Section 1 : Introduction

Lecture 1 Course Promotion Video copy 1:52
Lecture 2 A special message for hard of hearing and ESL students 0:58
Lecture 3 Thank you for investing in this Course! 0:45
Lecture 4 Course Overview 3:3
Lecture 5 Secret sauce inside! How to get the most out of this course 5:20
Lecture 7 Course Survey 4:7

Section 2 : Core Concepts

Lecture 8 Core Concepts Overview 1:20
Lecture 9 Computer Science - the `Train Wreck' Definition 0:54
Lecture 10 What's Data I can see data everywhere! 5:34
Lecture 11 Structured vs Unstructured Data 2:45
Lecture 12 Computer Science - Definition Revisited & The Greatest lie ever SOLD 11:3
Lecture 13 What's big data 6:40
Lecture 14 What is Artificial Intelligence (AI) 11:58
Lecture 15 What is Machine Learning - Part 1 - The ideas
Lecture 16 What is Machine Learning - Part 2 - An Example 6:49
Lecture 17 What is data science 5:6
Lecture 18 Recap & How do these relate to each other 3:28

Section 3 : Impacts, Importance and examples

Lecture 19 Impacts, Importance and examples - Overview 0:44
Lecture 20 Why is this important now
Lecture 21 Computers exploding! - The explosive growth of computer power explained 13:24
Lecture 22 What problems does Machine Learning Solve 4:29
Lecture 23 Where it's transforming our lives 9:16

Section 4 : The Machine Learning Process

Lecture 24 The Machine Learning Process - Overview 1:15
Lecture 25 Machine Learning Process Overview 1:54
Lecture 26 Asking the right question 2:49
Lecture 27 Identifying, obtaining, and preparing the right data 11:22
Lecture 28 Identifying and applying a ML Algorithm 11:9
Lecture 29 Evaluating the performance of the model and adjusting 4:5
Lecture 30 Using and presenting the model 1:58

Section 5 : How to apply Machine Learning for Data Science

Lecture 31 How to apply Machine Learning for Data Science - Overview 0:32
Lecture 32 Where to begin your journey 0:58
Lecture 33 Common platforms and tools for Data Science 1:57
Lecture 34 Data Science using - R 1:58
Lecture 35 Data Science using - Python 2:27
Lecture 36 Data Science using SQL 1:50
Lecture 37 Data Science using Excel
Lecture 38 Data Science using RapidMiner 1:11
Lecture 39 Cautionary Tales 1:46

Section 6 : Conclusion

Lecture 40 All done! What's next 0:41

Section 7 : Section 1 -Bonus course - Machine Learning in Python and Jupyter for Beginners

Lecture 41 Introduction and Anaconda Installation 4:52
Lecture 42 What will we cover! 6:13
Lecture 43 Introduction and Setup 8:18

Section 8 : Section 2 -Bonus course - Machine Learning in Python and Jupyter for Beginners

Lecture 44 Crash course in Python - Beginning concepts 6:7
Lecture 45 Crash course in Python - Strings, Slices and Lists! 7:4
Lecture 46 Crash course in Python - Expressions, Operators, Conditions and Loops
Lecture 47 Crash course in Python - Functions, Scope, Dictionaries and more! 5:26

Section 9 : Section 3 - Bonus course - Machine Learning in Python and Jupyter for Beginners

Lecture 48 Hands on Running Python 10:49

Section 10 : Section 4 - Bonus course - Machine Learning in Python and Jupyter for Beginners

Lecture 49 Foundations of Machine Learning and Data Science 5:38
Lecture 50 Foundations of Machine Learning and Data Science 5:9
Lecture 51 Foundations of Machine Learning and Data Science 7:9

Section 11 : Section 5 -Bonus course - Machine Learning in Python and Jupyter for Beginners

Lecture 52 Introducing the essential modules for Machine Learning, and NumPy Basics 7:17
Lecture 53 Pandas and Matplotlib 9:44
Lecture 54 Analysis using Pandas, plotting in Matplotlib 5:25

Section 12 : Section 6 - Bonus course - Machine Learning in Python and Jupyter for Beginners

Lecture 55 A Titanic Example - Getting our start 9:33
Lecture 56 A Titanic Example - Understanding the data set 11:26
Lecture 57 A Titanic Example - Understanding the data set in regards to survival 8:55
Lecture 58 A Titanic Example - Preparing the right data 12:57
Lecture 59 A Titanic Example - Applying regression algorithms
Lecture 60 A Titanic Example - Applying Decision Trees 6:0

Section 13 : Section 7 -Bonus course - Machine Learning in Python and Jupyter for Beginners

Lecture 61 Conclusions - for our Titanic Example, 6:6

Section 14 : Bonus Content

Lecture 62 Bonus Article - The startling breakthrough in Machine Learning from 2016 Pdf