What is Neural Network In Machine Learning?

A Neural Network in Machine Learning is a series of algorithms that endeavors to recognize underlying relationships in a set of data through a process that mimics the way the human brain operates. Essentially, these networks can learn and model complex patterns and prediction problems using historical data, making them capable of machine learning and deep learning. Neural Networks are the backbone of artificial intelligence (AI) and the cornerstone of the most advanced machine learning algorithms. They are used in various applications such as image recognition, speech recognition, natural language processing, etc. Building skills in neural networks involves understanding the architecture of these networks, which includes layers of interconnected nodes or "neurons," and how data is processed through these layers. It also involves learning various algorithms and techniques for training these networks, such as backpropagation and gradient descent. Knowledge of programming languages like Python and libraries like TensorFlow and Keras is also essential.

What jobs use Neural Network In Machine Learning?

Machine Learning Engineer : They design and implement machine learning models, including neural networks, to solve business problems. Data Scientist : They use neural networks as their toolkit for analyzing complex data and making predictions. AI Engineer : They build and test AI models, including neural networks, and are responsible for maintaining AI infrastructure. Research Scientist : They conduct research on new and improved machine learning algorithms, including neural networks. Business Intelligence Developer : They use neural networks to help businesses make data-driven decisions. Big Data Engineer/Architect : They work with large amounts of data and use neural networks to extract insights from this data. Robotics Engineer : They use neural networks to help robots understand and interact with their environment. Bioinformatics Scientist : They use neural networks to analyze complex biological data. Quantitative Analyst : They use neural networks to predict market trends and make investment decisions. Computer Vision Engineer : They use neural networks to develop systems to understand and interpret visual data. Natural Language Processing Engineer : They use neural networks to understand and generate human language. Autonomous Systems Engineer : They use neural networks to develop self-driving cars, drones, and other autonomous systems.

How to get started Neural Network In Machine Learning?

To get started with Neural Networks in Machine Learning on Coursera: Foundations First : Begin with introductory machine learning courses to grasp the essentials. Neural Network Focus : Seek out courses or specializations specifically tailored to neural networks and deep learning. Hands-on Labs : Choose courses with practical exercises, allowing you to design and train neural networks on real datasets. Achieve Certifications : Complete the coursework and projects to earn certificates, highlighting your expertise in neural networks.

Best Neural Network In Machine Learning Courses Online with Certificates [2024]

Choose the Neural Network In Machine Learning Course That Aligns Best With Your Educational Goals

DeepLearning.AI
Neural Networks and Deep Learning
Skills you'll gain: Artificial Neural Networks, Deep Learning, Machine Learning, Machine Learning Algorithms, Python Programming, Computer Programming, Network Architecture, Network Model, Calculus, Human Learning, Regression, Mathematics
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Intermediate · Course · 1 - 4 Weeks
DeepLearning.AI
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Skills you'll gain: Machine Learning, Deep Learning, Artificial Neural Networks, Machine Learning Algorithms, Applied Machine Learning, Python Programming, Machine Learning Software, Network Model, Algorithms, Computer Programming, Computer Vision, Network Architecture, Natural Language Processing, Tensorflow, Human Learning, Data Model, Exploratory Data Analysis, Organizational Development, Process Analysis, Strategy, Mathematics, Mathematical Theory & Analysis, Linear Algebra, Regression, Calculus
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4.8 stars
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DeepLearning.AI
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Skills you'll gain: Artificial Neural Networks, Computer Vision, Machine Learning, Applied Machine Learning, Deep Learning, Machine Learning Software, Machine Learning Algorithms, Network Model, Tensorflow, Network Architecture, Human Learning
4.9 stars
(42K reviews)
Intermediate · Course · 1 - 4 Weeks
IBM
Deep Neural Networks with PyTorch
Skills you'll gain: Human Learning, Machine Learning, Deep Learning, Python Programming, Artificial Neural Networks, Machine Learning Algorithms, Applied Machine Learning, Algorithms, Regression, Mathematics
4.4 stars
(1.7K reviews)
Intermediate · Course · 1 - 3 Months
IBM
Introduction to Deep Learning & Neural Networks with Keras
Skills you'll gain: Algorithms, Artificial Neural Networks, Deep Learning, Human Learning, Machine Learning, Machine Learning Algorithms, Network Model, Applied Machine Learning, Network Architecture, Python Programming, Regression
4.7 stars
(1.6K reviews)
Intermediate · Course · 1 - 3 Months
Multiple educators
Machine Learning
Skills you'll gain: Machine Learning, Machine Learning Algorithms, Applied Machine Learning, Algorithms, Deep Learning, Machine Learning Software, Artificial Neural Networks, Human Learning, Python Programming, Regression, Statistical Machine Learning, Tensorflow, Mathematics, Critical Thinking, Network Model, Network Architecture, Reinforcement Learning
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4.9 stars
(27K reviews)
Beginner · Specialization · 1 - 3 Months
IBM
Introduction to Artificial Intelligence (AI)
Skills you'll gain: Algorithms, Applied Machine Learning, Artificial Neural Networks, Computer Vision, Deep Learning, Human Learning, Machine Learning, Machine Learning Algorithms, Machine Learning Software, Computational Thinking, Natural Language Processing, Data Science
4.7 stars
(15K reviews)
Beginner · Course · 1 - 4 Weeks
Imperial College London
Mathematics for Machine Learning
Skills you'll gain: Algebra, Linear Algebra, Mathematics, Machine Learning, Mathematical Theory & Analysis, Computer Programming, Python Programming, Machine Learning Algorithms, Calculus, Computational Logic, Algorithms, Applied Machine Learning, Differential Equations, Problem Solving, Statistical Analysis, Dimensionality Reduction, Statistical Programming, Probability & Statistics, Regression
4.6 stars
(14K reviews)
Beginner · Specialization · 3 - 6 Months
DeepLearning.AI
Improving Deep Neural Networks: Hyperparameter Tuning, Regularization and Optimization
Skills you'll gain: Applied Machine Learning, Deep Learning, Machine Learning, Artificial Neural Networks, Machine Learning Algorithms, Algorithms, Computer Programming, Mathematics, Python Programming, Mathematical Theory & Analysis, Tensorflow, Human Learning
4.9 stars
(63K reviews)
Intermediate · Course · 1 - 4 Weeks
DeepLearning.AI
AI For Everyone
Skills you'll gain: Data Science, Deep Learning, Machine Learning, Artificial Neural Networks, Business Transformation, Human Learning, Planning, Project Management
4.8 stars
(43K reviews)
Beginner · Course · 1 - 4 Weeks
Duke University
Introduction to Machine Learning
Skills you'll gain: Artificial Neural Networks, Machine Learning, Natural Language Processing, Python Programming, Regression
4.7 stars
(3.6K reviews)
Intermediate · Course · 1 - 3 Months
Status: Free
Sungkyunkwan University
Fundamentals of CNNs and RNNs
Skills you'll gain: Deep Learning, Machine Learning
4.2 stars
(24 reviews)
Beginner · Course · 1 - 3 Months