Machine Learning, Data Science and Deep Learning with Python (Udemy.com)

Complete hands-on machine learning tutorial with data science, Tensorflow, artificial intelligence, and neural networks

Created by: Sundog Education by Frank Kane

Produced in 2021

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What you will learn

  • Build artificial neural networks with Tensorflow and Keras
  • Classify images, data, and sentiments using deep learning
  • Make predictions using linear regression, polynomial regression, and multivariate regression
  • Data Visualization with MatPlotLib and Seaborn
  • Implement machine learning at massive scale with Apache Spark's MLLib
  • Understand reinforcement learning - and how to build a Pac-Man bot
  • Classify data using K-Means clustering, Support Vector Machines (SVM), KNN, Decision Trees, Naive Bayes, and PCA
  • Use train/test and K-Fold cross validation to choose and tune your models
  • Build a movie recommender system using item-based and user-based collaborative filtering
  • Clean your input data to remove outliers
  • Design and evaluate A/B tests using T-Tests and P-Values

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Quality Score

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Overall Score : 94 / 100

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Course Description

New! Updated for Winter 2019 with extra content on feature engineering, regularization techniques, and tuning neural networks - as well as Tensorflow 2.0 support!
Machine Learning and artificial intelligence (AI) is everywhere; if you want to know how companies like Google, Amazon, and even Udemy extract meaning and insights from massive data sets, this data science course will give you the fundamentals you need. Data Scientists enjoy one of the top-paying jobs, with an average salary of $120,000 according to Glassdoor and Indeed. That's just the average! And it's not just about money - it's interesting work too!
If you've got some programming or scripting experience, this course will teach you the techniques used by real data scientists and machine learning practitioners in the tech industry - and prepare you for a move into this hot career path. This comprehensive machine learning tutorial includes over 100 lectures spanning 14 hours of video, and most topics include hands-on Python code examples you can use for reference and for practice. I'll draw on my 9 years of experience at Amazon and IMDb to guide you through what matters, and what doesn't.
Each concept is introduced in plain English, avoiding confusing mathematical notation and jargon. It's then demonstrated using Python code you can experiment with and build upon, along with notes you can keep for future reference. You won't find academic, deeply mathematical coverage of these algorithms in this course - the focus is on practical understanding and application of them. At the end, you'll be given a final project to apply what you've learned!

The topics in this course come from an analysis of real requirements in data scientist job listings from the biggest tech employers. We'll cover the machine learning, AI, and data mining techniques real employers are looking for, including:
  • Deep Learning / Neural Networks (MLP's, CNN's, RNN's) with TensorFlow and Keras
  • Data Visualization in Python with MatPlotLib and Seaborn
  • Transfer Learning
  • Sentiment analysis
  • Image recognition and classification
  • Regression analysis
  • K-Means Clustering
  • Principal Component Analysis
  • Train/Test and cross validation
  • Bayesian Methods
  • Decision Trees and Random Forests
  • Multiple Regression
  • Multi-Level Models
  • Support Vector Machines
  • Reinforcement Learning
  • Collaborative Filtering
  • K-Nearest Neighbor
  • Bias/Variance Tradeoff
  • Ensemble Learning
  • Term Frequency / Inverse Document Frequency
  • Experimental Design and A/B Tests
  • Feature Engineering
  • Hyperparameter Tuning

...and much more! There's also an entire section on machine learning with Apache Spark, which lets you scale up these techniques to "big data" analyzed on a computing cluster. And you'll also get access to this course's Facebook Group, where you can stay in touch with your classmates.
If you're new to Python, don't worry - the course starts with a crash course. If you've done some programming before, you should pick it up quickly. This course shows you how to get set up on Microsoft Windows-based PC's, Linux desktops, and Macs.
If you're a programmer looking to switch into an exciting new career track, or a data analyst looking to make the transition into the tech industry this course will teach you the basic techniques used by real-world industry data scientists. These are topics any successful technologist absolutely needs to know about, so what are you waiting for? Enroll now!

