Knowledge science methods for professionals and college students – study the idea behind logistic regression and code in Python
- program logistic regression from scratch in Python
- describe how logistic regression is beneficial in knowledge science
- derive the error and replace rule for logistic regression
- perceive how logistic regression works as an analogy for the organic neuron
- use logistic regression to unravel real-world enterprise issues like predicting person actions from e-commerce knowledge and facial features recognition
- perceive why regularization is used in machine studying
- Derivatives, matrix arithmetic, chance
- You need to know some fundamental Python coding with the Numpy Stack
This course is a lead-in to deep studying and neural networks – it covers a well-liked and elementary approach used in machine studying, knowledge science and statistics: logistic regression. We cowl the idea from the bottom up: derivation of the answer, and purposes to real-world issues. We present you ways one may code their very own logistic regression module in Python.
This course doesn’t require any exterior supplies. Every thing wanted (Python, and a few Python libraries) could be obtained at no cost.
This course gives you with many sensible examples as a way to actually see how deep studying can be utilized on something. All through the course, we’ll do a course undertaking, which can present you how you can predict person actions on a web site given person knowledge like whether or not or not that person is on a cell machine, the variety of merchandise they seen, how lengthy they stayed in your website, whether or not or not they’re a returning customer, and what time of day they visited.
One other undertaking on the finish of the course exhibits you ways you should use deep studying for facial features recognition. Think about having the ability to predict somebody’s feelings simply primarily based on an image!
If you’re a programmer and also you need to improve your coding skills by studying about knowledge science, then this course is for you. If in case you have a technical or mathematical background, and also you need use your abilities to make data-driven selections and optimize your corporation utilizing scientific ideas, then this course is for you.
This course focuses on “how to build and understand“, not just “how to use”. Anybody can study to make use of an API in 15 minutes after studying some documentation. It’s not about “remembering facts”, it’s about “seeing for yourself” through experimentation. It’ll educate you how you can visualize what’s taking place in the mannequin internally. If you’d like extra than only a superficial have a look at machine studying fashions, this course is for you.
- calculus (taking derivatives)
- matrix arithmetic
- Python coding: if/else, loops, lists, dicts, units
- Numpy coding: matrix and vector operations, loading a CSV file
TIPS (for getting by means of the course):
- Watch it at 2x.
- Take handwritten notes. This can drastically improve your capability to retain the data.
- Write down the equations. In the event you don’t, I assure it would simply appear to be gibberish.
- Ask numerous questions on the dialogue board. The extra the higher!
- Notice that the majority workouts will take you days or even weeks to finish.
- Write code your self, don’t simply sit there and have a look at my code.
WHAT ORDER SHOULD I TAKE YOUR COURSES IN?:
- Try the lecture “What order should I take your courses in?” (accessible in the Appendix of any of my programs, together with the free Numpy course)
- Grownup learners who need to get into the sphere of information science and massive knowledge
- College students who’re pondering of pursuing machine studying or knowledge science
- College students who’re uninterested in boring conventional statistics and prewritten features in R, and need to find out how issues actually work by implementing them in Python
- Individuals who know some machine studying however need to have the ability to relate it to synthetic intelligence
- People who find themselves in bridging the hole between computational neuroscience and machine studying
Created by Lazy Programmer Inc.
Final up to date 7/2020
English [Auto-generated], Portuguese [Auto-generated], 1 extra
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The publish Deep Learning Conditions: Logistic Regression in Python .
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