Artificial neural networks (ANNs) are the most powerful machine learning algorithms available today. They are capable of learning complex relationships in data, and they have been used to achieve state-of-the-art results in a wide variety of fields, including image recognition, natural language processing, and speech recognition. The Future of Machine Learning is Here! This Project on Deep Learning - Artificial Neural Network course will teach you how to build and train ANNs from scratch. You will learn about the different components of an ANN, such as the input layer, hidden layers, and output layer. You will also learn about the different activation functions that can be used in ANNs, and you will see how to optimise ANNs for different tasks. In addition to the theoretical concepts, you will also get experience with ANNs. You will work on a project where you will build an ANN to classify images. You will use the TensorFlow library to build your ANN, and you will see how to train your ANN on a dataset of images. By the end of this Project on Deep Learning - Artificial Neural Network course, you will have a deep understanding of ANNs and how to use them. You will be able to build your own ANNs to solve a variety of problems. You will also be able to use the TensorFlow library to build and train ANNs. So what are you waiting for? Enrol in this course today and start learning about the future of machine learning! Learning Outcomes: Through this comprehensive course, you should be able to: Understand the fundamental concepts of deep learning and artificial neural networks. Install and configure an artificial neural network framework. Preprocess and structure data for optimal model performance. Encode data effectively for neural network training and predictions. Build and deploy artificial neural networks for real-world applications. Address data imbalance challenges and optimise model accuracy. Who is this course for? This Project on Deep Learning - Artificial Neural Network course is ideal for: Data scientists and machine learning practitioners seeking to expand their knowledge. Software engineers interested in leveraging deep learning techniques. Students pursuing a career in artificial intelligence and machine learning. Professionals looking to enhance their skills in neural network development. Individuals with a passion for exploring advanced machine learning techniques. Career Path Our course will prepare you for a range of careers, including: Deep Learning Engineer: £40,000 - £100,000 per year. Machine Learning Researcher: £45,000 - £120,000 per year. Data Scientist: £50,000 - £110,000 per year. Artificial Intelligence Specialist: £55,000 - £130,000 per year. Software Engineer (specialising in AI): £45,000 - £100,000 per year. Research Scientist (Machine Learning): £50,000 - £120,000 per year. AI Consultant: £60,000 - £150,000 per year. Certification After studying the course materials of the Project on Deep Learning - Artificial Neural Network (ANNs) there will be a written assignment test which you can take either during or at the end of the course. After successfully passing the test you will be able to claim the pdf certificate for £5.99. Original Hard Copy certificates need to be ordered at an additional cost of £9.60. Prerequisites This Project on Deep Learning - Artificial Neural Network (ANNs) does not require you to have any prior qualifications or experience. You can just enrol and start learning.This Project on Deep Learning - Artificial Neural Network (ANNs) was made by professionals and it is compatible with all PC's, Mac's, tablets and smartphones. You will be able to access the course from anywhere at any time as long as you have a good enough internet connection. Course Curriculum Section 01: Introduction Introduction of Project 00:03:00 Section 02: ANN Installation Setup Environment for ANN 00:11:00 ANN Installation 00:09:00 Section 03: Data Preprocessing Import Libraries and Data Preprocessing 00:11:00 Data Preprocessing 00:07:00 Data Preprocessing Continue 00:10:00 Section 04: Data Encoding Data Exploration 00:10:00 Encoding 00:07:00 Encoding Continue 00:06:00 Preparation of Dataset for Training 00:04:00 Section 05: Steps to Build ANN Steps to Build ANN Part 1 00:06:00 Steps to Build ANN Part 2 00:06:00 Steps to Build ANN Part 3 00:06:00 Steps to Build ANN Part 4 00:09:00 Section 06: Predictions and Imbalance-Learn Predictions 00:11:00 Predictions Continue 00:08:00 Resampling Data with Imbalance-Learn 00:09:00 Resampling Data with Imbalance-Learn Continue 00:08:00
Use deep learning, Computer Vision, and machine learning techniques to build an autonomous car with Python
