PyATL talk about machine learning. Provides both an intro to machine learning and how to do it with Python. Includes simple examples with code and results.
Scikit Learn Tutorial | Machine Learning with Python | Python for Data Scienc...Edureka!
(Python Certification Training for Data Science: https://www.edureka.co/python)
This Edureka video on "Scikit-learn Tutorial" introduces you to machine learning in Python. It will also takes you through regression and clustering techniques along with a demo on SVM classification on the famous iris dataset. This video helps you to learn the below topics:
1. Machine learning Overview
2. Introduction to Scikit-learn
3. Installation of Scikit-learn
4. Regression and Classification
5. Demo
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Scikit-Learn is a powerful machine learning library implemented in Python with numeric and scientific computing powerhouses Numpy, Scipy, and matplotlib for extremely fast analysis of small to medium sized data sets. It is open source, commercially usable and contains many modern machine learning algorithms for classification, regression, clustering, feature extraction, and optimization. For this reason Scikit-Learn is often the first tool in a Data Scientists toolkit for machine learning of incoming data sets.
The purpose of this one day course is to serve as an introduction to Machine Learning with Scikit-Learn. We will explore several clustering, classification, and regression algorithms for a variety of machine learning tasks and learn how to implement these tasks with our data using Scikit-Learn and Python. In particular, we will structure our machine learning models as though we were producing a data product, an actionable model that can be used in larger programs or algorithms; rather than as simply a research or investigation methodology.
A Beginner's Guide to Machine Learning with Scikit-LearnSarah Guido
Given at the PyData NYC 2013 conference (http://vimeo.com/79517341), and will be given at PyTennessee 2014.
Scikit-learn is one of the most well-known machine learning Python modules in existence. But how does it work, and what, for that matter, is machine learning? For those with programming experience but who are new to machine learning, this talk gives a beginner-level overview of how machine learning can be useful, important machine learning concepts, and how to implement them with scikit-learn. We’ll use real world data to look at supervised and unsupervised machine learning algorithms and why scikit-learn is useful for performing these tasks.
Abstract: This PDSG workshop introduces the basics of Python libraries used in machine learning. Libraries covered are Numpy, Pandas and MathlibPlot.
Level: Fundamental
Requirements: One should have some knowledge of programming and some statistics.
Introduction to machine learning. Basics of machine learning. Overview of machine learning. Linear regression. logistic regression. cost function. Gradient descent. sensitivity, specificity. model selection.
Scikit Learn Tutorial | Machine Learning with Python | Python for Data Scienc...Edureka!
(Python Certification Training for Data Science: https://www.edureka.co/python)
This Edureka video on "Scikit-learn Tutorial" introduces you to machine learning in Python. It will also takes you through regression and clustering techniques along with a demo on SVM classification on the famous iris dataset. This video helps you to learn the below topics:
1. Machine learning Overview
2. Introduction to Scikit-learn
3. Installation of Scikit-learn
4. Regression and Classification
5. Demo
Subscribe to our channel to get video updates. Hit the subscribe button and click the bell icon.
Scikit-Learn is a powerful machine learning library implemented in Python with numeric and scientific computing powerhouses Numpy, Scipy, and matplotlib for extremely fast analysis of small to medium sized data sets. It is open source, commercially usable and contains many modern machine learning algorithms for classification, regression, clustering, feature extraction, and optimization. For this reason Scikit-Learn is often the first tool in a Data Scientists toolkit for machine learning of incoming data sets.
The purpose of this one day course is to serve as an introduction to Machine Learning with Scikit-Learn. We will explore several clustering, classification, and regression algorithms for a variety of machine learning tasks and learn how to implement these tasks with our data using Scikit-Learn and Python. In particular, we will structure our machine learning models as though we were producing a data product, an actionable model that can be used in larger programs or algorithms; rather than as simply a research or investigation methodology.
A Beginner's Guide to Machine Learning with Scikit-LearnSarah Guido
Given at the PyData NYC 2013 conference (http://vimeo.com/79517341), and will be given at PyTennessee 2014.
Scikit-learn is one of the most well-known machine learning Python modules in existence. But how does it work, and what, for that matter, is machine learning? For those with programming experience but who are new to machine learning, this talk gives a beginner-level overview of how machine learning can be useful, important machine learning concepts, and how to implement them with scikit-learn. We’ll use real world data to look at supervised and unsupervised machine learning algorithms and why scikit-learn is useful for performing these tasks.
