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Data Science: Supervised Machine Learning in Python

$29.99 $12.00

4.88 (12 reviews)
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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

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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)

Course content

  • Introduction and Review
  • K-Nearest Neighbor
  • Naive Bayes and Bayes Classifiers
  • Decision Trees
  • Perceptrons
  • Practical Machine Learning
  • Building a Machine Learning Web Service
  • Conclusion
  • Setting Up Your Environment (FAQ by Student Request)
  • Extra Help With Python Coding for Beginners (FAQ by Student Request)

12 reviews for Data Science: Supervised Machine Learning in Python

  1. Luis Brito

    I can definitely see this course helping breaking through that initial barrier when it comes to learning a programming language. I’m positive I’ll finish it and hopefully master python by then.

  2. Abdallah Galal Abouhussein

    I would like to share my personnel experience, i`m very pleased and happy for taking this impressive course because Dr. Angela always makes it simple and easy to understand and always makes a precaution section before or after any bug that may appear to some students and also write or record the solution for us, and each word she said is very real and easy to understand.
    Thanks Dr. Angela
    Abdallah Galal

  3. Jiaqing Fan

    Very good course. Dr Angela explains things very clearly. The exercises are interactive and helpful.

    Angela’s voice is like ASMR in most lessons. So you won’t lose focus?

  4. Jaime Sánchez Blanco

    Every concept is very well explained so you can assimilate it, and the teacher insists in practicing daily to get the habit of programming. It really motivates to keep studying and practicing every day.

  5. Bradley Carouthers

    3 days in and I’ve already learned more than other training programs where I’ve put months in. Her teaching style is both efficient and effective.

    Edit: Now on day 29 and the course has held up my expectations and hopes. The rise in complexity is not too sharp, and every day feels like an accomplishment. Angela takes her time to explain things without rushing, yet is able to pack tons of information in nuggets of time.

    Still recommend supplemental exposure for a more wholesome grasp. Though, this should be quite enough

  6. Tommy Prévost

    Very good course.

    PROs: lots of coding exercises, clear explanations, great examples, adapted to 2021’s reality.

    CONs: past the first 30 lessons, many similar coding hands-on.

  7. Quang Tu

    I love all the lectures and challenges of this course. I finally find myself in love with coding. This is definitely a fun course. Thank you Angela for this great course.

  8. Fortunate Eze

    This course is nothing short of AMAZING. The level of love and sincere desire to see students succeed present in this course is a rare sight. This course is fun, it’s challenging, it’s suspense-filled, it makes you feel good about yourself solving those challenges. You don’t take this course and memorise codes, you take it and you know what you’re doing because you literally do it yourself.

    It couldn’t possibly be any more obvious that Angela brought out her whole heart, time, energy and resources to give us a course that if priced based on it’s value, many of us certainly can’t afford. I mean, she kept advising and encouraging us to keep going, because she knows the road could get bumpy, she made a few jokes and used funny animations so we smile along the way keeping it fun. Why some people can’t see this and would rather subscribe to trying to bend courses to fit their own personal preferences is beyond me, but I guess people will always be people.

    Dear Angela, I cannot speak for everyone but I certainly can speak for myself and I’m saying thank you. The efforts you put into making this course outstanding didn’t go unnoticed.

    Let as many of us as appreciate your efforts make you happy and energize you to create more life changing courses for us.

    Cheers.

  9. Chendaniel

    Just the course fits me. Learn by doing, it is easy to start and just follow the flow and at day 15 you realized how to program in Python. Then the magic happen. I am on day 27 and I am looking forward to start a new section everyday.

  10. Caspar Cheng

    Angela is my favourite teacher on Udemy, her courses always come with clear and concise demonstration, make it really easy to understand the content. Meanwhile, she also provide a lot of tips to encourage us moving further on the way of coding. I’m definitely a super fan of her. I had finished her web development boot camp and now I’m learning Python 100 days of code. This course is amazing as she provided one particular project for each day’s learning, which is, really useful and handy. Also, those projects are really interesting and won’t let me feel disappointed about myself. Although some of them are a little bit difficult, they’re still resolvable after some struggling and searching online. I’ll recommend this course for those who really want to learn Python well and write real python projects from scratch!

  11. Jonas Weber

    The course was awesome in the first half! Really helped me to get back to programming and learning new techniques. Also the course got way to much focused on web development in the second half, which I’m not really interested in and is redundand to your dedicated web development course. The lack of videos in the last quarter was rather sad. I really would’ve liked some video explanations for pandas and matplotlib. The challenges and capstone projects were great and challenging!
    All in all I can really recommend this course for beginners and those who want to learn python and/or get back to programming!

  12. Rohit Prasad

    Angela is the best teacher I have ever experienced. The way she explains all the topics and makes use of it in the projects is commendable. Thank you so much Angela for this course.

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