Full Guide to Implementing Classic Machine Learning Algorithms in Python and with Sci-Kit Learn All in This Awesome Data Science Supervised Machine Learning in Python Udemy course.
What you'll learn
- Understand and implement K-Nearest Neighbors in Python
- Understand the limitations of KNN
- User KNN to solve several binary and multiclass classification problems
- Understand and implement Naive Bayes and General Bayes Classifiers in Python
- Understand the limitations of Bayes Classifiers
- Understand and implement a Decision Tree in Python
- Understand and implement the Perceptron in Python
- Understand the limitations of the Perceptron
- Understand hyperparameters and how to apply cross-validation
- Understand the concepts of feature extraction and feature selection
- Understand the pros and cons between classic machine learning methods and deep learning
- Use Sci-Kit Learn
- Implement a machine learning web service
- Python, Numpy, and Pandas experience
- Probability and statistics (Gaussian distribution)
- Strong ability to write algorithms
Machine learning has actually caused some remarkable outcomes, like having the ability to evaluate medical images and anticipate diseases on-par with human professionals.
Google's AlphaGo program had the ability to beat a world champ in the technique video (aff) game go using deep reinforcement learning.
Machine learning is even being used to program self driving vehicles, which is going to alter the automobile industry for good. Envision a world with significantly lowered vehicle mishaps, merely by getting rid of the aspect of human mistake.
Google notoriously revealed that they are now “machine learning initially”, indicating that machine learning is going to get a lot more attention now, and this is what's going to drive development in the coming years. It's ingrained into all sorts of various items.
Machine learning is used in lots of industries, like financing, online marketing, medication, and robotics.
It is an extensively suitable tool that will benefit you no matter what industry you're in, and it will likewise open a lots of profession chances when you get great.
Machine learning likewise raises some philosophical concerns. Are we developing a device that can believe? What does it indicate to be mindful? Will computer systems one day take control of the world?
In this course, we are first going to go over the K-Nearest Neighbor algorithm. It's exceptionally basic and user-friendly, and it's a fantastic very first category algorithm to discover. After we talk about the principles and execute it in code, we'll take a look at some methods which KNN can stop working.
It's essential to understand both the benefits and drawbacks of each algorithm we take a look at.
Next we'll take a look at the Naive Bayes Classifier and the General Bayes Classifier. This is a really fascinating algorithm to take a look at due to the fact that it is grounded in probability.
We'll see how we can change the Bayes Classifier into a direct and quadratic classifier to accelerate our computations.
Next we'll take a look at the well-known Decision Tree algorithm. This is the most intricate of the algorithms we'll study, and many courses you'll take a look at will not execute them. We will, given that I think execution is great practice.
The last algorithm we'll take a look at is the Perceptron algorithm. Perceptrons are the forefather of neural networks and deep learning, so they are necessary to study in the context of machine learning.
One we've studied these algorithms, we'll transfer to more useful maker finding out subjects. Hyperparameters, cross-validation, function extraction, function choice, and multiclass category.
We'll do a contrast with deep learning so you comprehend the advantages and disadvantages of each method.
We'll go over the Sci-Kit Learn library, since despite the fact that executing your own algorithms is enjoyable and instructional, you need to use enhanced and well-tested code in your real work.
We'll top things off with an extremely useful, real-world example by writing a web service that runs a maker discovering model and makes forecasts. This is something that genuine business do and generate income from.
All the products for this course are TOTALLY FREE. You can download and set up Python, Numpy, and Scipy with basic commands on Windows, Linux, or Mac.
This course concentrates on “how to develop and comprehend”, not simply “how to use”. Anybody can discover to use an API in 15 minutes after checking out some documentation. It's not about “keeping in mind truths”, it's about “seeing on your own” through experimentation. It will teach you how to envision what's taking place in the model internally. If you desire more than simply a superficial take a look at machine learning models, this course is for you.
- calculus (for some parts).
- probability (continuous and discrete distributions, joint, marginal, conditional, PDF, PMF, CDF, Bayes rule).
- Python coding: if/else, loops, lists, dicts, sets.
- Numpy, Scipy, Matplotlib.
TIPS (for making it through the course):.
- Watch it at 2x.
- Take handwritten notes. This will significantly increase your capability to keep the info.
- Make a note of the formulas. If you do not, I ensure it will simply appear like gibberish.
- Ask great deals of concerns on the conversation board. The more the much better!
- Understand that a lot of workouts will take you days or weeks to finish.
- Write code yourself, do not simply sit there and take a look at my code.
WHAT ORDER SHOULD I TAKE YOUR COURSES IN?:.
- Check out the lecture “What order should I take your courses in?” (offered in the Appendix of any of my courses, consisting of the totally free Numpy course).
Who this course is for:
- Students and professionals who want to apply machine learning techniques to their datasets
- Students and professionals who want to apply machine learning techniques to real world problems
- Anyone who wants to learn classic data science and machine learning algorithms
- Anyone looking for an introduction to artificial intelligence (AI)
Created by Lazy Programmer Inc.
Last updated 10/2018
Size: 1004.51 MB