Pro data science in Python

Pro data science in Python, available at $19.99, has an average rating of 3.9, with 47 lectures, 4 quizzes, based on 37 reviews, and has 500 subscribers.

You will learn about Use complex scikit-learn 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 This course is ideal for individuals who are Data science beginners, and intermediate users or Statisticians, and CS students wanting to strengthen their data science skills It is particularly useful for Data science beginners, and intermediate users or Statisticians, and CS students wanting to strengthen their data science skills.

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Summary

Title: Pro data science in Python

Price: $19.99

Average Rating: 3.9

Number of Lectures: 47

Number of Quizzes: 4

Number of Published Lectures: 47

Number of Published Quizzes: 4

Number of Curriculum Items: 51

Number of Published Curriculum Objects: 51

Original Price: ￡44.99

Quality Status: approved

Status: Live

What You Will Learn

Use complex scikit-learn 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

Who Should Attend

Data science beginners, and intermediate users

Statisticians, and CS students wanting to strengthen their data science skills

Target Audiences

Data science beginners, and intermediate users

Statisticians, and CS students wanting to strengthen their data science skills

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,?

Scikit-learn 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 Statistics-ML-Data 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, multi-layer 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

Course Curriculum

Chapter 1: Introduction

Lecture 1: Introduction

Chapter 2: Object Oriented programming in Python

Lecture 1: Classes 1

Lecture 2: Classes 2

Chapter 3: Pandas

Lecture 1: Loading data in Pandas

Lecture 2: Looping through Pandas Datasets – Lambda expressions

Lecture 3: Merging data

Lecture 4: Grouping data in Pandas

Lecture 5: Pivoting data in Pandas

Chapter 4: Plotting

Lecture 1: Setting up Matplotlib

Lecture 2: Line plots

Lecture 3: Bar plots

Chapter 5: Linear regression in Statsmodels

Lecture 1: Introduction to linear regression

Lecture 2: Linear Regression: Part1

Lecture 3: Linear regression: Part2

Chapter 6: Time Series in Statsmodels

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 7: Introduction to machine learning

Lecture 1: Introduction to machine learning

Lecture 2: Installing scikit-learn

Chapter 8: Machine learning with Scikit-learn: Supervised problems

Lecture 1: Naive Bayes – Bernoulli – Multinomial

Lecture 2: Detecting spam in SMS

Lecture 3: Linear support Vector machines SVM (SVM and LinearSVC)

Lecture 4: Lasso – Ridge

Lecture 5: Decision Trees

Lecture 6: Introduction to ensemble methods

Lecture 7: Averaging ensemble methods: Part 1: Bagging

Lecture 8: Averaging ensemble methods: Part 2: Random forests

Lecture 9: Boosting ensemble methods

Chapter 9: Machine learning with Scikit-learn: Unsupervised problems

Lecture 1: Principal components

Lecture 2: K-Means

Lecture 3: DBScan

Lecture 4: Clustering and PCA on real countries data from Kaggle

Chapter 10: Processing sound and identifying words in Audio

Lecture 1: Reading WAV files and extracting features

Lecture 2: Classifying word using Adaboost and SVM

Chapter 11: Reading and processing images

Lecture 1: A class that maps BW images to Python objects

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

Chapter 12: Deep Learning with Keras

Lecture 1: Quick Intro to Neural Networks, Theano, and Keras

Lecture 2: Keras – Theano or Tensorflow installation

Lecture 3: Keras Layers

Lecture 4: Multi-Layer Perceptron: Identifying triangles and squares

Lecture 5: Predicting real house prices in the US using deep learning

Lecture 6: The model API: Merging layers and more complex models

Lecture 7: Predicting German car prices using Deep Learning – batch training

Lecture 8: Deep Convolutional Networks: Predicting if hands are closed

Lecture 9: Deep Convolutional Networks: Predicting Nuts and Bolts

Lecture 10: Running high performance code in AWS

Instructors

Francisco Juretig
Mr

Rating Distribution

1 stars: 3 votes

2 stars: 3 votes

3 stars: 7 votes

4 stars: 11 votes

5 stars: 13 votes

Frequently Asked Questions

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

Can I take my courses with me wherever I go?

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