Course overview
 Provider
 Udemy
 Course type
 Paid course
 Level
 Intermediate
 Duration
 12 hours
 Lessons
 47 lessons
 Certificate
 Available on completion
 Course author
 Francisco Juretig

 Use complex scikitlearn tools for machine learning
 Do statistical analysis using Statsmodels
 Read, transform and manipulate data using Pandas
 Use Keras for neural networks
 Solve both supervised and unsupervised machine learning problems
 Do time series analysis and forecasting using Statsmodels
 Classify images using Deep Convolutional Networks
Description
This course explores several data science and machine learning techniques that every data science practitioner should be familiar with. Fundamentally, the course pivots over four axis:
 Pandas and Matplotlib for working with data
 Keras for Deep Learning,
 Scikitlearn for machine learning
 Statsmodels for statistics
This course explores the fundamental concepts in these big four topics, and provides the student with an overview of the problems that can be solved nowadays.
I only focus on the computational and practical implications of these techniques, and it is assumed that the student is partially familiar with StatisticsMLData Science  or is willing to complement the techniques presented here with theoretical material. Python programming experience will be absolutely necessary, as we only explain how to define Classes in Python (as we will use them along the course)
The teaching strategy is to briefly explain the theory behind these techniques, show how these techniques work in very simple problems, and finally present the student with some real examples. I believe that these real examples add an enormous value to the student, as it helps understand why these techniques are so used nowadays (because they solve real problems!)
Some examples that we will attack here will be: Forecasting the GDP of the United States, forecasting London new houses prices, identifying squares and triangles in pictures, predicting the value of vehicles using online data, detecting spam on SMS data, and many more!
In a nutshell, this course explains how to:
 Define classes for storing data in a better way
 Plotting data
 Merging, pivoting, subsetting, and grouping data via Pandas
 Using linear regression via Statsmodels
 Working with time series/forecasting in Statsmodels
 Several unsupervised machine learning techniques, such as clustering
 Several supervised techniques such as random forests, classification trees, Naive Bayes classifiers, etc
 Define Deep Learning architectures using Keras
 Design different neural networks such as recurrent neural networks, multilayer perceptrons,etc.
 Classify Audio/sounds in a similar way that Alexa, Siri and Cortana do using machine learning
The student needs to be familiar with statistics, Python and some machine learning concepts
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