Deep Learning & Neural Networks Python Course

Welcome to the world of Deep Learning and Neural Networks! If you are looking to acquire knowledge and skills in this highly in-demand field, then this course is just for you. The Deep Learning & Neural Networks Python Course is an 8-in-1 bundle course that has been designed to provide you with an extensive understanding of the principles of Deep Learning and Neural Networks, and how they are applied in real-world scenarios. This course has been created to help you get started in this exciting field and to provide you with the knowledge and skills you need to succeed.

In this course, you will learn everything you need to know about Deep Learning and Neural Networks. You will start with the basics of Python Programming from Scratch with MySQL Database, and then move on to Keras, the popular open-source neural network library. You will also learn how to work with Convolutional Neural Networks and Artificial Neural Networks. With this comprehensive course, you will be able to develop a strong understanding of the theory behind Deep Learning and Neural Networks, as well as the practical skills needed to apply them in real-world applications.

By the end of this course, you will have developed a solid understanding of the principles of Deep Learning and Neural Networks, and be able to apply this knowledge to a wide range of real-world scenarios. You will have the confidence to build and implement Deep Learning models, and use them to solve complex problems. So why wait? Enrol in the Deep Learning & Neural Networks Python Course today and take your first step towards a rewarding and challenging career in this exciting field!

Learning Outcomes:

• Develop a strong understanding of Python Programming and its applications in Deep Learning and Neural Networks.
• Understand the principles of Keras, an open-source neural network library, and learn how to use it to develop Deep Learning models.
• Learn how to work with Convolutional Neural Networks and Artificial Neural Networks, and apply them to real-world problems.
• Gain experience in working with MySQL Database and learn how to use it to store and manipulate data.
• Understand the principles of Deep Learning Heuristic and learn how to apply them to real-world scenarios.
• Learn how to develop and implement Deep Learning models, and use them to solve complex problems.
• Gain hands-on experience in developing Deep Learning Projects, including Handwritten Digit Recognition Using Neural Network and Convolutional Neural Network.

This Deep Learning & Neural Networks Python Course Bundle consists of the Following Courses:

• Course 01: Deep Learning & Neural Networks Python - Keras
• Course 02: Python Programming from Scratch with My SQL Database
• Course 03: Project on Deep Learning - Artificial Neural Network
• Course 04: Python Programming Bible | Networking, GUI, Email, XML, CGI
• Course 05: Deep Learning Neural Network with R
• Course 06: Essential Training on Deep Learning Heuristic using R
• Course 07: Deep Learning Projects - Convolutional Neural Network
• Course 08: Deep Learning Projects - Handwritten Digit Recognition Using Neural Network

If you want to learn more about Deep Learning & Neural Networks Python Course in-depth, this Deep Learning & Neural Networks Python Course bundle is perfect for you. This package includes many courses that address all critical facets of Deep Learning & Neural Networks Python Course. You will gain the expertise and knowledge of the industry needed to advance your career in Deep Learning & Neural Networks Python Course.

Here is the course curriculum for the primary Deep Learning & Neural Networks Python - Keras Course in this bundle:

• Course Introduction and Table of Contents
• Deep Learning Overview
• Choosing Between ML or DL for the next AI project - Quick Theory Session
• Preparing Your Computer
• Python Basics
• Theano Library Installation and Sample Program to Test
• TensorFlow library Installation and Sample Program to Test
• Keras Installation and Switching Theano and TensorFlow Backends
• Explaining Multi-Layer Perceptron Concepts
• Explaining Neural Networks Steps and Terminology
• First Neural Network with Keras - Understanding Pima Indian Diabetes Dataset
• Explaining Training and Evaluation Concepts
• Pima Indian Model - Steps Explained
• Coding the Pima Indian Model
• Pima Indian Model - Performance Evaluation
• Pima Indian Model - Performance Evaluation - k-fold Validation - Keras
• Pima Indian Model - Performance Evaluation - Hyper Parameters
• Understanding Iris Flower Multi-Class Dataset
• Developing the Iris Flower Multi-Class Model
• Understanding the Sonar Returns Dataset
• Developing the Sonar Returns Model
• Sonar Performance Improvement - Data Preparation - Standardization
• Sonar Performance Improvement - Layer Tuning for Smaller Network
• Sonar Performance Improvement - Layer Tuning for Larger Network
• Understanding the Boston Housing Regression Dataset
• Developing the Boston Housing Baseline Model
• Boston Performance Improvement by Standardization
• Boston Performance Improvement by Deeper Network Tuning
• Boston Performance Improvement by Wider Network Tuning
• Save & Load the Trained Model as JSON File (Pima Indian Dataset)
• Save and Load Model as YAML File - Pima Indian Dataset
• Load and Predict using the Pima Indian Diabetes Model
• Load and Predict using the Iris Flower Multi-Class Model
• Load and Predict using the Sonar Returns Model
• Load and Predict using the Boston Housing Regression Model
• An Introduction to Checkpointing
• Checkpoint Neural Network Model Improvements
• Checkpoint Neural Network Best Model
• Loading the Saved Checkpoint
• Plotting Model Behavior History
• Dropout Regularization - Visible Layer
• Dropout Regularization - Hidden Layer
• Learning Rate Schedule using Ionosphere Dataset - Intro
• Time Based Learning Rate Schedule
• Drop Based Learning Rate Schedule
• Convolutional Neural Networks - Introduction
• MNIST Handwritten Digit Recognition Dataset
• MNIST Multi-Layer Perceptron Model Development
• Convolutional Neural Network Model using MNIST
• Large CNN using MNIST
• Load and Predict using the MNIST CNN Model
• Introduction to Image Augmentation using Keras
• Augmentation using Sample Wise Standardization
• Augmentation using Feature Wise Standardization & ZCA Whitening
• Augmentation using Rotation and Flipping
• Saving Augmentation
• CIFAR-10 Object Recognition Dataset - Understanding and Loading
• Simple CNN using CIFAR-10 Dataset
• Train and Save CIFAR-10 Model
• Load and Predict using CIFAR-10 CNN Model

Assessment

After completing the course, your learning will be assessed by an assignment or multiple-choice based exam. You may choose to participate in a Mock Exam before attending the Final Exam with no extra cost.

• Individuals interested in learning about Deep Learning and Neural Networks
• Software developers looking to expand their knowledge of Deep Learning and Neural Networks
• Data scientists and analysts looking to learn about Deep Learning and Neural Networks
• Graduates looking to start a career in the field of Deep Learning and Neural Networks
• Professionals looking to transition into the field of Deep Learning and Neural Networks

For this Deep Learning & Neural Networks Python Course bundle, you don't need any formal qualifications.

• Deep Learning Engineer: £40,000 - £95,000
• Machine Learning Engineer: £35,000 - £85,000
• Data Scientist: £35,000 - £80,000
• Artificial Intelligence Engineer: £40,000 - £90,000
• Neural Network Developer: £35,000 - £75,000
• Computer Vision Engineer: £45,000 - £100,000

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Certificate of completion

Digital certificate - Included

Certificate of completion

Hard copy certificate - £9.99