Data Science: Supervised Machine Learning in Python

Full Guide to Implementing Classic Machine Learning Algorithms in Python and with Scikit-Learn

4.63 (3136 reviews)
Udemy
platform
English
language
Data Science
category
22,863
students
6.5 hours
content
Feb 2024
last update
$24.99
regular price

What you will 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

Description

In recent years, we've seen a resurgence in AI, or artificial intelligence, and machine learning.

Machine learning has led to some amazing results, like being able to analyze medical images and predict diseases on-par with human experts.

Google's AlphaGo program was able to beat a world champion in the strategy game go using deep reinforcement learning.

Machine learning is even being used to program self driving cars, which is going to change the automotive industry forever. Imagine a world with drastically reduced car accidents, simply by removing the element of human error.

Google famously announced that they are now "machine learning first", meaning that machine learning is going to get a lot more attention now, and this is what's going to drive innovation in the coming years. It's embedded into all sorts of different products.

Machine learning is used in many industries, like finance, online advertising, medicine, and robotics.

It is a widely applicable tool that will benefit you no matter what industry you're in, and it will also open up a ton of career opportunities once you get good.

Machine learning also raises some philosophical questions. Are we building a machine that can think? What does it mean to be conscious? Will computers one day take over the world?

In this course, we are first going to discuss the K-Nearest Neighbor algorithm. It’s extremely simple and intuitive, and it’s a great first classification algorithm to learn. After we discuss the concepts and implement it in code, we’ll look at some ways in which KNN can fail.

It’s important to know both the advantages and disadvantages of each algorithm we look at.

Next we’ll look at the Naive Bayes Classifier and the General Bayes Classifier. This is a very interesting algorithm to look at because it is grounded in probability.

We’ll see how we can transform the Bayes Classifier into a linear and quadratic classifier to speed up our calculations.

Next we’ll look at the famous Decision Tree algorithm. This is the most complex of the algorithms we’ll study, and most courses you’ll look at won’t implement them. We will, since I believe implementation is good practice.

The last algorithm we’ll look at is the Perceptron algorithm. Perceptrons are the ancestor of neural networks and deep learning, so they are important to study in the context of machine learning.

One we’ve studied these algorithms, we’ll move to more practical machine learning topics. Hyperparameters, cross-validation, feature extraction, feature selection, and multiclass classification.

We’ll do a comparison with deep learning so you understand the pros and cons of each approach.

We’ll discuss the Sci-Kit Learn library, because even though implementing your own algorithms is fun and educational, you should use optimized and well-tested code in your actual work.

We’ll cap things off with a very practical, real-world example by writing a web service that runs a machine learning model and makes predictions. This is something that real companies do and make money from.

All the materials for this course are FREE. You can download and install Python, Numpy, and Scipy with simple commands on Windows, Linux, or Mac.

This course focuses on "how to build and understand", not just "how to use". Anyone can learn to use an API in 15 minutes after reading some documentation. It's not about "remembering facts", it's about "seeing for yourself" via experimentation. It will teach you how to visualize what's happening in the model internally. If you want more than just a superficial look at machine learning models, this course is for you.


"If you can't implement it, you don't understand it"

  • Or as the great physicist Richard Feynman said: "What I cannot create, I do not understand".

  • My courses are the ONLY courses where you will learn how to implement machine learning algorithms from scratch

  • Other courses will teach you how to plug in your data into a library, but do you really need help with 3 lines of code?

  • After doing the same thing with 10 datasets, you realize you didn't learn 10 things. You learned 1 thing, and just repeated the same 3 lines of code 10 times...


Suggested Prerequisites:

  • 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


WHAT ORDER SHOULD I TAKE YOUR COURSES IN?:

  • Check out the lecture "Machine Learning and AI Prerequisite Roadmap" (available in the FAQ of any of my courses, including the free Numpy course)


UNIQUE FEATURES

  • Every line of code explained in detail - email me any time if you disagree

  • No wasted time "typing" on the keyboard like other courses - let's be honest, nobody can really write code worth learning about in just 20 minutes from scratch

  • Not afraid of university-level math - get important details about algorithms that other courses leave out

Content

Introduction and Review

Introduction and Outline
Review of Important Concepts
Where to get the Code and Data
How to Succeed in this Course

K-Nearest Neighbor

K-Nearest Neighbor Intuition
K-Nearest Neighbor Concepts
KNN in Code with MNIST
When KNN Can Fail
KNN for the XOR Problem
KNN for the Donut Problem
Effect of K
KNN Exercise

Naive Bayes and Bayes Classifiers

Bayes Classifier Intuition (Continuous)
Bayes Classifier Intuition (Discrete)
Naive Bayes
Naive Bayes Handwritten Example
Naive Bayes in Code with MNIST
Non-Naive Bayes
Bayes Classifier in Code with MNIST
Linear Discriminant Analysis (LDA) and Quadratic Discriminant Analysis (QDA)
Generative vs Discriminative Models