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Discover the thrilling world of artificial intelligence with the 'Machine Learning Course with Python'. Immerse yourself in a voyage from foundational concepts, unveiling the mysteries behind algorithms, to diving deep into the cores of preprocessing, regression, and classification. Crafted meticulously, this course introduces Python as the catalyst, opening doors to data-driven decision-making and predictive analysis, empowering your journey in the ever-evolving field of machine learning. Learning Outcomes Grasp the foundational knowledge of various machine learning algorithms. Attain proficiency in preprocessing data for optimal outcomes. Master the nuances of regression analysis using Python. Delve into the intricacies of classification techniques. Enhance problem-solving abilities with practical Python-driven machine learning applications. Why choose this Machine Learning Course with Python course? Unlimited access to the course for a lifetime. Opportunity to earn a certificate accredited by the CPD Quality Standards after completing this course. Structured lesson planning in line with industry standards. Immerse yourself in innovative and captivating course materials and activities. Assessments are designed to evaluate advanced cognitive abilities and skill proficiency. Flexibility to complete the Machine Learning Course with Python Course at your own pace, on your own schedule. Receive full tutor support throughout the week, from Monday to Friday, to enhance your learning experience. Who is this Machine Learning Course with Python course for? Aspiring data scientists eager to harness the power of machine learning. Python enthusiasts aiming to delve into its applications in AI. Professionals in the tech industry seeking a transition into data roles. Academics and researchers wanting to employ machine learning in their work. Business analysts aiming to leverage predictive analytics for better insights. Career path Data Scientist: £40,000 - £70,000 Machine Learning Engineer: £50,000 - £80,000 AI Researcher: £45,000 - £75,000 Data Analyst: £30,000 - £50,000 Python Developer: £35,000 - £65,000 Business Intelligence Developer: £40,000 - £60,000 Prerequisites This Machine Learning Course with Python does not require you to have any prior qualifications or experience. You can just enrol and start learning.This Machine Learning Course with Python was made by professionals and it is compatible with all PC's, Mac's, tablets and smartphones. You will be able to access the course from anywhere at any time as long as you have a good enough internet connection. Certification After studying the course materials, there will be a written assignment test which you can take at the end of the course. After successfully passing the test you will be able to claim the pdf certificate for £4.99 Original Hard Copy certificates need to be ordered at an additional cost of £8. Course Curriculum Module 01: Introduction to Machine Learning Algorithms Introduction to types of ML algorithm 00:02:00 Module 02: Preprocessing Importing a dataset in python 00:02:00 Resolving Missing Values 00:06:00 Managing Category Variables 00:04:00 Training and Testing Datasets 00:07:00 Normalizing Variables 00:02:00 Normalizing Variables - Python Code 00:03:00 Summary 00:01:00 Module 03: Regression Simple Linear Regression - How it works? 00:04:00 Simple Linear Regreesion - Python Implementation 00:07:00 Multiple Linear Regression - How it works? 00:01:00 Multiple Linear Regression - Python Implementation 00:09:00 Decision Trees - How it works? 00:05:00 Random Forest - How it works? 00:03:00 Decision Trees and Random Forest - Python Implementation 00:04:00 Module 04: Classification kNN - How it works? 00:02:00 kNN - Python Implementation 00:10:00 Decision Tree Classifier and Random Forest Classifier in Python 00:10:00 SVM - How it works? 00:04:00 SVM - Python Implementation 00:06:00 Assignment Assignment - Machine Learning Course with Python 00:00:00
Managing Successful Machine Learning Projects Machine learning projects are a different beast. You have to secure access to the required data, often from multiple siloed sources. You have to switch back and forth between research mode and execution mode. You have to delicately guide data exploration towards a well-defined machine learning objective. You have to align this machine learning objective with your business objectives. You have to ensure that any sensitive data is adequately protected. How do you tame this beast and lead your project to successful completion? In this presentation, Dr. Neeraj Kashyap will share some practical tips for succeeding at machine learning, gained from his years at Google and in healthcare. We will discuss the life cycles of healthy machine learning projects and unhealthy ones so that you can identify impending disasters and avert them before they get out of hand. Throughout the session, we will emphasize data privacy, because no amount of intelligence is worth compromising your users for.
