Tensorflow Deep Learning - Data Science in Python

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Minerva Singh

7:20:10

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1 - INTRODUCTION TO TENSORFLOW The Key Concepts and Software Tools

1 - Welcome to the World of TensorFlow.mp4

04:03

2 - Introduction to the Course.mp4

01:47

3 - Data and Scripts For the Course.html

4 - What is Artificial Intelligence.mp4

09:51

5 - Python Data Science Environment.mp4

10:57

6 - For Mac Users.mp4

04:05

7 - Introduction to IPython.mp4

19:13

8 - IPython in Browser.mp4

03:26

9 - Install Tensorflow.mp4

15:12

10 - Written Tensorflow Installation Instructions.html

2 - Introduction to TensorFlow

11 - A Brief Touchdown.mp4

02:36

12 - A Brief Touchdown Computational Graphs.mp4

02:56

13 - Common Mathematical Operators in Tensorflow.html

14 - A Tensorflow Session.mp4

04:37

15 - Interactive Tensorflow Session.mp4

01:38

15 - Lecture-18-interactive-session.txt

15 - lecture-14-interactive.txt

16 - Constants and Variables in Tensorflow.mp4

03:42

16 - Lecture-19-constant-var.txt

17 - Lecture-21-place.txt

17 - Placeholders in Tensorflow.mp4

03:59

18 - TensorBoard Visualize Graphs in TensorFlow.mp4

02:44

19 - Access TensorBoard Graphs.mp4

02:55

3 - Other Python Packages and Their Interaction with Tensorflow

20 - Miscellaneous Python Packages for Data Science.mp4

03:16

21 - Introduction to Numpy.mp4

03:46

22 - Create Numpy Arrays.mp4

10:51

22 - numpy-create.txt

23 - Numpy Operations.mp4

16:48

23 - numpy-op.txt

24 - Numpy for Statistical Operation.mp4

07:23

24 - numpy-stats.txt

25 - Introduction to Pandas.mp4

12:06

26 - Read in Data from CSV.mp4

05:42

26 - Resp2.csv

26 - bostonTxt.txt

26 - read-csv-pd.txt

26 - winequality-red.csv

27 - Read in Excel Data.mp4

05:31

27 - boston1.xls

27 - read-excel-pd.txt

28 - Basic Data Cleaning.mp4

04:30

29 - Convert to Tensor Objects.mp4

06:40

4 - Statistical Modelling with Tensorflow

30 - Correlation Analysis.mp4

08:26

31 - Linear RegressionTheory.mp4

10:44

32 - Linear Regression From First Principles With Tensorflow.mp4

09:22

32 - ols-reg.txt

33 - Lecture-32-Viz.txt

33 - Visualize the Results of OLS.mp4

03:28

34 - Lecture-33-ML-Reg.txt

34 - Multiple Regression With TensorflowPart 1.mp4

05:08

35 - Multiple Regression With TensorflowMachine Learning Approach.mp4

06:12

36 - Estimate With Tensorflow Estimators.mp4

03:05

37 - Multiple Regression With Tensorflow Estimators.mp4

05:24

38 - More on Linear Regressor Estimator.mp4

08:24

38 - linear-regressor-csv-data.txt

38 - listings.csv

39 - GLM Generalized Linear Model.mp4

05:25

40 - Linear Classifier For Binary Classification.mp4

09:33

40 - creditcard.csv

40 - linear-classifier-python.txt

41 - Accuracy Assessment For Binary Classification.mp4

04:19

42 - Linear Classification with Binary Classification With Mixed Predictors.mp4

08:15

42 - Linear-classifier-mixed-pred.txt