Decision Trees

Decision Tree Intuition
Decision Tree Basics
Information Entropy
Maximizing Information Gain
Choosing the Best Split
Decision Tree in Code

Perceptrons

Perceptron Concepts
Perceptron in Code
Perceptron for MNIST and XOR
Perceptron Loss Function

Practical Machine Learning

Hyperparameters and Cross-Validation
Feature Extraction and Feature Selection
Comparison to Deep Learning
Multiclass Classification
Sci-Kit Learn
Regression with Sci-Kit Learn is Easy

Building a Machine Learning Web Service

Building a Machine Learning Web Service Concepts
Building a Machine Learning Web Service Code

Conclusion

What’s Next? Support Vector Machines and Ensemble Methods (e.g. Random Forest)

Appendix / FAQ

What is the Appendix?
BONUS: Where to get Udemy coupons and FREE deep learning material
Windows-Focused Environment Setup 2018
How to install Numpy, Scipy, Matplotlib, and Sci-Kit Learn
How to Code by Yourself (part 1)
How to Code by Yourself (part 2)
How to Succeed in this Course (Long Version)
Is this for Beginners or Experts? Academic or Practical? Fast or slow-paced?
Proof that using Jupyter Notebook is the same as not using it
Python 2 vs Python 3
What order should I take your courses in? (part 1)
What order should I take your courses in? (part 2)

Screenshots

Data Science: Supervised Machine Learning in Python - Screenshot_01Data Science: Supervised Machine Learning in Python - Screenshot_02Data Science: Supervised Machine Learning in Python - Screenshot_03Data Science: Supervised Machine Learning in Python - Screenshot_04

Reviews

Ramon
June 13, 2023
Me ha encantado este curso! Muy útil para quien quiera esforzarse y aprender a programar Data Science.
Kelly
March 19, 2023
This is a great course to take after finishing a data science bootcamp. My favorite topic was decision trees, because they were surprisingly complex. I wish it could have covered deep learning too, but I see the instructor has a library of courses on deep learning already, maybe I'm just being greedy. :) I would definitely recommend this course to anyone who is learning machine learning.
Aastha
January 24, 2023
Really detailed course! I have always wanted to learn machine learning and I have bought several courses but the way you explain everything from scratch and use the bottom-up approach, it's great for me.
Rajesh
January 19, 2023
Perfect course for those who want to be experts in machine learning and all topics covered in the course are really well explained! He is an amazing trainer and guide. The illustrations and demos are awesome.
Paedru
January 10, 2023
He is good in explaining the holistic view and maths to a good extent that an average person good in maths and programming can understand
Jianjun
December 21, 2022
coding is not applied level. Hope after we know the rationale, we can use relatively easy code to run data.
Parvez
October 12, 2022
Very clear and concise information. Neat presentation and to the point. I am learning a lot of information about machine learning and getting a great experience implementing the different techniques. Thank you.
Bhavesh
September 21, 2022
Amazing and extensive course. The concepts are clear and the projects and exercises give a big learning experience.
Sohan
August 31, 2022
this course has given me a very solid background in the field of machine learning. Lazy programmer is also a very good tutor
Aditya
May 26, 2022
Course was really good, taught me a lot of new things that I couldn't have learned through other courses, and even better than my college course. I also really enjoyed the instructor's teaching methods were very helpful to gain confidence. I didn't bore through the whole course.
feli
April 22, 2022
no deja guardar videos ademas habla de otros cursos online que la verdad no me importa a mi me interesa el know how no la opinion acerca de otros cursos en linea tampoco como lo dictan las universidades gracias
Sabyasachi
February 10, 2022
thankful to have gotten real skills in my hand in machine learning, thank you very much for the course
Rylee
February 7, 2022
Whether you want to learn a new skill or take on a role as a data scientist, this course is strongly recommended. Lazy Programmer's teaching style is engaging and the course will challenge you in a multitude of ways. It gives you a real understanding of what machine learning is all about.
John
January 5, 2022
I made it through the course and I am very satisfied. A majority of the course goes into detail about how various machine learning algorithms work and you have an opportunity to build them using only simple constructs. The instructor explains each detail very clearly.
Breno
December 15, 2021
Curso muito bom e didático que detalha tanto a parte teórica dos modelos KNN, Naive Bayes, Decision Tree e Perceptron, quanto a parte prática com código em Python. Recomendo para aqueles com algum conhecimento em Numpy e Machine Learning. Ótimo curso para fixar e revisitar conceitos.

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944014
udemy ID
8/29/2016
course created date
10/17/2019
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