Description Register on the Deep Learning & Neural Networks Python - Keras today and build the experience, skills and knowledge you need to enhance your professional development and work towards your dream job. Study this course through online learning and take the first steps towards a long-term career. The course consists of a number of easy to digest, in-depth modules, designed to provide you with a detailed, expert level of knowledge. Learn through a mixture of instructional video lessons and online study materials. Receive online tutor support as you study the course, to ensure you are supported every step of the way. Get a certificate as proof of your course completion. The Deep Learning & Neural Networks Python - Keras course is incredibly great value and allows you to study at your own pace. Access the course modules from any internet-enabled device, including computers, tablets, and smartphones. The course is designed to increase your employability and equip you with everything you need to be a success. Enrol on the now and start learning instantly! What You Get With This Course Receive a digital certificate upon successful completion of the course Get taught by experienced, professional instructors Study at a time and pace that suits your learning style Get instant feedback on assessments 24/7 help and advice via email or live chat Get full tutor support on weekdays (Monday to Friday) Certificate of Achievement After the successful completion of the final assessment, you will receive a CPD-accredited certificate of achievement. The PDF certificate is for 9.99, and it will be sent to you immediately after through e-mail. You can get the hard copy for 15.99, which will reach your doorsteps by post. Method of Assessment You need to attend an assessment right after the completion of this course to evaluate your progression. For passing the assessment, you need to score at least 60%. After submitting your assessment, you will get feedback from our experts immediately. Who Is This Course For The course is ideal for those who already work in this sector or are aspiring professionals. This course is designed to enhance your expertise and boost your CV. Learn key skills and gain a professional qualification to prove your newly-acquired knowledge. Course Content Course Introduction And Table Of Contents Course Introduction and Table of Contents 00:11:00 Deep Learning Overview Deep Learning Overview - Theory Session - Part 1 00:06:00 Deep Learning Overview - Theory Session - Part 2 00:07:00 Choosing Between ML Or DL For The Next AI Project - Quick Theory Session Choosing Between ML or DL for the next AI project - Quick Theory Session 00:09:00 Preparing Your Computer Preparing Your Computer - Part 1 00:07:00 Preparing Your Computer - Part 2 00:06:00 Python Basics Python Basics - Assignment 00:09:00 Python Basics - Flow Control 00:09:00 Python Basics - Functions 00:04:00 Python Basics - Data Structures 00:12:00 Theano Library Installation And Sample Program To Test Theano Library Installation and Sample Program to Test 00:11:00 TensorFlow Library Installation And Sample Program To Test TensorFlow library Installation and Sample Program to Test 00:09:00 Keras Installation And Switching Theano And TensorFlow Backends Keras Installation and Switching Theano and TensorFlow Backends 00:10:00 Explaining Multi-Layer Perceptron Concepts Explaining Multi-Layer Perceptron Concepts 00:03:00 Explaining Neural Networks Steps And Terminology Explaining Neural Networks Steps and Terminology 00:10:00 First Neural Network With Keras - Understanding Pima Indian Diabetes Dataset First Neural Network with Keras - Understanding Pima Indian Diabetes Dataset 00:07:00 Explaining Training And Evaluation Concepts Explaining Training and Evaluation Concepts 00:11:00 Pima Indian Model - Steps Explained Pima Indian Model - Steps Explained - Part 1 00:09:00 Pima Indian Model - Steps Explained - Part 2 00:07:00 Coding The Pima Indian Model Coding the Pima Indian Model - Part 1 00:11:00 Coding the Pima Indian Model - Part 2 00:09:00 Pima Indian Model - Performance Evaluation Pima Indian Model - Performance Evaluation - Automatic Verification 00:06:00 Pima Indian Model - Performance Evaluation - Manual Verification 00:08:00 Pima Indian Model - Performance Evaluation - K-Fold Validation - Keras Pima Indian Model - Performance Evaluation - k-fold Validation - Keras 00:10:00 Pima Indian Model - Performance Evaluation - Hyper Parameters Pima Indian Model - Performance Evaluation - Hyper Parameters 00:12:00 Understanding Iris Flower Multi-Class Dataset