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English

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Data Science

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Tensorflow Deep Learning - Data Science in Python

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

4.50 (197 reviews)

Students

7 hours

Content

Apr 2019

Last Update
Regular Price


What you will 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


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 learning  using the Tensorflow framework in Python..                         

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.  

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.

THIS IS MY PROMISE TO YOU: COMPLETE THIS ONE COURSE & BECOME A PRO IN PRACTICAL PYTHON TENSORFLOW BASED DATA SCIENCE!

But first things first. My name is Minerva Singh and I am an Oxford University MPhil (Geography and Environment) graduate. I recently finished a PhD at Cambridge University (Tropical Ecology and Conservation).

I have several years of experience in analyzing real life data from different sources  using data science related techniques and producing publications for international peer reviewed journals.

 Over the course of my research I realized almost all the Python data science courses and books out there 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:

• A full introduction to Python Data Science and powerful Python driven framework for data science, Anaconda
• Getting started with Jupyter notebooks for implementing data science techniques in Python
• A comprehensive presentation about Tensorflow installation and a brief introduction to the other Python data science packages
• Brief introduction to the working of Pandas and Numpy
• The basics of the Tensorflow syntax and graphing environment
• Statistical modelling with Tensorflow
• Machine Learning, Supervised Learning, Unsupervised Learning in the Tensorflow framework
• You’ll even discover how to create artificial neural networks and deep learning structures with Tensorflow

BUT,  WAIT! THIS ISN'T JUST ANY OTHER DATA SCIENCE COURSE:

You’ll start by absorbing the most valuable Python Tensorflow Data Science basics and techniques.

I use easy-to-understand, hands-on methods to simplify and address even the most difficult concepts.

My course will 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, you’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  data for your own projects whatever your skill level, and impress your potential employers with actual examples of your data science abilities.

This course will take students without a prior Python and/or statistics background 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 a 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  techniques on real data and interpret the results..

After each video you will learn a new concept or technique which you may apply to your own projects!

JOIN THE COURSE NOW!

#tensorflow #python #deeplearning #android #java #neuralnetwork  #models


Screenshots

Tensorflow Deep Learning - Data Science in Python
Tensorflow Deep Learning - Data Science in Python
Tensorflow Deep Learning - Data Science in Python
Tensorflow Deep Learning - Data Science in Python

Content

INTRODUCTION TO TENSORFLOW : The Key Concepts and Software Tools

Welcome to the World of TensorFlow

Introduction to the Course

Data and Scripts For the Course

What is Artificial Intelligence?

Python Data Science Environment

For Mac Users

Introduction to IPython

IPython in Browser

Install Tensorflow

Written Tensorflow Installation Instructions

Introduction to TensorFlow

A Brief Touchdown

A Brief Touchdown: Computational Graphs

Common Mathematical Operators in Tensorflow

A Tensorflow Session

Interactive Tensorflow Session

Constants and Variables in Tensorflow

Placeholders in Tensorflow

TensorBoard: Visualize Graphs in TensorFlow

Access TensorBoard Graphs

Other Python Packages and Their Interaction with Tensorflow

Miscellaneous Python Packages for Data Science

Introduction to Numpy

Create Numpy Arrays

Numpy Operations

Numpy for Statistical Operation

Introduction to Pandas

Read in Data from CSV

Read in Excel Data

Basic Data Cleaning

Convert to Tensor Objects

Statistical Modelling with Tensorflow

Correlation Analysis

Linear Regression-Theory

Linear Regression (From First Principles) With Tensorflow

Visualize the Results of OLS

Multiple Regression With Tensorflow-Part 1

Multiple Regression With Tensorflow-Machine Learning Approach

Estimate With Tensorflow Estimators

Multiple Regression With Tensorflow Estimators

More on Linear Regressor Estimator

GLM: Generalized Linear Model

Linear Classifier For Binary Classification

Accuracy Assessment For Binary Classification

Linear Classification with Binary Classification With Mixed Predictors

Introduction to Machine Learning

Introduction

What is Machine Learning?

Unsupervised Learning

What is Unsupervised Learning?

K-Means Clustering:Theory

Implement K-Means on Real Data

Supervised Learning

Softmax Classification

Random Forest (RF) for Binary Classification

Random Forest (RF) for Multiclass Classification

kNN- Classification

Artificial Neural Networks and Deep Learning with Tensorflow

Introduction to Artificial Neural Networks (ANN)

Multi Layer Perceptron (MLP)

Deep Neural Network (DNN) Classifier

Deep Neural Network (DNN) Classifier With Mixed Predictors

Deep Neural Network (DNN) Regression

Wide and Deep Learning

Autoencoders Theory

Autoencoders for Credit Card Fraud Detection

Autoencoders for Multiple Classes

Convolution Neural Network (CNN) For Image Analysis

Introduction to CNN

Implement a CNN for Multi-Class Supervised Classification

Activation Functions

More on CNN

Pre-Requisite For Working With Imagery Data

CNN on Image Data

More on TFLearn

Autoencoders with CNN

Miscellaneous Section

Use Colabs for Jupyter Data Science


Reviews

A
Anonymized12 September 2020

I certainly find the course very interesting. Actually I was looking for this kind of information which has been ideally arranged through these lectures.

M
Mark5 August 2020

Too broad and far reaching. Would have appreciated more in depth tensorflow course with less basic data science to begin with.

I
Ishita25 December 2019

Course gives pretty good insight about deep learning. Very well organized session, lectures and lots of material ! Course is exactly what I hoped for!

P
Pavel25 December 2019

Very straightforward and interesting course to learn about Tensorflow Deep Learning Data Science in Python from scratch. The tutorials were very informative. It's very helpful when instructor go through all the codes!

N
Neamul23 December 2019

Fascinating and unique course. All the tough concepts were taught in a very simple way. The that this should be in the introductory course!

H
Hia23 December 2019

Leaned so much about deep learning! Explained in very simple and easy way! Resources and quiz are very informative! Thanks a lot!

F
Faiza21 December 2019

Love it! Excellent sessions and lectures! Codes are impressive! Python is really great with Deep Learning!

M
Maksud21 December 2019

Very detailed course. So many hands-on, practical and comprehensive resource for deep learning. Also includes lots of tips and tricks. Thanks a lot!

T
Topu21 November 2019

Perfect course for data science deep learning. You really make this course easy. Your python codes are such class!

S
Sadia20 November 2019

They way you explain makes deep Learning concepts very intuitive and easily to understandable. Great job!

M
Mustakim20 November 2019

Conceptual and practical combination is delightful. excellent course session. Slow but qualitative for deep data science.

D
Danny16 October 2019

Lectures are scattered and unfocused, can't stand when someone reads directly off of slides, in one lecture about 10% of the screen was missing

A
Alex1 July 2019

The instructor is bad at communicating, and the video editing is very bad as well. It gets in the way of learning, which is unfortunate because if those things were better, the course would be a five star.

V
Vinodh25 April 2019

Trainer just goes through the concept with words instead of explaining how anything happens or how things are derived. Its almost like we have to just accept this step happens without knowing why

C
Choklet8 January 2019

Great thought-out course content by an experienced teacher. I loved learning the like softmax, nonlinear activation functions, backpropagation, numpy vectorization. Minerva is interesting, and makes these subjects a lot of fun.


Coupons

DateDiscountStatus
11/22/201992% OFFExpired
6/28/202181% OFFExpired

1776912

Udemy ID

7/1/2018

Course created date

10/25/2019

Course Indexed date
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