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[/vc_column_text][vc_column_text el_class=”topfeed-embedly”]Resources Exceeded[/vc_column_text][/vc_column][/vc_row][vc_row el_id=”Time-Series-Analysis-in-Python-An-Introduction-“][vc_column width=”1/2”][vc_separator][vc_column_text]

Time Series Analysis in Python: An Introduction –

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  • # Retrieve TSLA data from Quandl – tesla = quandl.get(‘WIKI/TSLA’)# Retrieve the GM data from Quandl – gm = of GM data fromquandlThere is an almost unlimited amount of data on quandl, but I wanted to focus on comparing two companies within the same industry, namely Tesla and General Motors….
  • # The adjusted close accounts for stock splits, so that is what we should graph – plt.plot(gm.index, gm[‘Adj. Close’]) – plt.title(‘GM Stock Price’) – plt.ylabel(‘Price tesla[‘Adj. Close’], ‘r’) – plt.title(‘Tesla Stock Price’) – plt.ylabel(‘Price ($)’); –;Raw StockPricesComparing the two companies on stock prices alone does not show which is…
  • # Yearly average number of shares outstanding for Tesla and GM – tesla_shares = {2018: 168e6, 2017: 162e6, 2016: 144e6, 2015: 128e6, 2014: 125e6, 2013: 119e6, 2012: 107e6, 2011: 100e6, 2010: 51e6}gm_shares = {2018: 1.42e9, 2017: 1.50e9, 2016: 1.54e9, 2015: 1.59e9, 2014: 1.61e9, 2013: 1.39e9, 2012: 1.57e9, 2011: 1.54e9, 2010:1.50e9}#…
  • Do things stay that way over the entire 8)) – plt.plot(cars[‘Date’], cars[‘gm_cap’], ‘b-‘, label = ‘GM’) – plt.plot(cars[‘Date’], cars[‘tesla_cap’], ‘r-‘, label = ‘TESLA’) – plt.xlabel(‘Date’); plt.ylabel(‘Market Cap (Billions $)’); plt.title(‘Market Cap of GM and Tesla’) – plt.legend();Market Capitalization Historical DataWe observe a meteoric rise for Tesla and a minor increase…
  • We then create prophet models and fit them to the data, much like a Scikit-Learn machine learning model: – import fbprophet# Prophet requires columns ds (Date) and y (value) – gm = gm.rename(columns={‘Date’: ‘ds’, ‘cap’: ‘y’})# Put market cap in billions – gm[‘y’] = gm[‘y’] / 1e9# Make the prophet…

[/vc_column_text][vc_column_text el_class=”topfeed-tags”]Tags: data, market cap, time series, tesla, gm[/vc_column_text][/vc_column][vc_column width=”1/2″][vc_separator][vc_column_text el_class=”topfeed-tweet”]

[/vc_column_text][vc_column_text el_class=”topfeed-embedly”]Time Series Analysis in Python: An Introduction – Towards Data Science[/vc_column_text][/vc_column][/vc_row][vc_row el_id=”10-Principles-for-Leading-the-Next-Industrial-Revolution”][vc_column width=”1/2″][vc_separator][vc_column_text]

10 Principles for Leading the Next Industrial Revolution

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  • The leaders of the next industrialrevolution are companies making advances in fields such as robotics, machine learning, digital fabrication (including 3D printing), the Industrial Internet, the Internet of Things (IoT), data analytics and blockchain (a system of decentralized, automated transaction verification).
  • In industry after industry, incumbents that cling to old business models lose ground to upstarts that introduce new products and services at much lower prices.
  • Companies that are slow to change will lose to those that rethink their business models to take advantage of the new platforms and their new opportunities.
  • What the value chain was to the old industrial system, the platform is to the new.
  • GE has announced its goal to be the worlds first digital industrial company; its cloud-based Predix platform combines data analytics, connectivity, cyber-protection, and offerings such as the Digital Twin, a simulation of industrial processes based on digital profiles of more than half a million machines.

[/vc_column_text][vc_column_text el_class=”topfeed-tags”]Tags: Industrial Internet, industrial revolution, company, data, data analytics[/vc_column_text][/vc_column][vc_column width=”1/2″][vc_separator][vc_column_text el_class=”topfeed-tweet”]

[/vc_column_text][vc_column_text el_class=”topfeed-embedly”]10 Principles for Leading the Next Industrial Revolution[/vc_column_text][/vc_column][/vc_row][vc_row el_id=”How-To-Get-Better-Machine-Learning-Performance”][vc_column width=”1/2″][vc_separator][vc_column_text]

How To Get Better Machine Learning Performance

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  • This cheat sheet is designed to give you ideas to lift performance on your machine learning problem.
  • For example, a new framing of your problem or more data is often going to give you more payoff than tuning the parameters of your best performing algorithm.
  • Strategy: Create new and different perspectives on your data in order to best expose the structure of the underlying problem to the learning algorithms.
  • Strategy: Identify the algorithms and data representations that perform above a baseline of performance and better than average.
  • Outcome: You should now have a short list of highly tunedalgorithms on your machine learning problem, maybe even just one.

[/vc_column_text][vc_column_text el_class=”topfeed-tags”]Tags: data, algorithms, algorithm, new data, data representations[/vc_column_text][/vc_column][vc_column width=”1/2″][vc_separator][vc_column_text el_class=”topfeed-tweet”]

[/vc_column_text][vc_column_text el_class=”topfeed-embedly”]Machine Learning Performance Improvement Cheat Sheet[/vc_column_text][/vc_column][/vc_row][vc_row el_id=”-The-best-real-time-news-sites-information-“][vc_column width=”1/2”][vc_separator][vc_column_text]

, The best real-time news sites information.

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  • Landoop Lenses Promises to Ease Application Development for Kafka Streams – The New Stack – – Take a quick gander at Landoop’s GitHub account, and you can easily see the company’s primary focus: making data from the?Apache Kafka?stream processing platform usable by enterprises.
  • The company, founded by CEO Antonios Chalkiopoulos and Chief Product Officer?Christina Daskalaki, grew out of the pair’s multiple years of developing add-ons and tooling around Apache Kafka.

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[/vc_column_text][vc_column_text el_class=”topfeed-embedly”]The New Arms Race in AI[/vc_column_text][/vc_column][/vc_row]