h264, yuv420p, 1920×1080 | 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
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