42 - titanic.csv

5 - Introduction to Machine Learning

43 - Introduction.mp4

05:36

44 - What is Machine Learning.mp4

05:32

6 - Unsupervised Learning

45 - What is Unsupervised Learning.mp4

01:38

46 - KMeans ClusteringTheory.mp4

01:57

47 - Implement KMeans on Real Data.mp4

05:37

47 - hw-data.txt

47 - kmeans-hw.txt

7 - Supervised Learning

48 - Softmax Classification.mp4

07:35

48 - softmax.txt

49 - Random Forest RF for Binary Classification.mp4

07:09

49 - random-forest-binary.txt

49 - titanic.csv

50 - Random Forest RF for Multiclass Classification.mp4

05:07

51 - kNN Classification.mp4

03:22

51 - knn-class.txt

8 - Artificial Neural Networks and Deep Learning with Tensorflow

52 - Introduction to Artificial Neural Networks ANN.mp4

09:17

53 - Multi Layer Perceptron MLP.mp4

06:24

53 - mlp-mnist.txt

54 - Deep Neural Network DNN Classifier.mp4

06:47

54 - Iris.csv

54 - dnnclass.txt

55 - Deep Neural Network DNN Classifier With Mixed Predictors.mp4

08:11

55 - dnnclass-titanic.txt

55 - titanic.csv

56 - Deep Neural Network DNN Regression.mp4

05:24

56 - dnnreg.txt

57 - Wide and Deep Learning.mp4

11:34

57 - adult.csv

57 - dnn-wide-deep.txt

58 - Autoencoders Theory.mp4

01:46

59 - Autoencoders for Credit Card Fraud Detection.mp4

07:32

59 - autoencoder-binary.txt

59 - creditcard.csv

60 - Autoencoders for Multiple Classes.mp4

05:43

60 - sec8-lect61.txt

9 - Convolution Neural Network CNN For Image Analysis

61 - Introduction to CNN.mp4

11:25

62 - Implement a CNN for MultiClass Supervised Classification.mp4

07:27

62 - cnn1.txt

63 - Activation Functions.mp4

05:50

64 - More on CNN.mp4

04:36

65 - PreRequisite For Working With Imagery Data.mp4

02:33

65 - cnn-image.txt

66 - CNN on Image Data.mp4

10:41

66 - cnn-cat-dog.txt

67 - More on TFLearn.mp4

07:54

67 - sec9-lect68.txt

68 - Autoencoders with CNN.mp4

07:15

68 - sec9-lect69.txt

10 - Miscellaneous Section

69 - Use Colabs for Jupyter Data Science.mp4

07:13

69 - colab.txt

70 - Colab GPU.mp4

05:50

71 - Introduction To Github.mp4

05:16

Description

Tensorflow Deep Learning Python : Tensorflow Neural Network Training : Tensorflow Models - Android Java : Tensorflow C#

What You'll Learn?

Harness The Power Of Anaconda/iPython For Practical Data Science
Learn How To Install & Use Tensorflow Within Anaconda
Implement Statistical & Machine Learning With Tensorflow
Implement Neural Network Modelling With Tensorflow
Implement Deep Learning Based Unsupervised Learning With Tensorflow
Implement Deep Learning Based Supervised Learning With Tensorflow

Who is this for?

People Interested In Learning Python Based Tensorflow For Data Science Applications

People With Prior Exposure To Python Programming &/Or Data Science Concepts

People Interested In Carrying Out Data Science In Jupyter Notebook Environment

People Interested In Implementing Statistical and Machine Learning Models With Tensorflow