  • "I started doing your course in 2015... Eventually I got interested and never thought that I will be working for corporate before a friend offered me this job. I am learning a lot which was impossible to learn in academia and enjoying it thoroughly. To me, your course is the one that helped me understand how to work with corporate problems. How to think to be a success in corporate AI research. I find you the most impressive instructor in ML, simple yet convincing." - Kanad Basu, PhD

Who this course is for:
  • Software developers or programmers who want to transition into the lucrative data science and machine learning career path will learn a lot from this course.
  • Technologists curious about how deep learning really works
  • Data analysts in the finance or other non-tech industries who want to transition into the tech industry can use this course to learn how to analyze data using code instead of tools. But, you'll need some prior experience in coding or scripting to be successful.
  • If you have no prior coding or scripting experience, you should NOT take this course - yet. Go take an introductory Python course first.

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Instructor Details

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Sundog Education's mission is to make highly valuable career skills in big data, data science, and machine learning accessible to everyone in the world. Our consortium of expert instructors shares our knowledge in these emerging fields with you, at prices anyone can afford.
Sundog Education is led by Frank Kane and owned by Frank's company, Sundog Software LLC. Frank spent 9 years at Amazon and IMDb, developing and managing the technology that automatically delivers product and movie recommendations to hundreds of millions of customers, all the time. Frank holds 17 issued patents in the fields of distributed computing, data mining, and machine learning. In 2012, Frank left to start his own successful company, Sundog Software, which focuses on virtual reality environment technology, and teaching others about big data analysis.
Due to our volume of students we are unable to respond to private messages; please post your questions within the Q&A of your course. Thanks for understanding.Frank spent 9 years at Amazon and IMDb, developing and managing the technology that automatically delivers product and movie recommendations to hundreds of millions of customers, all the time. Frank holds 17 issued patents in the fields of distributed computing, data mining

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Reviews

4.7

150 total reviews

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By Brian Sparks

Excellent. I feared it would be slower paced, glad its not. Frank also never goes off on tangents, which is the #1 killer of lectures like these.

By Conor Donohoe

Excellent course very well put together would love to see a module on how to deploy a classification script to something like a movies website, besides that loved it

By Prasanna Kumar Rambhatla

Thank you for the wonderful ML,DS and DL course - a lot of insights to learn- I enjoyed the course.

By Jonathan Taylor

This course really helped me get a good idea about how machine learning works and how I can implement some of the neural networks to solve various problems. Using Colaboratory form google as my jupyter notebook was key to me avoiding install issues.

By Reinhard Hannak

Excellent course. At the beginning it appeared as fairly basic and I was close to stopping as it went through fairly basic stuff like covariance, correlation etc, but actually it was not so bad to have a repetition and it turned out to be useful warm up for the actual machine learning stuff that followed later. Many good hands on example and excellent case studies with lots of room to play around with the data. Excellent explanations, right balance of explaining the concepts behind the machine learning techniques and practical hands-on examples showing of how they work in practice, with lots of room to experiment. Excellent introduction into the various techniques, from decision trees, to KNN, to SVM, including how to run them on heavier machines using Apache Spark and even touching on deep learning and neural networks and demonstrating how easy it can be when you use the right models that might already be available . I can highly recommend this course.

By Bernard Freund

It is a very comprehensive course, really good for beginner Data Scientists with strong Python skills

By Dany Joe

A very good course for a Beginner and absolutely loved the lectures. They were quite helpful and it has taught me a lot.

By Karthik Muthuveeramani

Thank you Frank. You have really kept it simple and given some nice ways to handle ML problems. This course, I think, would help me to explore things on my own. Thanks again.

By Koffler

I really appreciate the balanced combination of getting teached the basics, being enabled to get hands-on practice and having seen more sophisticated techniques based on real world samples. Thanks a lot for such valuable education!

Just a little hint for future videos: Displaying the captions at the bottom sometimes interfered with the content of the slides respectively with your entries in python notebook when reaching the bottom of your screen. So it would be better to only use the upper 80% of your screen and reserve the lower 20% for people using the caption option.

By Murali Chowhan

Nice course useful for those who has little bit knowledge of AI, machine learning and deep learning thank you udemy.

By Jürgen Kindler

Good pace. Mostly easy to understand.

Best course I worked through on ML :-)

By Roseta

I really enjoyed the course, Frank is a great instructor and he managed to keep the lectures interesting with plenty of examples to follow. I had a bit of trouble with the more deep down technical stuff but that's no fault of Frank, it's that I haven't really been a hands on programmer for +20 years!