Udemy
  •  
  •  
  •  
  •  
  •  
  •  
  •  
  •  
  •  
  •  
  •  
  •  
  •  
Development
Web Development Data Science Mobile Development Programming Languages Game Development Database Design & Development Software Testing Software Engineering Development Tools No-Code Development
Business
Entrepreneurship Communications Management Sales Business Strategy Operations Project Management Business Law Business Analytics & Intelligence Human Resources Industry E-Commerce Media Real Estate Other Business
Finance & Accounting
Accounting & Bookkeeping Compliance Cryptocurrency & Blockchain Economics Finance Finance Cert & Exam Prep Financial Modeling & Analysis Investing & Trading Money Management Tools Taxes Other Finance & Accounting
IT & Software
IT Certification Network & Security Hardware Operating Systems Other IT & Software
Office Productivity
Microsoft Apple Google SAP Oracle Other Office Productivity
Personal Development
Personal Transformation Personal Productivity Leadership Career Development Parenting & Relationships Happiness Esoteric Practices Religion & Spirituality Personal Brand Building Creativity Influence Self Esteem & Confidence Stress Management Memory & Study Skills Motivation Other Personal Development
Design
Web Design Graphic Design & Illustration Design Tools User Experience Design Game Design Design Thinking 3D & Animation Fashion Design Architectural Design Interior Design Other Design
Marketing
Digital Marketing Search Engine Optimization Social Media Marketing Branding Marketing Fundamentals Marketing Analytics & Automation Public Relations Advertising Video & Mobile Marketing Content Marketing Growth Hacking Affiliate Marketing Product Marketing Other Marketing
Lifestyle
Arts & Crafts Beauty & Makeup Esoteric Practices Food & Beverage Gaming Home Improvement Pet Care & Training Travel Other Lifestyle
Photography & Video
Digital Photography Photography Portrait Photography Photography Tools Commercial Photography Video Design Other Photography & Video
Health & Fitness
Fitness General Health Sports Nutrition Yoga Mental Health Dieting Self Defense Safety & First Aid Dance Meditation Other Health & Fitness
Music
Instruments Music Production Music Fundamentals Vocal Music Techniques Music Software Other Music
Teaching & Academics
Engineering Humanities Math Science Online Education Social Science Language Teacher Training Test Prep Other Teaching & Academics
AWS Certification Microsoft Certification AWS Certified Solutions Architect - Associate AWS Certified Cloud Practitioner CompTIA A+ Cisco CCNA Amazon AWS CompTIA Security+ AWS Certified Developer - Associate
Photoshop Graphic Design Adobe Illustrator Drawing Digital Painting InDesign Character Design Canva Figure Drawing
Life Coach Training Neuro-Linguistic Programming Mindfulness Personal Development Meditation Personal Transformation Life Purpose Neuroscience Emotional Intelligence
Web Development JavaScript React CSS Angular PHP WordPress Node.Js Python
Google Flutter Android Development iOS Development Swift React Native Dart Programming Language Mobile Development Kotlin SwiftUI
Digital Marketing Google Ads (Adwords) Social Media Marketing Google Ads (AdWords) Certification Marketing Strategy Internet Marketing YouTube Marketing Email Marketing Retargeting
SQL Microsoft Power BI Tableau Business Analysis Business Intelligence MySQL Data Analysis Data Modeling Data Science
Business Fundamentals Entrepreneurship Fundamentals Business Strategy Online Business Business Plan Startup Freelancing Blogging Home Business
Unity Game Development Fundamentals Unreal Engine C# 3D Game Development C++ 2D Game Development Unreal Engine Blueprints Blender
30-Day Money-Back Guarantee
Development Data Science Python

Data Science: Supervised Machine Learning in Python

Full Guide to Implementing Classic Machine Learning Algorithms in Python and with Sci-Kit Learn
Rating: 4.6 out of 54.6 (2,118 ratings)
17,332 students
Created by Lazy Programmer Team, Lazy Programmer Inc.
Last updated 1/2021
English
English [Auto], Spanish [Auto]
30-Day Money-Back Guarantee

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
Curated for the Udemy for Business collection

Course content

12 sections • 53 lectures • 6h 14m total length

  • Preview04:08
  • Preview03:27
  • Where to get the Code and Data
    02:09
  • Anyone Can Succeed in this Course
    12:42

  • K-Nearest Neighbor Intuition
    04:11
  • K-Nearest Neighbor Concepts
    05:02
  • KNN in Code with MNIST
    07:41
  • Preview03:49
  • KNN for the XOR Problem
    02:05
  • KNN for the Donut Problem
    02:36
  • Effect of K
    05:48
  • KNN Exercise
    04:05
  • Suggestion Box
    03:03

  • Bayes Classifier Intuition (Continuous)
    18:16
  • Bayes Classifier Intuition (Discrete)
    10:58
  • Naive Bayes
    09:00
  • Naive Bayes Handwritten Example
    03:28
  • Naive Bayes in Code with MNIST
    05:56
  • Non-Naive Bayes
    04:04
  • Bayes Classifier in Code with MNIST
    02:03
  • Linear Discriminant Analysis (LDA) and Quadratic Discriminant Analysis (QDA)
    06:07
  • Generative vs Discriminative Models
    02:47

