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Janani Ravi
Even as the number of machine learning frameworks and libraries increases on a daily basis, scikit-learn is retaining its popularity with ease. The one domain where scikit-learn is distinctly behind competing frameworks is in the construction of neural...
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Even as the number of machine learning frameworks and libraries increases on a daily basis, scikit-learn is retaining its popularity with ease. The one domain where scikit-learn is distinctly behind competing frameworks is in the construction of neural...
Read more
Even as the number of machine learning frameworks and libraries increases on a daily basis, scikit-learn is retaining its popularity with ease. The one domain where scikit-learn is distinctly behind competing frameworks is in the construction of neural networks for deep learning. In this course, Building Neural Networks with scikit-learn, you will gain the ability to make the best of the support that scikit-learn does provide for deep learning. First, you will learn precisely what gaps exist in scikit-learn’s support for neural networks, as well as how to leverage constructs such as the perceptron and multi-layer perceptrons that are made available in scikit-learn. Next, you will discover how perceptrons are just neurons with step activation, and multi-layer perceptrons are effectively feed-forward neural networks. Then, you'll use scikit-learn estimator objects for neural networks to build regression and classification models, working with numeric, text, and image data. Finally, you will use Restricted Boltzmann Machines to perform dimensionality reduction on data before feeding it into a machine learning model. When you’re finished with this course, you will have the skills and knowledge to leverage every bit of support that scikit-learn currently has to offer for the construction of neural networks.
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Teaches how to use scikit-learn constructs to build neural networks, providing a solid foundation in deep learning
Suitable for learners with prior experience in machine learning and an interest in deep learning
Emphasizes the practical application of scikit-learn for neural network construction, making it relevant to industry
Covers advanced topics such as Restricted Boltzmann Machines for dimensionality reduction, expanding learners' knowledge in machine learning
Requires prior machine learning knowledge, making it less accessible to complete beginners
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Career center
Learners who complete Building Neural Networks with scikit-learn will develop knowledge and skills
that may be useful to these careers:
Data Scientist
Data Scientist
Data Scientists bridge the gap between analysts and engineers. They have the skills to extract useful data from raw data using machine learning algorithms. The models they construct use the data to make predictions and uncover actionable insights. To be successful in this role, you must have a strong foundation in neural networks, data modeling, and statistical analysis. Gaining proficiency in these areas will give you an edge in the competitive field of Data Science.
Machine Learning Engineer
Machine Learning Engineer
Machine Learning Engineers develop and maintain machine learning models. They use their expertise in neural networks to build models that can learn from data and make predictions. To be successful in this role, you must have a strong foundation in neural network architecture and algorithms. Taking this course will enhance your understanding of how neural networks work and how to apply the knowledge to real-world machine learning problems.
Deep Learning Engineer
Deep Learning Engineer
Deep Learning Engineers specialize in building deep learning models. They have the expertise to develop and implement deep neural networks for a variety of applications. To be successful in this role, you must have a deep understanding of neural network architectures, algorithms, and training techniques. Taking this course will provide you with the foundation you need to excel in this field.
Artificial Intelligence Engineer
Artificial Intelligence Engineer
Artificial Intelligence Engineers design, develop, and implement AI systems. They have the expertise to apply neural networks and other AI techniques to solve complex problems. To be successful in this role, you must have a strong foundation in neural network architecture, algorithms, and training techniques. Taking this course will provide you with the knowledge you need to be successful in this field.
Neural Network Architect
Neural Network Architect
Neural Network Architects design and develop the architecture of neural networks. They have the expertise to create innovative neural network architectures that can solve complex problems. To be successful in this role, you must have a deep understanding of neural network theory and algorithms. Taking this course will provide you with the foundation you need to excel in this field.
Computer Vision Engineer
Computer Vision Engineer
Computer Vision Engineers develop and implement computer vision systems. They have the expertise to apply neural networks to tasks such as image recognition, object detection, and video analysis. To be successful in this role, you must have a strong foundation in neural network architecture, algorithms, and training techniques. Taking this course will provide you with the knowledge you need to be successful in this field.
