Data Science


Tensorflow and Keras For Neural Networks and Deep Learning

Master the Most Important Deep Learning Frameworks (Tensorflow & Keras) for Python Data Science

4.65 (381 reviews)


7.5 hours


Nov 2020

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 & Keras

Implement Deep Learning Based Unsupervised Learning With Tensorflow and Keras

Implement Deep Learning Based Supervised Learning With Tensorflow & Keras

Implement Convolution Neural Networks With Tensorflow & Keras



It is a full 7-Hour Python Tensorflow & Keras Neural Network & Deep Learning Boot Camp that will help you learn basic machine learning, neural networks and deep learning  using two of the most important Deep Learning frameworks- Tensorflow and Keras.                         


This course is your complete guide to practical machine & deep learning using the Tensorflow & Keras framework in Python..

This means, this course covers the important aspects of Keras and 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 and Keras 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 and Keras is revolutionizing Deep Learning...

By gaining proficiency in Keras and and Tensorflow, you can give your company a competitive edge and boost your career to the next level.


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 & Keras and give you a one-of-a-kind grounding in these frameworks!


• 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 & Keras 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
• The basics of the Keras syntax
• Machine Learning, Supervised Learning, Unsupervised Learning in the Tensorflow & Keras frameworks
• You’ll even discover how to create artificial neural networks and deep learning structures with Tensorflow & Keras


You’ll start by absorbing the most valuable Python Tensorflow and Keras 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 and Keras. 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!



Tensorflow and Keras For Neural Networks and Deep Learning
Tensorflow and Keras For Neural Networks and Deep Learning
Tensorflow and Keras For Neural Networks and Deep Learning
Tensorflow and Keras For Neural Networks and Deep Learning


INTRODUCTION TO THE COURSE: The Key Concepts and Software Tools

Introduction to the Course

Data and Scripts For the Course

Python Data Science Environment

For Mac Users

Introduction to IPython

Written Tensorflow Installation Instructions

Install Keras on Windows 10

Install Keras on Mac

Written Keras Installation Instructions

Introduction to Python Data Science Packages

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 Data from Excel

Basic Data Cleaning

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

Introduction to Keras

What is Keras

Some Preliminary Tensorflow and Keras Applications

Theory of Linear Regression (OLS)

OLS From First Principles

Visualize the Results of OLS

Multiple Regression With Tensorflow-Part 1

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

Softmax Classification With Tensorflow

Some Basic Concepts

What is Machine Learning?

Theory Behind ANN (Artificial Neural Network) and DNN (Deep Neural Networks)

Unsupervised Learning With Tensorflow and Keras

What is Unsupervised Learning?

Autoencoders for Unsupervised Classification

Autoencoders in Tensorflow (Binary Class Problem)

Autoencoders in Tensorflow (Multiple Classes)

Autoencoders in Keras (Sparsity Constraints)

Autoencoders in Keras (Simple)

Deep Autoencoder With Keras

Neural Network for Tensorflow & Keras

Multi Layer Perceptron (MLP) with Tensorflow

Multi Layer Perceptron (MLP) With Keras

Keras MLP For Binary Classification

Keras MLP for Multiclass Classification

Keras MLP for Regression

Deep Learning For Tensorflow & Keras

What is Artificial Intelligence?

Deep Neural Network (DNN) Classifier With Tensorflow

Deep Neural Network (DNN) Classifier With Mixed Predictors

Deep Neural Network (DNN) Regression With Tensorflow

Wide & Deep Learning (Tensorflow)

DNN Classifier With Keras

DNN Classifier With Keras-Example 2

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-Part 1

CNN on Image Data-Part 2

More on TFLearn

CNN Workflow for Keras

CNN With Keras

CNN on Image Data with Keras-Part 1

CNN on Image Data with Keras-Part 2

Autoencoders With Convolution Neural Networks (CNN)

Autoencoders for With CNN- Tensorflow

Autoencoders for With CNN- Keras

Recurrent Neural Networks (RNN)

Theory Behind RNNs

LSTM For Time Series Data

LSTM for Predicting Stock Prices

Miscellaneous Section

Use Colabs for Jupyter Data Science


Anonymized5 September 2020

The course is perfect for my requirements. The lectures are crisp and compact and concepts are well explained.

Nakul21 August 2020

Please do not waste your money on this course. It is not for beginners and the lecturer reads out the code like its a story with zero explanation. Finished the course and i still do not know what keras and tensorflow is.

Eirik4 August 2020

I know a it about Machine Learning and Neural network in advance, and expected to be helped furter on in my learning path. However, the didactics in this course is on a very lo level: If you know stuff in advance, you might follow along fairly good, but if you do not know stuff, it is not explained at all; the instructor just reads aloud what is written in the slides. I am sure that the instructor is very experienced in Neural networks, but alas not very skilled in tutoring.

Ramesh16 July 2020

excellent course. very useful for my work. The contents and their analysis is beyond my expectations. immensely satisfying.

Rifat18 February 2020

This is the best tensorflow course. I would recommend it to not only students but to those too who are working professionally in IT.

Jahed17 February 2020

An awesome practical course that helps me to start creating my first neural networks using keras in such great methods, the instructor is very good at delivering the knowledge she has. I am totally satisfied.

Rumana16 February 2020

This course helped me to understand how TensorFlow can be used to build the neural networks. It is a piece of art. I see how carefully and precise course was build and recorded. Thank you for awesome experience!

Neamul11 December 2019

After finishing the course, It feels I am an expert in Neural network and DL. In lectures, all the topics are explained in details. All the documents are extremely helpful. Found exactly what I was looking for!

Pavel10 December 2019

Easy and resourceful course. Very organized sessions. Neural network and deep earning explained a to z. Thanks a lot!

Maksud10 December 2019

Very good lectures, sessions and resources. Short course but explained almost everything. I would like to prefer this kind of short course rather than long less informative course!

Ishita8 December 2019

The instructor did a great job in the course material. Great combination of theory and practical. This course is great in all aspects.

Faiza8 December 2019

If I know how easy and personal the course would be, I would have enroll way to much early. It's easy and VERY resourceful. Deep knowledge about neural networks!

Clifford26 November 2019

Information presented are very basic without insight or explanation. She is just reading many of the written contents including some from wiki ....etc. I feel like I'm watching her English reading practice.

Mustakim22 November 2019

Very fluent, easy and knowledgeable course. Covered almost everything for neural networks and deep learning. Course contents are very very rich.

Sadia21 November 2019

Impressive knowledgeable course. Lectures are well organized and easy to understand even for such complected topic like Neural networks. Deep learning resources are very useful.


5/12/2019100% OFFExpired


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