Learn to create machine learning algorithms in Python for students and professionals
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!
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.
Course Content
- Introduction to Machine Learning –> 7 lectures • 10min.
- Simple Linear Regression –> 6 lectures • 11min.
- Multiple Linear Regression –> 8 lectures • 20min.
- Classification Algorithms: K-Nearest Neighbors –> 10 lectures • 14min.
- Classification Algorithms: Decision Tree –> 8 lectures • 13min.
- Classification Algorithms: Logistic regression –> 8 lectures • 13min.
- Clustering –> 15 lectures • 27min.
- Recommender System –> 17 lectures • 25min.
- Conclusion –> 1 lecture • 1min.
Requirements
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!