Abstract: This PDSG workshop introduces the basics of Python libraries used in machine learning. Libraries covered are Numpy, Pandas and MathlibPlot.
Level: Fundamental
Requirements: One should have some knowledge of programming and some statistics.
Introduction to machine learning. Basics of machine learning. Overview of machine learning. Linear regression. logistic regression. cost function. Gradient descent. sensitivity, specificity. model selection.
Dimensionality reduction, or dimension reduction, is the transformation of data from a high-dimensional space into a low-dimensional space so that the low-dimensional representation retains some meaningful properties of the original data
Machine Learning in 10 Minutes | What is Machine Learning? | EdurekaEdureka!
YouTube Link: https://youtu.be/qWHi09C3Dq0
** Machine Learning Training with Python: https://www.edureka.co/machine-learning-certification-training**
This Edureka video on 'Machine Learning in 10 Minutes' will help you understand what exactly is Machine Learning and what are the different types of Machine Learning along with some career opportunities that you can achieve through Machine Learning.
Example
What is AI?
What is Machine Learning
Steps for Machine Learning
Types of Machine Learning
Supervised Learning
Unsupervised Learning
Reinforcement Learning
Applications of Machine Learning
What can you be with Machine Learning?
Follow us to never miss an update in the future.
YouTube: https://www.youtube.com/user/edurekaIN
Instagram: https://www.instagram.com/edureka_learning/
Facebook: https://www.facebook.com/edurekaIN/
Twitter: https://twitter.com/edurekain
LinkedIn: https://www.linkedin.com/company/edureka
Castbox: https://castbox.fm/networks/505?country=in
This presentation about Scikit-learn will help you understand what is Scikit-learn, what can we achieve using Scikit-learn and a demo on how to use Scikit-learn in Python. Scikit is a powerful and modern machine learning python library. It's a great tool for fully and semi-automated advanced data analysis and information extraction. There are a lot of reasons why Scikit-Learn is a preferred machine learning tool. It has efficient tools to identify and organize problems, such as whether it fits a supervised or unsupervised learning model. It contains many free and open data sets. It has a rich set of built-in libraries for learning and predicting. It provides model support for every problem type. It also has built-in functions such as pickle for model persistence. It is supported by a huge open source community and vendor base. Now, let us get started and understand Sciki-Learn in detail.
Below topics are explained in this Scikit-Learn presentation:
1. What is Scikit-learn?
2. What we can achieve using Scikit-learn
3. Demo
Simplilearn’s Python Training Course is an all-inclusive program that will introduce you to the Python development language and expose you to the essentials of object-oriented programming, web development with Django and game development. Python has surpassed Java as the top language used to introduce U.S. students to programming and computer science. This course will give you hands-on development experience and prepare you for a career as a professional Python programmer.
What is this course about?
The All-in-One Python course enables you to become a professional Python programmer. Any aspiring programmer can learn Python from the basics and go on to master web development & game development in Python. Gain hands-on experience creating a flappy bird game clone & website functionalities in Python.
What are the course objectives?
By the end of this online Python training course, you will be able to:
1. Internalize the concepts & constructs of Python
2. Learn to create your own Python programs
3. Master Python Django & advanced web development in Python
4. Master PyGame & game development in Python
5. Create a flappy bird game clone
The Python training course is recommended for:
1. Any aspiring programmer can take up this bundle to master Python
2. Any aspiring web developer or game developer can take up this bundle to meet their training needs
Learn more at https://www.simplilearn.com/mobile-and-software-development/python-development-training
The amount of data available to us is growing rapidly, but what is required to make useful conclusions out of it?
Outline
1. Different tactics to gather your data
2. Cleansing, scrubbing, correcting your data
3. Running analysis for your data
4. Bring your data to live with visualizations
5. Publishing your data for rest of us as linked open data
This is the slides for the data science workshop at CDIPS, UC Berkeley on 06-28-2017. It is about general machine learning with a focus on scikit-learn. You can find all the related material: https://github.com/qingkaikong/20170628_ML_sklearn
Tutorial on Scikit Learn I gave at SF Data Mining meetup on May 1st 2017. Review of major parts of the Scikit-Learn API and quick coding exercise on Iris Dataset
Dimensionality reduction, or dimension reduction, is the transformation of data from a high-dimensional space into a low-dimensional space so that the low-dimensional representation retains some meaningful properties of the original data
Machine Learning in 10 Minutes | What is Machine Learning? | EdurekaEdureka!
