Data Science- Supervised Machine Learning in Python

Data Science: Supervised Machine Learning in Python, available at $24.99, has an average rating of 4.65, with 55 lectures, based on 3342 reviews, and has 23949 subscribers.

You will learn about 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 This course is ideal for individuals who are Students and professionals who want to apply machine learning techniques to their datasets or Students and professionals who want to apply machine learning techniques to real world problems or Anyone who wants to learn classic data science and machine learning algorithms or Anyone looking for an introduction to artificial intelligence (AI) It is particularly useful for Students and professionals who want to apply machine learning techniques to their datasets or Students and professionals who want to apply machine learning techniques to real world problems or Anyone who wants to learn classic data science and machine learning algorithms or Anyone looking for an introduction to artificial intelligence (AI).

Enroll now: Data Science: Supervised Machine Learning in Python

Summary

Title: Data Science: Supervised Machine Learning in Python

Price: $24.99

Average Rating: 4.65

Number of Lectures: 55

Number of Published Lectures: 54

Number of Curriculum Items: 55

Number of Published Curriculum Objects: 54

Original Price: $24.99

Quality Status: approved

Status: Live

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

Who Should Attend

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)

Target Audiences

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)

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

Course Curriculum

Chapter 1: Introduction and Review

Lecture 1: Introduction and Outline

Lecture 2: How to Succeed in this Course

Lecture 3: Where to get the Code and Data

Lecture 4: Review of Important Concepts

Chapter 2: K-Nearest Neighbor

Lecture 1: K-Nearest Neighbor Intuition

Lecture 2: K-Nearest Neighbor Concepts

Lecture 3: KNN in Code with MNIST

Lecture 4: When KNN Can Fail

Lecture 5: KNN for the XOR Problem

Lecture 6: KNN for the Donut Problem

Lecture 7: Effect of K

Lecture 8: KNN Exercise

Lecture 9: Suggestion Box

Chapter 3: Naive Bayes and Bayes Classifiers

Lecture 1: Bayes Classifier Intuition (Continuous)

Lecture 2: Bayes Classifier Intuition (Discrete)

Lecture 3: Naive Bayes

Lecture 4: Naive Bayes Handwritten Example

Lecture 5: Naive Bayes in Code with MNIST

Lecture 6: Non-Naive Bayes

Lecture 7: Bayes Classifier in Code with MNIST

Lecture 8: Linear Discriminant Analysis (LDA) and Quadratic Discriminant Analysis (QDA)

Lecture 9: Generative vs Discriminative Models

Chapter 4: Decision Trees

Lecture 1: Decision Tree Intuition

Lecture 2: Decision Tree Basics

Lecture 3: Information Entropy

Lecture 4: Maximizing Information Gain

Lecture 5: Choosing the Best Split

Lecture 6: Decision Tree in Code

Chapter 5: Perceptrons

Lecture 1: Perceptron Concepts

Lecture 2: Perceptron in Code

Lecture 3: Perceptron for MNIST and XOR

Lecture 4: Perceptron Loss Function

Chapter 6: Practical Machine Learning

Lecture 1: Hyperparameters and Cross-Validation

Lecture 2: Feature Extraction and Feature Selection

Lecture 3: Comparison to Deep Learning

Lecture 4: Multiclass Classification

Lecture 5: Sci-Kit Learn

Lecture 6: Regression with Sci-Kit Learn is Easy

Chapter 7: Building a Machine Learning Web Service

Lecture 1: Building a Machine Learning Web Service Concepts

Lecture 2: Building a Machine Learning Web Service Code

Chapter 8: Conclusion

Lecture 1: What’s Next? Support Vector Machines and Ensemble Methods (e.g. Random Forest)

Chapter 9: Setting Up Your Environment (FAQ by Student Request)

Lecture 1: Pre-Installation Check

Lecture 2: Anaconda Environment Setup

Lecture 3: How to install Numpy, Scipy, Matplotlib, and Sci-Kit Learn

Chapter 10: Extra Help With Python Coding for Beginners (FAQ by Student Request)

Lecture 1: How to Code by Yourself (part 1)

Lecture 2: How to Code by Yourself (part 2)

Lecture 3: Proof that using Jupyter Notebook is the same as not using it

Lecture 4: Python 2 vs Python 3

Chapter 11: Effective Learning Strategies for Machine Learning (FAQ by Student Request)

Lecture 1: How to Succeed in this Course (Long Version)

Lecture 2: Is this for Beginners or Experts? Academic or Practical? Fast or slow-paced?

Lecture 3: Machine Learning and AI Prerequisite Roadmap (pt 1)

Lecture 4: Machine Learning and AI Prerequisite Roadmap (pt 2)

Chapter 12: Appendix / FAQ Finale

Lecture 1: What is the Appendix?

Lecture 2: BONUS

Instructors

Lazy Programmer Team
Artificial Intelligence and Machine Learning Engineer

Lazy Programmer Inc.
Artificial intelligence and machine learning engineer

Rating Distribution

1 stars: 25 votes

2 stars: 25 votes

3 stars: 128 votes

4 stars: 1465 votes

5 stars: 1699 votes

Frequently Asked Questions

How long do I have access to the course materials?

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