h264, yuv420p, 1920x1080 | ENGLISH, aac, 48000 Hz, 2 channels, s16 | 7h 43 mn | 2.22 GB

Instructor: Francisco Juretig

Practice machine learning and data science with real problems

What you'll learn

Work with many ML techniques in real problems such as classification, image processing, regression

Build neural networks for classification and regression

Apply machine learning and data science to Audio Processing, Image detection, real time video, sentiment analysis and many more things

Requirements

Some experience with Python

General knowledge on Machine Learning, Statistics

Description

This course explores a variety of machine learning and data science techniques using real life datasets/images/audio collected from several sources. These realistic situations are much better than dummy examples, because they force the student to better think the problem, pre-process the data in a better way, and evaluate the performance of the prediction in different ways.

The datasets used here are from different sources such as Kaggle, US Data.gov, CrowdFlower, etc. And each lecture shows how to preprocess the data, model it using an appropriate technique, and compute how well each technique is working on that specific problem. Certain lectures contain also multiple techniques, and we discuss which technique is outperforming the other. Naturally, all the code is shared here, and you can contact me if you have any questions. Every lecture can also be downloaded, so you can enjoy them while travelling.

The student should already be familiar with Python and some data science techniques. In each lecture, we do discuss some technical details on each method, but we do not invest much time in explaining the underlying mathematical principles behind each method

Some of the techniques presented here are:

Pure image processing using OpencCV

Convolutional neural networks using Keras-Theano

Logistic and naive bayes classifiers

Adaboost, Support Vector Machines for regression and classification, Random Forests

Real time video processing, Multilayer Perceptrons, Deep Neural Networks,etc.

Linear regression

Penalized estimators

Clustering

Principal components

The modules/libraries used here are:

Scikit-learn

Keras-theano

Pandas

OpenCV

Some of the real examples used here:

Predicting the GDP based on socio-economic variables

Detecting human parts and gestures in images

Tracking objects in real time video

Machine learning on speech recognition

Detecting spam in SMS messages

Sentiment analysis using Twitter data

Counting objects in pictures and retrieving their position

Forecasting London property prices

Predicting whether people earn more than a 50K threshold based on US Census data

Predicting the nuclear output of US based reactors

Predicting the house prices for some US counties

And much more...

The motivation for this course is that many students willing to learn data science/machine learning are usually suck with dummy datasets that are not challenging enough. This course aims to ease that transition between knowing machine learning, and doing real machine learning on real situations.

Who this course is for:

Intermediate Python users with some knowledge on data science

Students wanting to practice with real datasets

Students who know some machine learning, but want to evaluate scikit-learn and Keras(Theano/Tensorflow) to real problems they will encounter in the analytics industry