Understanding Iris Flower Multi-Class Dataset 00:08:00 Developing The Iris Flower Multi-Class Model Developing the Iris Flower Multi-Class Model - Part 1 00:09:00 Developing the Iris Flower Multi-Class Model - Part 2 00:06:00 Developing the Iris Flower Multi-Class Model - Part 3 00:09:00 Understanding The Sonar Returns Dataset Understanding the Sonar Returns Dataset 00:07:00 Developing The Sonar Returns Model Developing the Sonar Returns Model 00:10:00 Sonar Performance Improvement - Data Preparation - Standardization Sonar Performance Improvement - Data Preparation - Standardization 00:15:00 Sonar Performance Improvement - Data Preparation - Standardization Sonar Performance Improvement - Layer Tuning for Smaller Network 00:07:00 Sonar Performance Improvement - Layer Tuning For Larger Network Sonar Performance Improvement - Layer Tuning for Larger Network 00:06:00 Understanding The Boston Housing Regression Dataset Understanding the Boston Housing Regression Dataset 00:07:00 Developing The Boston Housing Baseline Model Developing the Boston Housing Baseline Model 00:08:00 Boston Performance Improvement By Standardization Boston Performance Improvement by Standardization 00:07:00 Boston Performance Improvement By Deeper Network Tuning Boston Performance Improvement by Deeper Network Tuning 00:05:00 Boston Performance Improvement By Wider Network Tuning Boston Performance Improvement by Wider Network Tuning 00:04:00 Save & Load The Trained Model As JSON File (Pima Indian Dataset) Save & Load the Trained Model as JSON File (Pima Indian Dataset) - Part 1 00:09:00 Save & Load the Trained Model as JSON File (Pima Indian Dataset) - Part 2 00:08:00 Save And Load Model As YAML File - Pima Indian Dataset Save and Load Model as YAML File - Pima Indian Dataset 00:05:00 Load And Predict Using The Pima Indian Diabetes Model Load and Predict using the Pima Indian Diabetes Model 00:09:00 Load And Predict Using The Iris Flower Multi-Class Model Load and Predict using the Iris Flower Multi-Class Model 00:08:00 Load And Predict Using The Sonar Returns Model Load and Predict using the Sonar Returns Model 00:10:00 Load And Predict Using The Boston Housing Regression Model Load and Predict using the Boston Housing Regression Model 00:08:00 An Introduction To Checkpointing An Introduction to Checkpointing 00:06:00 Checkpoint Neural Network Model Improvements Checkpoint Neural Network Model Improvements 00:10:00 Checkpoint Neural Network Best Model Checkpoint Neural Network Best Model 00:04:00 Loading The Saved Checkpoint Loading the Saved Checkpoint 00:05:00 Plotting Model Behavior History Plotting Model Behavior History - Introduction 00:06:00 Plotting Model Behavior History - Coding 00:08:00 Dropout Regularization - Visible Layer Dropout Regularization - Visible Layer - Part 1 00:11:00 Dropout Regularization - Visible Layer - Part 2 00:06:00 Dropout Regularization - Hidden Layer Dropout Regularization - Hidden Layer 00:06:00 Learning Rate Schedule Using Ionosphere Dataset - Intro Learning Rate Schedule using Ionosphere Dataset 00:06:00 Time Based Learning Rate Schedule Time Based Learning Rate Schedule - Part 1 00:07:00 Time Based Learning Rate Schedule - Part 2 00:12:00 Drop Based Learning Rate Schedule Drop Based Learning Rate Schedule - Part 1 00:07:00 Drop Based Learning Rate Schedule - Part 2 00:08:00 Convolutional Neural Networks - Introduction Convolutional Neural Networks - Part 1 00:11:00 Convolutional Neural Networks - Part 2 00:06:00 MNIST Handwritten Digit Recognition Dataset Introduction to MNIST Handwritten Digit Recognition Dataset 00:06:00 Downloading and Testing MNIST Handwritten Digit Recognition Dataset 00:10:00 MNIST Handwritten Digit Recognition Dataset MNIST Multi-Layer Perceptron Model Development - Part 1 00:11:00 MNIST Multi-Layer Perceptron Model Development - Part 2 00:06:00 Convolutional Neural Network Model Using MNIST Convolutional Neural Network Model using MNIST - Part 1 00:13:00 Convolutional Neural Network Model using MNIST - Part 2 00:12:00 Large CNN Using MNIST Large CNN using MNIST 00:09:00 Load And Predict Using The MNIST CNN Model Load and Predict using the MNIST CNN Model 00:14:00 Introduction To Image Augmentation Using Keras Introduction to Image Augmentation using Keras 00:11:00 Augmentation Using Sample Wise Standardization Augmentation using Sample Wise Standardization 00:10:00 Augmentation Using Feature Wise Standardization & ZCA Whitening Augmentation using Feature Wise Standardization & ZCA Whitening 00:04:00 Augmentation Using Rotation And Flipping Augmentation using Rotation and Flipping 00:04:00 Saving Augmentation Saving Augmentation 00:05:00 CIFAR-10 Object Recognition Dataset - Understanding And Loading CIFAR-10 Object Recognition Dataset - Understanding and Loading 00:12:00 Simple CNN Using CIFAR-10 Dataset Simple CNN using CIFAR-10 Dataset - Part 1 00:09:00 Simple CNN using CIFAR-10 Dataset - Part 2 00:06:00 Simple CNN using CIFAR-10 Dataset - Part 3 00:08:00 Train And Save CIFAR-10 Model Train and Save CIFAR-10 Model 00:08:00 Load And Predict Using CIFAR-10 CNN Model Load and Predict using CIFAR-10 CNN Model 00:16:00 RECOMENDED READINGS Recomended Readings 00:00:00