YouTube Link: https://youtu.be/qWHi09C3Dq0
** Machine Learning Training with Python: https://www.edureka.co/machine-learning-certification-training**
This Edureka video on 'Machine Learning in 10 Minutes' will help you understand what exactly is Machine Learning and what are the different types of Machine Learning along with some career opportunities that you can achieve through Machine Learning.
Example
What is AI?
What is Machine Learning
Steps for Machine Learning
Types of Machine Learning
Supervised Learning
Unsupervised Learning
Reinforcement Learning
Applications of Machine Learning
What can you be with Machine Learning?
Follow us to never miss an update in the future.
YouTube: https://www.youtube.com/user/edurekaIN
Instagram: https://www.instagram.com/edureka_learning/
Facebook: https://www.facebook.com/edurekaIN/
Twitter: https://twitter.com/edurekain
LinkedIn: https://www.linkedin.com/company/edureka
Castbox: https://castbox.fm/networks/505?country=in
This presentation about Scikit-learn will help you understand what is Scikit-learn, what can we achieve using Scikit-learn and a demo on how to use Scikit-learn in Python. Scikit is a powerful and modern machine learning python library. It's a great tool for fully and semi-automated advanced data analysis and information extraction. There are a lot of reasons why Scikit-Learn is a preferred machine learning tool. It has efficient tools to identify and organize problems, such as whether it fits a supervised or unsupervised learning model. It contains many free and open data sets. It has a rich set of built-in libraries for learning and predicting. It provides model support for every problem type. It also has built-in functions such as pickle for model persistence. It is supported by a huge open source community and vendor base. Now, let us get started and understand Sciki-Learn in detail.
Below topics are explained in this Scikit-Learn presentation:
1. What is Scikit-learn?
2. What we can achieve using Scikit-learn
3. Demo
Simplilearn’s Python Training Course is an all-inclusive program that will introduce you to the Python development language and expose you to the essentials of object-oriented programming, web development with Django and game development. Python has surpassed Java as the top language used to introduce U.S. students to programming and computer science. This course will give you hands-on development experience and prepare you for a career as a professional Python programmer.
What is this course about?
The All-in-One Python course enables you to become a professional Python programmer. Any aspiring programmer can learn Python from the basics and go on to master web development & game development in Python. Gain hands-on experience creating a flappy bird game clone & website functionalities in Python.
What are the course objectives?
By the end of this online Python training course, you will be able to:
1. Internalize the concepts & constructs of Python
2. Learn to create your own Python programs
3. Master Python Django & advanced web development in Python
4. Master PyGame & game development in Python
5. Create a flappy bird game clone
The Python training course is recommended for:
1. Any aspiring programmer can take up this bundle to master Python
2. Any aspiring web developer or game developer can take up this bundle to meet their training needs
Learn more at https://www.simplilearn.com/mobile-and-software-development/python-development-training
The amount of data available to us is growing rapidly, but what is required to make useful conclusions out of it?
Outline
1. Different tactics to gather your data
2. Cleansing, scrubbing, correcting your data
3. Running analysis for your data
4. Bring your data to live with visualizations
5. Publishing your data for rest of us as linked open data
This is the slides for the data science workshop at CDIPS, UC Berkeley on 06-28-2017. It is about general machine learning with a focus on scikit-learn. You can find all the related material: https://github.com/qingkaikong/20170628_ML_sklearn
Tutorial on Scikit Learn I gave at SF Data Mining meetup on May 1st 2017. Review of major parts of the Scikit-Learn API and quick coding exercise on Iris Dataset
Data Science and Machine Learning Using Python and Scikit-learnAsim Jalis
Workshop at DataEngConf 2016, on April 7-8 2016, at Galvanize, 44 Tehama Street, San Francisco, CA.
Demo and labs for workshop are at https://github.com/asimjalis/data-science-workshop
Tree models with Scikit-Learn: Great models with little assumptionsGilles Louppe
This talk gives an introduction to tree-based methods, both from a theoretical and practical point of view. It covers decision trees, random forests and boosting estimators, along with concrete examples based on Scikit-Learn about how they work, when they work and why they work.
In this talk by AWeber's Michael Becker, you will get a brief overview of Machine Learning and scikit-learn. This is a scaled down version of this talk from Pycon 2013: http://github.com/jakevdp/sklearn_pycon2013
Realtime predictive analytics using RabbitMQ & scikit-learnAWeber
In this talk, AWeber's Michael Becker describes how to deploy a predictive model in a production environment using RabbitMQ and scikit-learn. You'll see a realtime content classification system to demonstrate this design.
scikit-learn has emerged as one of the most popular open source machine learning toolkits, now widely used in academia and industry.
scikit-learn provides easy-to-use interfaces to perform advanced analysis and build powerful predictive models.