  • Decision Tree Intuition
    04:47
  • Decision Tree Basics
    04:58
  • Information Entropy
    03:58
  • Maximizing Information Gain
    07:58
  • Choosing the Best Split
    04:02
  • Decision Tree in Code
    13:10

  • Perceptron Concepts
    07:06
  • Perceptron in Code
    05:26
  • Perceptron for MNIST and XOR
    03:16
  • Perceptron Loss Function
    04:01

  • Hyperparameters and Cross-Validation
    04:15
  • Feature Extraction and Feature Selection
    03:54
  • Comparison to Deep Learning
    04:39
  • Multiclass Classification
    03:20
  • Sci-Kit Learn
    09:02
  • Regression with Sci-Kit Learn is Easy
    05:50

  • Building a Machine Learning Web Service Concepts
    04:10
  • Building a Machine Learning Web Service Code
    06:12

  • What’s Next? Support Vector Machines and Ensemble Methods (e.g. Random Forest)
    02:50

  • Windows-Focused Environment Setup 2018
    20:20
  • How to install Numpy, Scipy, Matplotlib, and Sci-Kit Learn
    17:32

  • How to Code by Yourself (part 1)
    15:54
  • How to Code by Yourself (part 2)
    09:23
  • Proof that using Jupyter Notebook is the same as not using it
    12:29
  • Python 2 vs Python 3
    04:38

Requirements

  • Python, Numpy, and Pandas experience
  • Probability and statistics (Gaussian distribution)
  • Strong ability to write algorithms

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)

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)

Instructors

Lazy Programmer Team
Artificial Intelligence and Machine Learning Engineer
Lazy Programmer Team
  • 4.6 Instructor Rating
  • 40,516 Reviews
  • 147,955 Students
  • 14 Courses

Today, I spend most of my time as an artificial intelligence and machine learning engineer with a focus on deep learning, although I have also been known as a data scientist, big data engineer, and full stack software engineer.

I received my masters degree in computer engineering with a specialization in machine learning and pattern recognition.

Experience includes online advertising and digital media as both a data scientist (optimizing click and conversion rates) and big data engineer (building data processing pipelines). Some big data technologies I frequently use are Hadoop, Pig, Hive, MapReduce, and Spark.

I've created deep learning models to predict click-through rate and user behavior, as well as for image and signal processing and modeling text.

My work in recommendation systems has applied Reinforcement Learning and Collaborative Filtering, and we validated the results using A/B testing.

I have taught undergraduate and graduate students in data science, statistics, machine learning, algorithms, calculus, computer graphics, and physics for students attending universities such as Columbia University, NYU, Hunter College, and The New School.

Multiple businesses have benefitted from my web programming expertise. I do all the backend (server), frontend (HTML/JS/CSS), and operations/deployment work. Some of the technologies I've used are: Python, Ruby/Rails, PHP, Bootstrap, jQuery (Javascript), Backbone, and Angular. For storage/databases I've used MySQL, Postgres, Redis, MongoDB, and more.

Lazy Programmer Inc.
Artificial intelligence and machine learning engineer
Lazy Programmer Inc.
  • 4.6 Instructor Rating
  • 108,181 Reviews
  • 422,558 Students
  • 28 Courses

Today, I spend most of my time as an artificial intelligence and machine learning engineer with a focus on deep learning, although I have also been known as a data scientist, big data engineer, and full stack software engineer.

I received my masters degree in computer engineering with a specialization in machine learning and pattern recognition.

Experience includes online advertising and digital media as both a data scientist (optimizing click and conversion rates) and big data engineer (building data processing pipelines). Some big data technologies I frequently use are Hadoop, Pig, Hive, MapReduce, and Spark.

I've created deep learning models to predict click-through rate and user behavior, as well as for image and signal processing and modeling text.

My work in recommendation systems has applied Reinforcement Learning and Collaborative Filtering, and we validated the results using A/B testing.

I have taught undergraduate and graduate students in data science, statistics, machine learning, algorithms, calculus, computer graphics, and physics for students attending universities such as Columbia University, NYU, Hunter College, and The New School. 

Multiple businesses have benefitted from my web programming expertise. I do all the backend (server), frontend (HTML/JS/CSS), and operations/deployment work. Some of the technologies I've used are: Python, Ruby/Rails, PHP, Bootstrap, jQuery (Javascript), Backbone, and Angular. For storage/databases I've used MySQL, Postgres, Redis, MongoDB, and more.

  • Udemy for Business
  • Teach on Udemy
  • Get the app
  • About us
  • Contact us
  • Careers
  • Blog
  • Help and Support
  • Affiliate
  • Terms
  • Privacy policy
  • Cookie settings
  • Sitemap
  • Featured courses
Udemy
© 2021 Udemy, Inc.