Section 1 : Introduction
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Lecture 1 | Course Promotion Video copy | 00:01:52 Duration |
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Lecture 2 | A special message for hard of hearing and ESL students | 00:00:58 Duration |
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Lecture 3 | Thank you for investing in this Course! | 00:00:45 Duration |
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Lecture 4 | Course Overview | 00:03:03 Duration |
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Lecture 5 | Secret sauce inside! How to get the most out of this course | 00:05:20 Duration |
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Lecture 6 | Course Survey | 00:04:07 Duration |
Section 2 : Core Concepts
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Lecture 1 | Core Concepts Overview | 00:01:20 Duration |
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Lecture 2 | Computer Science - the `Train Wreck' Definition | 00:00:54 Duration |
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Lecture 3 | What's Data I can see data everywhere! | 00:05:34 Duration |
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Lecture 4 | Structured vs Unstructured Data | 00:02:45 Duration |
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Lecture 5 | Computer Science - Definition Revisited & The Greatest lie ever SOLD | 00:11:03 Duration |
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Lecture 6 | What's big data | 00:06:40 Duration |
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Lecture 7 | What is Artificial Intelligence (AI) | 00:11:58 Duration |
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Lecture 8 | What is Machine Learning - Part 1 - The ideas | |
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Lecture 9 | What is Machine Learning - Part 2 - An Example | 00:06:49 Duration |
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Lecture 10 | What is data science | 00:05:06 Duration |
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Lecture 11 | Recap & How do these relate to each other | 00:03:28 Duration |
Section 3 : Impacts, Importance and examples
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Lecture 1 | Impacts, Importance and examples - Overview | 00:00:44 Duration |
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Lecture 2 | Why is this important now | |
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Lecture 3 | Computers exploding! - The explosive growth of computer power explained | 00:13:24 Duration |
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Lecture 4 | What problems does Machine Learning Solve | 00:04:29 Duration |
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Lecture 5 | Where it's transforming our lives | 00:09:16 Duration |
Section 4 : The Machine Learning Process
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Lecture 1 | The Machine Learning Process - Overview | 00:01:15 Duration |
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Lecture 2 | Machine Learning Process Overview | 00:01:54 Duration |
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Lecture 3 | Asking the right question | 00:02:49 Duration |
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Lecture 4 | Identifying, obtaining, and preparing the right data | 00:11:22 Duration |
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Lecture 5 | Identifying and applying a ML Algorithm | 00:11:09 Duration |
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Lecture 6 | Evaluating the performance of the model and adjusting | 00:04:05 Duration |
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Lecture 7 | Using and presenting the model | 00:01:58 Duration |
Section 5 : How to apply Machine Learning for Data Science
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Lecture 1 | How to apply Machine Learning for Data Science - Overview | 00:00:32 Duration |
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Lecture 2 | Where to begin your journey | 00:00:58 Duration |
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Lecture 3 | Common platforms and tools for Data Science | 00:01:57 Duration |
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Lecture 4 | Data Science using - R | 00:01:58 Duration |
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Lecture 5 | Data Science using - Python | 00:02:27 Duration |
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Lecture 6 | Data Science using SQL | 00:01:50 Duration |
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Lecture 7 | Data Science using Excel | |
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Lecture 8 | Data Science using RapidMiner | 00:01:11 Duration |
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Lecture 9 | Cautionary Tales | 00:01:46 Duration |
Section 6 : Conclusion
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Lecture 1 | All done! What's next | 00:00:41 Duration |
Section 7 : Section 1 -Bonus course - Machine Learning in Python and Jupyter for Beginners
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Lecture 1 | Introduction and Anaconda Installation | 00:04:52 Duration |
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Lecture 2 | What will we cover! | 00:06:13 Duration |
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Lecture 3 | Introduction and Setup | 00:08:18 Duration |
Section 8 : Section 2 -Bonus course - Machine Learning in Python and Jupyter for Beginners
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Lecture 1 | Crash course in Python - Beginning concepts | 00:06:07 Duration |
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Lecture 2 | Crash course in Python - Strings, Slices and Lists! | 00:07:04 Duration |
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Lecture 3 | Crash course in Python - Expressions, Operators, Conditions and Loops | |
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Lecture 4 | Crash course in Python - Functions, Scope, Dictionaries and more! | 00:05:26 Duration |
Section 9 : Section 3 - Bonus course - Machine Learning in Python and Jupyter for Beginners
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Lecture 1 | Hands on Running Python | 00:10:49 Duration |
Section 10 : Section 4 - Bonus course - Machine Learning in Python and Jupyter for Beginners
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Lecture 1 | Foundations of Machine Learning and Data Science | 00:05:38 Duration |
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Lecture 2 | Foundations of Machine Learning and Data Science | 00:05:09 Duration |
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Lecture 3 | Foundations of Machine Learning and Data Science | 00:07:09 Duration |
Section 11 : Section 5 -Bonus course - Machine Learning in Python and Jupyter for Beginners
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Lecture 1 | Introducing the essential modules for Machine Learning, and NumPy Basics | 00:07:17 Duration |
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Lecture 2 | Pandas and Matplotlib | 00:09:44 Duration |
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Lecture 3 | Analysis using Pandas, plotting in Matplotlib | 00:05:25 Duration |
Section 12 : Section 6 - Bonus course - Machine Learning in Python and Jupyter for Beginners
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Lecture 1 | A Titanic Example - Getting our start | 00:09:33 Duration |
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Lecture 2 | A Titanic Example - Understanding the data set | 00:11:26 Duration |
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Lecture 3 | A Titanic Example - Understanding the data set in regards to survival | 00:08:55 Duration |
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Lecture 4 | A Titanic Example - Preparing the right data | 00:12:57 Duration |
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Lecture 5 | A Titanic Example - Applying regression algorithms | |
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Lecture 6 | A Titanic Example - Applying Decision Trees | 00:06:00 Duration |
Section 13 : Section 7 -Bonus course - Machine Learning in Python and Jupyter for Beginners
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Lecture 1 | Conclusions - for our Titanic Example, | 00:06:06 Duration |
Section 14 : Bonus Content
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Lecture 1 | Bonus Article - The startling breakthrough in Machine Learning from 2016 |