The tutorial will cover basic concepts of machine learning, such as supervised and unsupervised learning, cross validation, and model selection. We will see how to prepare data for machine learning, and go from applying a single algorithm to building a machine learning pipeline.
We will also cover how to build machine learning models on text data, and how to handle very large datasets.
A brief introduction to clustering with Scikit learn. In this presentation, we provide an overview with real examples of how to make use and optimize within k-means clustering.
Machine learning in production with scikit-learnJeff Klukas
Presented at PyOhio 2017: https://pyohio.org/schedule/presentation/284/
The Python data ecosystem provides amazing tools to quickly get up and running with machine learning models, but the path to stably serving them in production is not so clear. We'll discuss details of wrapping a minimal REST API around scikit-learn, training and persisting models in batch, and logging decisions, then compare to some other common approaches to productionizing models.
Personal point of view on scikit-learn: past, present, and future.
This talks gives a bit of history, mentions exciting development, and a personal vision on the future.
Numerical tour in the Python eco-system: Python, NumPy, scikit-learnArnaud Joly
We first present the Python programming language and the NumPy package for scientific computing. Then, we devise a digit recognition system highlighting the scikit-learn package.
Accelerating Random Forests in Scikit-LearnGilles Louppe
Random Forests are without contest one of the most robust, accurate and versatile tools for solving machine learning tasks. Implementing this algorithm properly and efficiently remains however a challenging task involving issues that are easily overlooked if not considered with care. In this talk, we present the Random Forests implementation developed within the Scikit-Learn machine learning library. In particular, we describe the iterative team efforts that led us to gradually improve our codebase and eventually make Scikit-Learn's Random Forests one of the most efficient implementations in the scientific ecosystem, across all libraries and programming languages. Algorithmic and technical optimizations that have made this possible include:
- An efficient formulation of the decision tree algorithm, tailored for Random Forests;
- Cythonization of the tree induction algorithm;
- CPU cache optimizations, through low-level organization of data into contiguous memory blocks;
- Efficient multi-threading through GIL-free routines;
- A dedicated sorting procedure, taking into account the properties of data;
- Shared pre-computations whenever critical.
Overall, we believe that lessons learned from this case study extend to a broad range of scientific applications and may be of interest to anybody doing data analysis in Python.
Scikit-learn for easy machine learning: the vision, the tool, and the projectGael Varoquaux
Scikit-learn is a popular machine learning tool. What can it do for you?Why you you want to use it? What can you do with it? Where is it going?In this talk, I will discuss why and how scikit-learn became popular. Iwill argue that it is successful because of its vision: it fills an important slot in the rich ecosystem of data science. I will demonstrate how scikit-learn makes predictive analysis easy and yet versatile.I will shed some light on our development process: how do we, as a community, ensure the quality and the growth of scikit-learn?
The ABC of Implementing Supervised Machine Learning with Python.pptxRuby Shrestha
It is to our fact that machine learning has taken a significant height. However, knowing and understanding how small problems can be solved from a machine learning perspective is necessary to form a good base, appreciate the process of implementation and get started in this domain. Therefore, in this post, I would like to talk about the ABC of implementing Supervised Machine Learning with Python by navigating through a simple example, which is, adding two numbers. So, to put it in simple terms, I would like to make a machine learn to add. This can be put in other words; I would like to develop a predictive model that can add. Sounds simple, right? View the presentation for more details.
Start machine learning in 5 simple stepsRenjith M P
Simple steps to get started with machine learning.
The use case uses python programming. Target audience is expected to have a very basic python knowledge.
Workshop: Your first machine learning projectAlex Austin
Tutorial to help you create your first machine learning project. The goal was to make this straightforward even someone who's never written a line of code. We gave the workshop to MBA students at UC Berkeley and had a lot of fun learning together - don't be intimidated, anyone can do it!
Jupyter Notebooks for machine learning on Kubernetes & OpenShift | DevNation ...Red Hat Developers
In this session you will see how to take your machine model from development to production by watching the steps involved, which include: 1) Developing a ML model crafted via a Jupyter Notebook directly on top of Kubernetes/OpenShift; 2) Publishing that model as a service to be shared with your team or even the world; and 3) Monitoring the RESTful service via Grafana.
