In this video, our Data Scientist, Guillem Ballesteros, will be taking you through the Machine Learning Studio of Microsoft Azure.
This tutorial video illustrates how to perform some basic data transformations and time series modeling using R and Microsoft’s Azure Machine Learning. The video complements the Quick Start Guide to R in Azure ML at http://azure.microsoft.com/en-gb/documentation/articles/machine-learning-r-quickstart/ Video Rating: / 5
In this video, you will learn how to use an external python function to run your data through a forecast evaluation. Using Python files uploaded to the cloud environment within the Azure Machine Learning Studio, you can call functions within those files from the Jupyter Notebooks within the same cloud environment.
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1:51 Defining Problem
2:50 Understanding the Data
3:18 Analyzing Data (Trend, Seasonality)
4:40 Traditional Timeseries Forecasting (ARIMA, Prophet)
6:01 Univariate & Multivariate Time series
8:15 Time series with Machine Learning
9:02 Types of Time series models
11:05 Machine Learning Vs. Traditional Time Series
 Math behind Facebook prophet: https://medium.com/future-vision/the-math-of-prophet-46864fa9c55a
 Traditional time series analysis step by step: https://www.kaggle.com/freespirit08/time-series-for-beginners-with-arima
 Dealing with time series data: https://online.stat.psu.edu/stat510/lesson/1
 Catboost is slick : https://catboost.ai/docs/concepts/tutorials.html
Stock Market prediction is an everyday use case of Machine Learning. It allows you to analyze and predict the future values of company stock. In this video, you will learn how to predict stock prices using time series data. We’ll implement machine learning algorithms using specific libraries in Python to predict the prices.
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Watch lecture 1 of Basics Of Fundamental Analysis and learn how to build a mindset of a finance person even if you are not formally educated in finance and commerce. This video focuses on building self-confidence in taking finances in your own hand and make-believe that one can learn Fundamental Analysis. Video Rating: / 5
In this tutorial on Python for Data Science, You will learn about Multiple linear regression Model using Scikit learn and pandas in Python. You will learn about how to check missing data and Correlation.
This is the 30th Video of Python for Data Science Course! In This series I will explain to you Python and Data Science all the time! It is a deep rooted fact, Python is the best programming language for data analysis because of its libraries for manipulating, storing, and gaining understanding from data. Watch this video to learn about the language that make Python the data science powerhouse. Jupyter Notebooks have become very popular in the last few years, and for good reason. They allow you to create and share documents that contain live code, equations, visualizations and markdown text. This can all be run from directly in the browser. It is an essential tool to learn if you are getting started in Data Science, but will also have tons of benefits outside of that field. Harvard Business Review named data scientist „the sexiest job of the 21st century.“ Python pandas is a commonly-used tool in the industry to easily and professionally clean, analyze, and visualize data of varying sizes and types. We’ll learn how to use pandas, Scipy, Sci-kit learn and matplotlib tools to extract meaningful insights and recommendations from real-world datasets.
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Kaggle has released a new competition for 0,000 total prize money. In this series we will work together on this competition from start to finish, learning some of the practical tools of machine learning and data analysis with Python.
In part 1 of this series I will go over the objectives and give you an overview of the competition. Please subscribe to stay tuned for more videos in the future.
You will find the competition here:
https://www.kaggle.com/c/m5-forecasting-accuracy Video Rating: / 5
This is lecture 3 of course 6.S094: Deep Learning for Self-Driving Cars taught in Winter 2017. This lecture introduces computer vision, convolutional neural networks, and end-to-end learning of the driving task.
Links to individual lecture videos for the course:
Lecture 1: Introduction to Deep Learning and Self-Driving Cars
Lecture 2: Deep Reinforcement Learning for Motion Planning
Lecture 3: Convolutional Neural Networks for End-to-End Learning of the Driving Task
Lecture 4: Recurrent Neural Networks for Steering through Time
Lecture 5: Deep Learning for Human-Centered Semi-Autonomous Vehicles
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Hey guys and welcome to another fun and easy machine tutorial on Convolutional Neural Networks.
What are Convolutional Neural Networks and why are they important?
Convolutional Neural Networks (ConvNets or CNNs) are a category of Neural Networks that have proven very effective in areas such as image recognition and classification. ConvNets have been successful in identifying faces, objects and traffic signs apart from powering vision in robots and self-driving cars.
A ConvNet is able to recognize scenes and the system is able to suggest relevant captions for example (“a girl playing tennis”) while this image shows an example of ConvNets being used for recognizing everyday objects, humans and animals. There are also ConvNets involved in playing games like StarCraft, Mario and Doom.
ConvNets, therefore, are an important tool for most deep learning practitioners today. However, understanding ConvNets and learning to use them for the first time can sometimes be a bit daunting. But don’t worry you are in good hands here at Arduino Startups. If you are new to neural networks in general, I would recommend you check out my lecture on Artificial Neural Networks and then return to this one.
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This is the recording of the 1st Cross-Meetup-Group Virtual Event. General slides are found under https://hilpisch.com/virtual_meetup_01.pdf.
Dr. Richard L. Peterson & Anthony Luciani (MarketPsych Indices):
Creating Market Forecasts with News and Social Media Data using Jupyter Notebooks
Dr. Yves Hilpisch (The Python Quants | The AI Machine):
Reinforcement Learning: From Playing Games to Trading Stocks
The event is co-organized by The Python Quants and Refinitiv.
A continuation of the previous Machine Learning example. Here we use our Neural Net code on historical financial timeseries to get a prediction of the future in our target exchange rate. Note that this is just an example of using the Neural Net for timeseries and not an actual predictor of the exchange rate. Video Rating: / 5
Demand forecasting and inventory planning software demonstration