Africa is home to some of the world's most stunning landscapes. Enjoy this 4k Scenic Relaxation film across Africa's most wild destinations. From the highlands of Ethiopia, to the plains of the Serengeti, Africa is a magical continent waiting to be explored! This is one of my favorite films we've done and I'm so excited to share it with you!

Special thanks to Skypacking for filming a lot of these incredible shots. He traveled from Cape Town to Cairo and documented his entire journey - https://youtu.be/NM_hiIwNKGk

Our other Relaxation films:

Animals of Africa 4K - https://youtu.be/GRde7WGScrM

South America Relaxation Film 4K - https://youtu.be/gghgYaYeG_M

Dolomites Relaxation Film 4K - https://youtu.be/-00PZ3FaHV4

Greece Relaxation Film 4K - https://youtu.be/RSRKFAmfqnI

Sardinia Relaxation Film 4K - https://youtu.be/TIBx3w3loMY

Austria Relaxation Film 4K - https://youtu.be/oHdecbMrcbI

Germany Relaxation Film 4K - https://youtu.be/li-_BLtq58w

Switzerland Relaxation Film 4K - https://youtu.be/kVxTrhojpFI

Italy Relaxation Film 4K - https://youtu.be/2b2gJu-g3qE

Alps Relaxation Film 4K - https://youtu.be/3PZ65s2qLTE

Follow us on instagram @scenicrelaxationfilms

Where we get our music - http://share.mscbd.fm/scenicrelaxation
Great Place for Stock footage - https://bit.ly/38b1EJH
Free stock footage, guides & luts - https://sellfy.com/ryanshirley

Timestamps:

0:00 - Intro
4:11 - Namibia & Egypt
5:35 - Drakensberg & Victoria Falls
6:47 - Tanzania
7:30 - Dallol, Ethiopia
8:36 - Cape Town, South Africa
9:50 - Zanzibar & Seychelles
11:54 - Kenya & Uganda
14:30 - Mauritius & Reunion
15:20 - Morocco
16:48 - Cape Town, South Africa
18:34 - Semien Mountains & Megab
19:51 - Maletsunyane Falls
20:19 - Namibia
20:46 - Victoria Falls, Zimbabwe
21:22 - Tanzania
22:14 - Morocco
26:13 - Zanzibar & Seychelles
28:44 - Uganda
30:13 - Zambia & Wildlife
31:56 - Ghana & Mali
33:26 - Namibia & Cape Town
35:40 - Arusha & Dar es Salaam
37:06 - Ethiopia & Kenya
39:26 - Maasai Mara, Kenya
40:21 - Morocco
42:36 - Tanzania & S. Africa
44:36 - Across Africa
46:53 - Maasai Mara
49:39 - Outro

Thanks for watching :)

We'll be using the numpy module to convert data to numpy arrays, which is what Scikit-learn wants. We will talk more on preprocessing and cross_validation when we get to them in the code, but preprocessing is the module used to do some cleaning/scaling of data prior to machine learning, and cross_ alidation is used in the testing stages. Finally, we're also importing the LinearRegression algorithm as well as svm from Scikit-learn, which we'll be using as our machine learning algorithms to demonstrate results.

At this point, we've got data that we think is useful. How does the actual machine learning thing work? With supervised learning, you have features and labels. The features are the descriptive attributes, and the label is what you're attempting to predict or forecast. Another common example with regression might be to try to predict the dollar value of an insurance policy premium for someone. The company may collect your age, past driving infractions, public criminal record, and your credit score for example. The company will use past customers, taking this data, and feeding in the amount of the "ideal premium" that they think should have been given to that customer, or they will use the one they actually used if they thought it was a profitable amount.

Thus, for training the machine learning classifier, the features are customer attributes, the label is the premium associated with those attributes.

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