Real data science problems with Python_1

Real data science problems with Python, available at $19.99, has an average rating of 3.8, with 31 lectures, based on 56 reviews, and has 622 subscribers.

You will learn about 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 This course is ideal for individuals who are Intermediate Python users with some knowledge on data science or Students wanting to practice with real datasets or 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 It is particularly useful for Intermediate Python users with some knowledge on data science or Students wanting to practice with real datasets or 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.

Enroll now: Real data science problems with Python

Summary

Title: Real data science problems with Python

Price: $19.99

Average Rating: 3.8

Number of Lectures: 31

Number of Published Lectures: 31

Number of Curriculum Items: 31

Number of Published Curriculum Objects: 31

Original Price: ￡19.99

Quality Status: approved

Status: Live

What You Will 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

Who Should Attend

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

Target Audiences

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

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.

Course Curriculum

Chapter 1: Introduction

Lecture 1: Introduction

Chapter 2: Wines

Lecture 1: Predicting Wine characteristics – Using GridsearchCV

Chapter 3: Doing Machine learning with Audio – Classifying sounds

Lecture 1: Reading WAV files and extracting features

Lecture 2: Classifying words using Adaboost and SVM

Lecture 3: Classifying words using Multilayer Perceptron Deep Neural networks

Chapter 4: Nuclear reactors in the US

Lecture 1: Predicting nuclear output in the US via MLP and SVR

Lecture 2: Multi-output neural networks

Chapter 5: Clustering

Lecture 1: K-Means and PCA on a real dataset containing data for 168 countries

Chapter 6: Used car prices for German Ebay

Lecture 1: Incremental training in Keras

Chapter 7: Identifying poisonous mushrooms

Lecture 1: Poisonous mushrooms detection using Kaggle Data

Lecture 2: Classifying mushrooms using a super GPU on AWS

Chapter 8: Plotting

Lecture 1: Heatmaps: plotting traffic camera revenues in Chicago and Homicides in the US

Chapter 9: Useful image classes

Lecture 1: A class that maps Black&White images to Python objects

Lecture 2: A class that maps RGB Images to Python objects

Chapter 10: Image classification

Lecture 1: Detecting hands in pictures via Convolutional Neural Networks

Lecture 2: Identifying bolts and nuts in images

Lecture 3: Identifying bolts and nuts by calculating polygons

Chapter 11: Working with Video

Lecture 1: Processing video in real time using OpenCV

Lecture 2: Machine learning on real time video

Lecture 3: Following a marker on the screen

Chapter 12: Sentiment analysis and social media

Lecture 1: Sentiment analysis

Lecture 2: Sentiment analysis on self driving cars

Chapter 13: Forecasting

Lecture 1: Intro to time series

Lecture 2: Forecasting the US GDP:Part1

Lecture 3: Forecasting the US GDP: Part2

Lecture 4: Forecasting London property prices

Chapter 14: House Prices in the US

Lecture 1: Predicting real house prices using ExtraTrees

Lecture 2: Estimating contributions in US house prices via regression

Chapter 15: SPAM

Lecture 1: Detecting spam in real SMS data

Chapter 16: Economics

Lecture 1: Predicting whether income exceeds 50K using logistic regression

Lecture 2: Predicting the GDP based on socio-economic variables

Instructors

Francisco Juretig
Mr

Rating Distribution

1 stars: 4 votes

2 stars: 5 votes

3 stars: 13 votes

4 stars: 12 votes

5 stars: 22 votes

Frequently Asked Questions

How long do I have access to the course materials?

You can view and review the lecture materials indefinitely, like an on-demand channel.

Can I take my courses with me wherever I go?

Definitely! If you have an internet connection, courses on Udemy are available on any device at any time. If you don’t have an internet connection, some instructors also let their students download course lectures. That’s up to the instructor though, so make sure you get on their good side!