Scientific Python & Deep Learning Masterclass (4 Projects)
- Description
- Curriculum
- FAQ
- Reviews
In this course you will learn how to code with the Python Programming Language and How to use Python for Scientific Work. First we will study Python Basics: Python statements, Python Built-in Types and Built-in Methods,Mathematical Operations. Then You will get familiar with the Scipy and Numpy Libraries who are the two main Scientific Libraries in Python. We will then get into Object Oriented Programming with Python. You will also get in depth knowledge on Deep Learning. We will study the Theory of Neural Networks, Convolutional Neural Networks for Image recognition Problems and more and the programming part of Deep Learning which includes building Neural Networks from scratch and building multiple feature learning algorithms. The class includes 4 Application Projects to apply the Knowledge in concrete real world applications. The 4 Applications are the following: Computing Heat Transfers between 3D objects, Building a Hardware Simulation Framework to simulate a Portable Ultrasound Device, The Real Estate Webscrapper, and Predicting the Survivors of the Titanic using Deep Learning.
The Class includes numerous code examples for the different concepts or algorithms studied. So if you are looking to start a deep learning Project you can simply copy some of this code and build your own Project from this Class.
At the end of this Class you will be able to build Deep Learning Systems and do scientific work using the Python Programming Language like a Master!!
-
5The Assignement StatementVideo lesson
-
6The IF StatementVideo lesson
-
7Loop StatementsVideo lesson
-
8Break StatementVideo lesson
-
9Continue StatementVideo lesson
-
10Try StatementVideo lesson
-
11Pass StatementVideo lesson
-
12Raise StatementVideo lesson
-
13Def StatementVideo lesson
-
14Return StatementVideo lesson
-
15Assert StatementVideo lesson
-
16Yield StatementVideo lesson
-
17IntroductionVideo lesson
-
18Boolean TypeVideo lesson
-
19Bytearray TypeVideo lesson
-
20Bytes TypeVideo lesson
-
21Complex TypeVideo lesson
-
22Dict TypeVideo lesson
-
23Float TypeVideo lesson
-
24Int TypeVideo lesson
-
25Set TypeVideo lesson
-
26Frozenset TypeVideo lesson
-
27List TypeVideo lesson
-
28None TypeVideo lesson
-
29Range TypeVideo lesson
-
30String TypeVideo lesson
-
31TuplesVideo lesson
-
38Numpy ArraysVideo lesson
-
39Sorting Numpy ArraysVideo lesson
-
40Concatenate Numpy ArraysVideo lesson
-
41Shape of Numpy ArraysVideo lesson
-
42Indexing and SlicingVideo lesson
-
43Hstack and VstackVideo lesson
-
44Mathematical Operations on Numpy ArraysVideo lesson
-
45Numpy UniqueVideo lesson
-
46Saving and Loading Numpy ArraysVideo lesson
-
47MatplotlibVideo lesson
-
72MotivationVideo lesson
-
73Feature Learning MethodsVideo lesson
-
74Linear CorrelationVideo lesson
-
75Mutual InformationVideo lesson
-
76Relevance and RedundancyVideo lesson
-
77Greedy Algorithms for Relevance AssessmentVideo lesson
-
78Principal Component AnalysisVideo lesson
-
79Lasso RegressionVideo lesson
External Links May Contain Affiliate Links read more