Last updated 3/2020MP4 | Video: h264, 1280x720 | Audio: AAC, 44.1 KHzLanguage: English | Size: 2.01 GB | Duration: 4h 9m

Master Practical Neural Networks and Deep Learning With the Keras Framework in Python

What you'll learn

Harness The Power Of Anaconda/iPython For Practical Data Science

Learn How To Install & Use Tensorflow Within Anaconda

Implement Neural Network Modelling With Keras

Implement Deep Learning Based Unsupervised Learning With Keras

Implement Deep Learning Based Supervised Learning With Keras

Implement Convolution Neural Networks With Keras

Requirements

Be Able To Operate & Install Software On A Computer

Prior Exposure To Python based Data Science Will Be Beneficial

Prior Exposure To Basic Statistical Concepts & Implementation Will be Useful

Have Prior Exposure To Common Machine Learning Terms such as cross-validation

Description

THIS IS A COMPLETE NEURAL NETWORKS & DEEP LEARNING TRAINING WITH KERAS IN PYTHON!It is a full 7-Hour Python Keras Neural Network & Deep Learning Boot Camp that will help you learn basic machine learning, neural networks and deep learning using one of the most important Deep Learning frameworks: Keras. HERE IS WHY YOU SHOULD ENROLL IN THIS COURSE:This course is your complete guide to practical machine & deep learning using the Keras framework in Python. This means, this course covers the important aspects of Keras if you take this course, you can do away with taking other courses or buying books on Python 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 Keras is revolutionizing Deep Learning.By gaining proficiency in Keras 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 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 modelling 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 KERAS 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 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 Keras syntax Machine Learning, Supervised Learning, Unsupervised Learning in the Keras frameworks You’ll even discover how to create artificial neural networks and deep learning structures with KerasBUT, WAIT! THIS ISN'T JUST ANY OTHER DATA SCIENCE COURSE:You’ll start by absorbing the most valuable Python 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 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 from a basic level to perfog some of the most common advanced data science techniques using the powerful Python-based Jupyter notebooksIt is a practical, hands-on course, i.e. we will spend some dealing with some of the theoretical concepts related to data science. However, the 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!

Overview

Section 1: Welcome To The Course

Lecture 1 What is the Course About?

Lecture 2 Data and Code Used in the Course

Lecture 3 Why AI and Deep Learning?

Lecture 4 Get Started With the Python Data Science Environment: Anaconda

Lecture 5 Anaconda for Mac Users

Lecture 6 The iPython Environment

Section 2: Introduction to Python Data Science Packages

Lecture 7 Python Packages for Data Science

Lecture 8 Introduction to Numpy

Lecture 9 Create Numpy Arrays

Lecture 10 Numpy Operations

Lecture 11 Numpy for Basic Vector Arithmetic

Lecture 12 Numpy for Basic Matrix Arithmetic

Lecture 13 Introduction to Pandas

Lecture 14 Read in Data from CSV

Lecture 15 Read in Data from Excel

Lecture 16 Basic Data Cleaning

Section 3: Introduction to Keras

Lecture 17 What is Keras?

Lecture 18 Keras Installation-Windows

Lecture 19 Keras Installation on Mac OS

Lecture 20 Written Keras Installation Instructions

Section 4: Some Basic Concepts

Lecture 21 What is Machine Learning?

Section 5: Neural Networks With Keras

Lecture 22 Theory Behind ANN (Artificial Neural Network) and DNN (Deep Neural Networks)

Lecture 23 Activation Functions

Lecture 24 Multi Layer Perceptron (MLP) With Keras

Lecture 25 What is Backpropagation?

Lecture 26 Keras MLP For Binary Classification

Lecture 27 Accuracy Assessment For Binary Classification

Lecture 28 Keras MLP for Multiclass Classification

Lecture 29 Keras MLP for Regression

Section 6: Unsupervised Learning With Keras

Lecture 30 What is Unsupervised Learning?

Lecture 31 Autoencoders for Unsupervised Classification

Lecture 32 Autoencoders in Keras (Sparsity Constraints)

Lecture 33 Autoencoders in Keras (Simple)

Lecture 34 Deep Autoencoder With Keras

Section 7: Deep Learning For Tensorflow & Keras

Lecture 35 DNN Classifier With Keras

Lecture 36 DNN Classifier With Keras-Example 2

Section 8: Convolution Neural Network (CNN) For Image Analysis

Lecture 37 Introduction to CNN

Lecture 38 CNN Workflow for Keras

Lecture 39 CNN With Keras

Lecture 40 CNN on Image Data with Keras-Part 1

Lecture 41 CNN on Image Data with Keras-Part 2

Lecture 42 Autoencoders for With CNN- Keras

People Interested In Learning Python Based Tensorflow and Keras For Data Science Applications,People With Prior Exposure To Python Programming &/Or Data Science Concepts,People Interested In Implementing Neural Networks & Deep Learning Models With Keras

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