Innovazione di Prodottto e di Processo con l' Intelligenza Artificiale
Tutto quello che ti serve sapere su come costruire prodotti intelligenti, innovare processi ed investire in A.I.
Watch Promo Acquista- Apprendi quando vuoi| Non ci sono obblighi di presenza in certe date.
- Casi d' uso reali| Extensive Case Based Learning.
- Il contenuto e' curato da specialisti di prodotto della silicon valley.
- Certificato finale, puoi mostrarlo su linkedin.
- All Entry Levels, ideal per managers non tecnici o managers che sono in posizioni ibride tecniche e business.
- Non e' richiesta una conoscenza a priori di machine learning.
- E' richiesta la conoscenza di Inglese scritto di base e la comprensione di termini di business.
- Circa 20 ore di video e circa 15 ore di letture piu' esercizi per un totale di circa 40 ore di sforzo.
- Accesso illimitato [singolo uso no per piu' di una persona].
- Questo corso ti permette n 40 ore di acquisire esperienze reali che ti richiederebbero 18-24 mesi.
- Questo corso contiene anche i corsi di AI e ML per managers
- Questa versione e' in Lingua Inglese sottotitolata in Italiano
When new technology breakthroughs emerge so do opportunities.
Artificial intelligence will change the productivity in business processes as well as the way we carry our daily tasks! At this exciting time, you may want to try something new in your career or within your company taking on an exciting and rewarding job.
You have a lifetime opportunity to build a leading product in your company or improve a process that has a significant impact on your organization’s bottom line. Artificial Intelligence (Ai) and Machine learning (ML) are suited to do exactly that. If you want to lead a new product or process implementation, you do not need to have a PhD in computer science or a Masters in computer science. In fact, you do not even need to be technically educated.
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Course Curriculum
Preliminary Information
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Machine Learning Product Innovation Cycle-L1
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after you enroll
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PreviewIntroduction (1:28)
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StartFactors impacting an artificial intelligence product: an overview (13:44)
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PreviewInterlude: How Managers Should Look at Data and Machine Learning Products (3:14)
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StartProduct Research an introduction Part-1 (13:31)
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StartInterlude: The ingredients of an AI Product Research (2:50)
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StartProduct Research An Introduction Part-2 (7:39)
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StartInterlude: How Managers Should Invest Resources To Create Business Value (3:08)
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StartProduct Research An Introduction Part-3 (12:07)
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StartInterlude: Guidelines to Build AI Products That Work for Users. (2:44)
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StartInterlude: Insights For Successful A.I. Product Development (3:19)
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StartDesign and implement a product incorporating machine intelligence Part-1 (15:43)
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StartInterlude: How to Spot relevant Machine Learning Trends To Use In Your Products (3:25)
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StartDesign And Implement A Product Incorporating Machine Learning Part-2 (17:19)
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StartDesign and implement a product incorporating machine intelligence Part-3 (15:16)
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StartLaunching the new product (8:22)
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StartInterlude: The Complexity Of Real A.I. Projects Implementation : A CTO View Point (3:34)
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StartProduct maintenance and improvement over time (8:15)
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StartView Point: CTO View Point On Machine Learning and A.I. Trends (3:25)
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StartView Point: AI Strategist On The Challenges of Real Implementations (2:36)
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StartSection Slides (4:30)
AI and Machine Learning Foundation for Managers-L2
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after you enroll
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PreviewIntroduction (0:44)
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StartArtificial Intelligence, Machine Learning and the process overview (17:08)
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StartUnderstanding the machine learning process Part-1 (11:20)
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StartUnderstanding the machine learning process-Part2 (12:54)
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StartUnderstanding the machine learning process Part-3 (11:46)
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StartSupervised Learning: Regression Part-1 (7:36)
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StartSupervised Learning: Regression Part-2 (19:08)
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StartSupervised Learning: Introduction to Classification (8:31)
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StartSupervised Learning: Classification with KNN (6:58)
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StartSupervised Learning: Classification: Logistic Regression (14:40)
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StartSupervised Learning: Logistic Regression Advanced Topics (6:07)
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StartSupervised Learning: Classification: Support Vector Machines (14:20)
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StartSupervised Learning: Classification: Decision Tree (15:00)
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StartSupervised Learning: Classification: Random Forest (7:54)
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StartHow to select the proper algorithm to solve a machine learning problem (3:53)
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StartA recipe to select the proper algorithm (3:58)
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PreviewWhy we need the Deep Learning Technology (3:55)
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StartSupervised Learning: Deep Learning Fundamentals (22:13)
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StartBridging Artificial Neural Networks with Deep Neural Networks (4:27)
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StartSupervised Learning: Convolution Neural Networks (22:56)
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StartSupervised Learning: Recurring Neural Networks. (16:25)
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StartUnsupervised Learning: Intro and clustering methods (12:38)
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StartUnsupervised Learning: Principal Component Analysis (13:04)
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StartAdditional Readings
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StartCourse Slides