
When it comes to being attractive, data scientists are already there. In a
highly competitive job market, it is tough to keep them after they have been
hired. People with a unique mix of scientific training, computer expertise,
and analytical abilities are hard to find.
Like the Wall Street "quants" of the 1980s and 1990s, modern-day data
scientists are expected to have a similar skill set. People with a
background in physics and mathematics flocked to investment banks and hedge
funds in those days because they could come up with novel algorithms and
data methods.
That being said, data science is becoming one of the most well-suited
occupations for success in the twenty-first century. It is computerized,
programming-driven, and analytical in nature. Consequently, it comes as no
surprise that the need for data scientists has been increasing in the
employment market over the last several years.
The supply, on the other hand, has been quite restricted. It is challenging
to get the knowledge and abilities required to be recruited as a data
scientist.
In this course, mathematical notations and jargon are minimized, each topic
is explained in simple English, making it easier to understand. Once you've
gotten your hands on the code, you'll be able to play with it and build on
it. The emphasis of this course is on understanding and using these
algorithms in the real world, not in a theoretical or academic
context.
You'll walk away from each video with a fresh idea that you can put to use
right away!
All skill levels are welcome in this course, and even if you have no prior
statistical experience, you will be able to succeed!
Who this course is for:
- Anyone who want to pursue a career in Machine Learning
- Any Python programming enthusiast willing to add machine learning proficiency to their portfolio
- Technologists who are curious about how Machine Learning works in the real world
- Programmers who are looking to add machine learning to their skillset