4.65 (381 reviews)
☑ 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
THIS IS A COMPLETE NEURAL NETWORKS & DEEP LEARNING TRAINING WITH TENSORFLOW & KERAS IN PYTHON!
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.
HERE IS WHY YOU SHOULD ENROLL IN THIS COURSE:
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.
THIS IS MY PROMISE TO YOU: COMPLETE THIS ONE COURSE & BECOME A PRO IN PRACTICAL KERAS & 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 & Keras and give you a one-of-a-kind grounding in these frameworks!
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 & 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
BUT, WAIT! THIS ISN'T JUST ANY OTHER DATA SCIENCE COURSE:
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!
JOIN THE COURSE NOW!
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 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
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
Use Colabs for Jupyter Data Science
The course is perfect for my requirements. The lectures are crisp and compact and concepts are well explained.
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.
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.
excellent course. very useful for my work. The contents and their analysis is beyond my expectations. immensely satisfying.
This is the best tensorflow course. I would recommend it to not only students but to those too who are working professionally in IT.
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.
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!
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!
Easy and resourceful course. Very organized sessions. Neural network and deep earning explained a to z. Thanks a lot!
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!
The instructor did a great job in the course material. Great combination of theory and practical. This course is great in all aspects.
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!
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.
Very fluent, easy and knowledgeable course. Covered almost everything for neural networks and deep learning. Course contents are very very rich.
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.