Embark on a captivating journey into the mind with our comprehensive course 'The Neuroscience of Learning: Unlocking Your Cognitive Potential'. This enlightening course delves into the intricate world of neuroscience and its pivotal role in learning, providing you with invaluable insights into cognitive development, neuroplasticity, attention, focus, and the influence of emotions on learning. Through this course, you will explore the neuroscience behind motivation and reward systems, gain an understanding of learning disorders and neurodiversity, and discover effective, brain-based learning strategies. Unravel the mysteries of the human brain and unlock your full cognitive potential to enhance your learning capability and overall performance. Learning Outcomes Understand the basics of neuroscience and its connection to learning. Explore the concept of neuroplasticity and its role in learning. Understand cognitive development, attention, focus, and the impact of emotions on learning. Gain insight into motivation and reward systems in the brain. Develop effective, brain-based learning strategies and understand learning disorders and neurodiversity. Why choose this The Neuroscience of Learning: Unlocking Your Cognitive Potential course? Unlimited access to the course for a lifetime. Opportunity to earn a certificate accredited by the CPD Quality Standards and CIQ after completing this course. Structured lesson planning in line with industry standards. Immerse yourself in innovative and captivating course materials and activities. Assessments designed to evaluate advanced cognitive abilities and skill proficiency. Flexibility to complete the Course at your own pace, on your own schedule. Receive full tutor support throughout the week, from Monday to Friday, to enhance your learning experience. Unlock career resources for CV improvement, interview readiness, and job success. Who is this The Neuroscience of Learning: Unlocking Your Cognitive Potential course for? Educators and teachers seeking to understand the neuroscience behind learning. Students aiming to enhance their learning potential. Professionals working in cognitive development and brain-based learning. Individuals interested in understanding the brain and its impact on learning. Healthcare professionals working with learning disorders and neurodiversity. Career path Educational Neuroscientist: £35,000 - £45,000 per annum Learning and Development Specialist: £30,000 - £40,000 per annum Cognitive Enhancement Coach: £28,000 - £38,000 per annum Neuroeducation Consultant: £26,000 - £36,000 per annum Education Technology Specialist: £30,000 - £40,000 per annum Neuroscience Research Assistant: £28,000 - £38,000 per annum Prerequisites This The Neuroscience of Learning: Unlocking Your Cognitive Potential does not require you to have any prior qualifications or experience. You can just enrol and start learning.This The Neuroscience of Learning: Unlocking Your Cognitive Potential was made by professionals and it is compatible with all PC's, Mac's, tablets and smartphones. You will be able to access the course from anywhere at any time as long as you have a good enough internet connection. Certification After studying the course materials, there will be a written assignment test which you can take at the end of the course. After successfully passing the test you will be able to claim the pdf certificate for £4.99 Original Hard Copy certificates need to be ordered at an additional cost of £8. Course Curriculum Module 01: Introduction to Neuroscience and Learning Introduction to Neuroscience and Learning 00:09:00 Module 02: Basics of Neuroscience Basics of Neuroscience 00:16:00 Module 03: Neuroplasticity and Learning Neuroplasticity and Learning 00:14:00 Module 04: Cognitive Development and Learning Cognitive Development and Learning 00:13:00 Module 05: Attention and Focus Attention and Focus 00:13:00 Module 06: Emotions and Learning Emotions and Learning 00:11:00 Module 07: Motivation and Reward Systems Motivation and Reward Systems 00:12:00 Module 08: Learning Disorders and Neurodiversity Learning Disorders and Neurodiversity 00:12:00 Module 09: Brain-Based Learning Strategies Brain-Based Learning Strategies 00:10:00