People Interested In Implementing Deep Learning Models With Tensorflow

More details

Description
Complete Tensorflow Mastery For Machine Learning & Deep Learning in Python
THIS IS A COMPLETE DATA SCIENCE TRAINING WITH TENSORFLOW IN PYTHON!
It is a full 7-Hour Python Tensorflow Data Science Boot Camp that will help you learn statistical modelling, data visualization, machine learning and basic deep learningÂ using the Tensorflow framework in Python..Â Â Â Â Â Â Â Â Â Â Â Â Â
HERE IS WHY YOU SHOULD ENROLL IN THIS COURSE:
This course is your complete guide to practical data science using the Tensorflow framework in Python..
This means, this course covers all the aspects of practical data science with Tensorflow (Google's powerful Deep Learning framework) and if you take this course, you can do away with taking other courses or buying books on Python Tensorflow based data science. Â
In this age of big data, companies across the globe use Python to sift through the avalanche of information at their disposal and advent of Tensorflow is revolutionizing Deep Learning...
By storing, filtering, managing, and manipulating data in Python and Tensorflow, you can give your company a competitive edge and boost your career to the next level.
THISÂ ISÂ MYÂ PROMISEÂ TOÂ YOU: COMPLETEÂ THISÂ ONEÂ COURSEÂ &Â BECOMEÂ AÂ PROÂ INÂ PRACTICALÂ PYTHONÂ TENSORFLOW BASEDÂ DATAÂ SCIENCE!
But first things first.Â My name isÂ Minerva SinghÂ and IÂ am an Oxford University MPhil (Geography and Environment) graduate. I recently finished aÂ PhD at Cambridge University (Tropical Ecology and Conservation).
I have several yearsÂ ofÂ experience in analyzing real life data from different sources Â using data science related techniques and producing publications for international peer reviewed journals.
Â Over the course of my research I realized almost all the Python data science courses and books out thereÂ do not account for the multidimensional nature of the topic and use data science interchangeably with machine learning..
This gives students an incomplete knowledge of the subject. My course, on the other hand, will give you a robust grounding in all aspects of data science within the Tensorflow framework.
Unlike other Python courses, we dig deep into the statistical modeling features of Tensorflow and give you a one-of-a-kind grounding in Python based Tensorflow Data Science!
DISCOVER 8 COMPLETE SECTIONS ADDRESSING EVERY ASPECT OF PYTHON BASED TENSORFLOW DATA SCIENCE:
â€¢ A full introduction to Python Data Science andÂ powerful Python driven framework for data science, Anaconda
â€¢ Getting started with Jupyter notebooks for implementing data science techniquesÂ in Python
â€¢ A comprehensive presentation about Tensorflow installation and a brief introduction to the other Python data science packages
â€¢ Brief introduction to the working of Pandas and Numpy
â€¢ The basics of the Tensorflow syntax and graphing environment
â€¢ Statistical modelling with Tensorflow
â€¢ Machine Learning, Supervised Learning, Unsupervised Learning in the Tensorflow framework
â€¢ Youâ€™ll even discover how to create artificial neural networks and deep learning structures with Tensorflow
BUT,Â WAIT! THIS ISN'T JUST ANY OTHER DATA SCIENCE COURSE:
Youâ€™ll start by absorbing the most valuable Python Tensorflow Data Science basics and techniques.
I useÂ easy-to-understand, hands-on methods to simplify and address even the most difficult concepts.
My course willÂ help you implement the methods using real data obtained from different sources. Many courses use made-up data that does not empower students to implement Python based data science in real -life.
After taking this course, youâ€™ll easily use packages like Numpy, Pandas, and Matplotlib to work with real data in Python along with gaining fluency in Tensorflow. I will even introduce you to deep learning models such as Convolution Neural network (CNN) !!
The underlying motivation for the course is to ensure you can apply Python based data science on real data into practice today, start analyzing Â data for your own projects whatever your skill level, andÂ impressÂ your potential employers with actual examples of your data science abilities.
This course will take students without a prior Python and/or statistics backgroundÂ background from a basic level to performing some of the most common advanced data science techniques using the powerful Python based Jupyter notebooks
It is aÂ practical, hands-on course, i.e. we will spend some time dealing with some of the theoretical concepts related to data science. However, majority of the course will focus on implementing different Â techniques on real data and interpret the results..
After each video you will learn a new concept or technique which you mayÂ apply to your own projects!
JOIN THE COURSE NOW!
#tensorflow #python #deeplearning #android #java #neuralnetworkÂ #models
Who this course is for:
People Interested In Learning Python Based Tensorflow For Data Science Applications
People With Prior Exposure To Python Programming &/Or Data Science Concepts
People Interested In Carrying Out Data Science In Jupyter Notebook Environment
People Interested In Implementing Statistical and Machine Learning Models With Tensorflow
People Interested In Implementing Deep Learning Models With Tensorflow

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I completed a PhD (University of Cambridge, UK) in 2017 where I focussed on implementing data science techniques for quantifying the impact of forest loss on tropical ecosystems. I hold an MPhil (School of Geography and Environment) and an MSc (Department of Engineering) from Oxford University. I have more than 10 year's experience in conducting academic research (published in high level peer-reviewed international scientific journals such as PLOS One) and advising both non-governmental and industry stakeholders in data science, deep learning and earth observation (EO) related topics. I have a strong track record in implementing machine learning, data visualization, spatial data analysis, deep learning and natural language processing tasks using both R and Python. In addition to being educated at the best universities in the world, I have honed my statistical and data analysis skills through many MOOCs, including The Analytics Edge (R based statistics and machine learning course offered by EdX), Statistical Learning (R-based Machine Learning course offered by Stanford online) and the IBM Data Science Professional certificate Track. I specialise in a variety of topics ranging from deep learning (Tensorflow, Keras) to machine learning to spatial data analysis (including EO data processing), data visualizations, natural language processing, financial analysis among others. I have acted as a peer reviewer on highly regarded academic journals such as Remote Sensing and given guest lectures on prestigious forums such as Open Data Science Conference (ODSC).

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