Jose Leiva, data scientist at Ets Asset Management Factory, gives an accurate and simple introduction to Machine Learning. He explains some of the problems that quantitative managers have to get alpha in the markets, and how to face them using Deep Learning.
Learning Predictive Modeling with TSA and KaggleYvonne K. Matos
Ever wanted to do a challenging data science project but feel like you don’t have enough experience? Just go for it! Diving into a 3 TB, 3D image dataset has been my best learning experience. I want to share this deep learning project with you, and tips for overcoming challenges along the way.
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Embracing GenAI - A Strategic ImperativePeter Windle
Artificial Intelligence (AI) technologies such as Generative AI, Image Generators and Large Language Models have had a dramatic impact on teaching, learning and assessment over the past 18 months. The most immediate threat AI posed was to Academic Integrity with Higher Education Institutes (HEIs) focusing their efforts on combating the use of GenAI in assessment. Guidelines were developed for staff and students, policies put in place too. Innovative educators have forged paths in the use of Generative AI for teaching, learning and assessments leading to pockets of transformation springing up across HEIs, often with little or no top-down guidance, support or direction.
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2. Machine Learning (ML):
• Finding patterns in data
• Modeling patterns
• Use models to make
predictions
Slide #2
Intro to Machine Learning with Python
matt@liveramp.com
3. ML can be easy*
• You already have ML applications!
• You can start applying ML methods
now with Python &scikit-learn
• Theoretical knowledge of ML not
needed (initially)*
*Gaining more background, theory, and
experience will help
Slide #3
Intro to Machine Learning with Python
matt@liveramp.com
8. Variance/Bias Trade Off
• Need models that can adapt to
relationships in our data
• Highly adaptable models can over-fit
and will not generalize
• Regularization – Common strategy to
address variance/bias trade off
Slide #8
Intro to Machine Learning with Python
matt@liveramp.com
13. Example: Image Classification
• Classify
handwritten digits
with ML models
• Each input is an
entire image
• Output is digit in
the image
Slide #13
Intro to Machine Learning with Python
matt@liveramp.com
15. import numpyas np
from sklearn.ensembleimport RandomForestClassifier
with np.load(’train.npz') as data:
pixels_train = data['pixels']
labels_train = data['labels’]
with np.load(’test.npz') as data:
pixels_test = data['pixels']
# flatten
X_train = pixels_train.reshape(pixels_train.shape[0], -1)
X_test = pixels_test.reshape(pixels_test.shape[0], -1)
model = RandomForestClassifier(n_estimators=50)
model.fit(X_train, labels_train)
labels_test = model.predict(X_test)
Slide #15
Intro to Machine Learning with Python
matt@liveramp.com
16. Predicting the tags of Stack Overflow
questions with machine learning
Kaggle Data Science Competition
• Given 6 million
training questions
labeled with tags
• Predict the tags for
2 million unlabeled
test questions
www.users.globalnet.co.uk/~slocks/instructions.html
stackoverflow.com/questions/895371/bubble-sort-homework
Slide #16
Intro to Machine Learning with Python
matt@liveramp.com
17. Text Classification Overview
Feature Extraction &
Selection
Raw Posts
Slide #17
Model Selection
& Training
Vector Space
Intro to Machine Learning with Python
Machine
Learning Model
matt@liveramp.com
18. Term Frequency Feature Extraction
Characterize text by the frequency of specific
words in each text entry
Slide #18
processing
sorted
array
faster
“Why is processing a
sorted array faster
than processing an
array this is not
sorted?”
Term Frequencies
why
Example Title:
1
2
2
2
1
Ignore common words
(i.e. stop words)
Intro to Machine Learning with Python
matt@liveramp.com
22. ML can be easy*
• You already have ML problems!
• You can start applying ML methods now
with Python &scikit-learn
• Theoretical knowledge of ML not needed
(initially)*
scikit-learn.org
github.com/scikit-learn
Slide #24
Intro to Machine Learning with Python
matt@liveramp.com
23. Helping companies use their marketing data to delight customers
Tools
Opportunities
• Backend Engineers
• Data Scientists
• Full-Stack Engineers
• Java
• Hadoop (Map/Reduce)
• Ruby
Build and work with large distributed systems that
process massive data sets.
Check out: liveramp.com/careers
Slide #25
Intro to Machine Learning with Python
matt@liveramp.com