Unlock the power of inclusive education with our course, 'Understanding Specific Learning Difficulties: Supporting Diverse Learners.' In a world that celebrates diversity, it's crucial to equip yourself with the knowledge and skills to support learners facing specific learning difficulties (SLD). This comprehensive program delves deep into the heart of SLD, guiding you through the intricacies of different learning disabilities, the diagnostic journey, and the far-reaching impacts of SLD on individuals and society. Through modules focused on learning difficulties and dyslexia, you'll gain a profound understanding of these challenges and learn how to provide person-centred care and support. Join us on this educational journey to empower diverse learners and make a meaningful impact in the field of education. Learning Outcomes Gain a thorough understanding of learning disabilities and specific learning difficulties (SLD). Identify and differentiate between various types of learning disabilities. Explore the diagnostic process and the journey to diagnosing SLD. Analyze the societal and individual impacts of SLD. Develop the skills to provide person-centred care and support for individuals with SLD. Why buy this Understanding Specific Learning Difficulties: Supporting Diverse Learners? Unlimited access to the course for forever Digital Certificate, Transcript, student ID all included in the price Absolutely no hidden fees Directly receive CPD accredited qualifications after course completion Receive one to one assistance on every weekday from professionals Immediately receive the PDF certificate after passing Receive the original copies of your certificate and transcript on the next working day Easily learn the skills and knowledge from the comfort of your home Certification After studying the course materials of the Understanding Specific Learning Difficulties: Supporting Diverse Learners there will be a written assignment test which you can take either during or at the end of the course. After successfully passing the test you will be able to claim the pdf certificate for £5.99. Original Hard Copy certificates need to be ordered at an additional cost of £9.60. Who is this Understanding Specific Learning Difficulties: Supporting Diverse Learners course for? Educators and teachers committed to inclusive education. Parents and caregivers seeking to better support children with learning difficulties. Individuals interested in pursuing careers in special education. Educational policymakers and advocates for inclusive learning. Anyone eager to make a positive impact on the lives of diverse learners. Prerequisites This Understanding Specific Learning Difficulties: Supporting Diverse Learners does not require you to have any prior qualifications or experience. You can just enrol and start learning.This Understanding Specific Learning Difficulties: Supporting Diverse Learners was made by professionals and it is compatible with all PC's, Mac's, tablets and smartphones. You will be able to access the course from anywhere at any time as long as you have a good enough internet connection. Career path Special Education Teacher: £25,000 - £40,000 per annum Learning Support Assistant: £16,000 - £23,000 per annum Educational Psychologist: £40,000 - £60,000 per annum Inclusion Coordinator: £30,000 - £45,000 per annum Speech and Language Therapist: £25,000 - £45,000 per annum Special Educational Needs (SEN) Advisor: £25,000 - £40,000 per annum Course Curriculum Module 01: Introduction to Learning Disability Introduction to Learning Disability 00:11:00 Module 02: Different Types of Learning Disability Different Types of Learning Disability 00:19:00 Module 03: The Journey to Diagnosis The Journey to Diagnosis 00:13:00 Module 04: Impacts of SLD on Individuals and Society Impacts of SLD on Individuals and Society 00:10:00 Module 05: Learning Difficulties and Dyslexia Learning Difficulties and Dyslexia 00:08:00 Module 06: Person Centred Care and Support Person Centred Care and Support 00:08:00 Module 07: Future Perspectives on SLD Future Perspectives on SLD 00:14:00
Formation officielle BRCGS Food v9 (Norme Mondiale pour la Sécurité des Denrées Alimentaires version 9) pour les sites en français. Dispensée en ligne (Zoom) en direct par un partenaire de formation agréé BRCGS. Frais d'examen et de certificat inclus dans le prix.