Natural Language Processing Engineer
Natural Language Processing Engineer
Natural Language Processing Engineers develop and implement natural language processing systems. They have the expertise to apply neural networks to tasks such as text classification, sentiment analysis, and machine translation. To be successful in this role, you must have a strong foundation in neural network architecture, algorithms, and training techniques. Taking this course will provide you with the knowledge you need to be successful in this field.
Speech Recognition Engineer
Speech Recognition Engineer
Speech Recognition Engineers develop and implement speech recognition systems. They have the expertise to apply neural networks to tasks such as speech recognition, speaker identification, and language identification. To be successful in this role, you must have a strong foundation in neural network architecture, algorithms, and training techniques. Taking this course will provide you with the knowledge you need to be successful in this field.
Robotics Engineer
Robotics Engineer
Robotics Engineers design, develop, and implement robotic systems. They have the expertise to apply neural networks to tasks such as robot navigation, object manipulation, and human-robot interaction. To be successful in this role, you must have a strong foundation in neural network architecture, algorithms, and training techniques. Taking this course will provide you with the knowledge you need to be successful in this field.
Data Analyst
Data Analyst
Data Analysts collect, analyze, and interpret data. They use their expertise in neural networks to build models that can identify patterns and trends in data. To be successful in this role, you must have a strong foundation in neural network architecture, algorithms, and training techniques. Taking this course will provide you with the knowledge you need to be successful in this field.
Business Analyst
Business Analyst
Business Analysts use data to help businesses make better decisions. They have the expertise to apply neural networks to tasks such as market research, customer segmentation, and risk assessment. To be successful in this role, you must have a strong foundation in neural network architecture, algorithms, and training techniques. Taking this course will provide you with the knowledge you need to be successful in this field.
Financial Analyst
Financial Analyst
Financial Analysts use data to help businesses make better financial decisions. They have the expertise to apply neural networks to tasks such as stock market prediction, credit risk assessment, and fraud detection. To be successful in this role, you must have a strong foundation in neural network architecture, algorithms, and training techniques. Taking this course will provide you with the knowledge you need to be successful in this field.
Marketing Analyst
Marketing Analyst
Marketing Analysts use data to help businesses make better marketing decisions. They have the expertise to apply neural networks to tasks such as customer segmentation, campaign optimization, and lead generation. To be successful in this role, you must have a strong foundation in neural network architecture, algorithms, and training techniques. Taking this course will provide you with the knowledge you need to be successful in this field.
Product Manager
Product Manager
Product Managers are responsible for the development and launch of new products. They have the expertise to apply neural networks to tasks such as market research, product design, and user experience. To be successful in this role, you must have a strong foundation in neural network architecture, algorithms, and training techniques. Taking this course will provide you with the knowledge you need to be successful in this field.
Software Engineer
Software Engineer
Software Engineers design, develop, and implement software systems. They have the expertise to apply neural networks to tasks such as natural language processing, computer vision, and robotics. To be successful in this role, you must have a strong foundation in neural network architecture, algorithms, and training techniques. Taking this course will provide you with the knowledge you need to be successful in this field.
For more career information including salaries, visit:
OpenCourser.com/course/z2hlmk/building
Reading list
We haven't picked any books for this reading list yet.
Comprehensive introduction to causal inference in statistics. It covers all the major concepts of causal inference, including graphical models, counterfactuals, and causal effects.
Comprehensive introduction to machine learning. It covers all the major concepts of machine learning, including data preprocessing, feature engineering, model selection, and evaluation.
Provides a comprehensive introduction to information theory, inference, and learning algorithms. It covers all the major concepts of information theory, inference, and learning algorithms, including entropy, mutual information, and Bayesian inference.
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Provides a comprehensive introduction to deep learning using Python. It covers all the major concepts of deep learning, including neural networks, convolutional neural networks, and recurrent neural networks.
Provides a comprehensive introduction to machine learning using Python and the scikit-learn, Keras, and TensorFlow libraries. It covers a wide range of topics, including data preprocessing, feature engineering, model selection, and evaluation.
Provides a comprehensive introduction to statistical learning with sparsity. It covers all the major concepts of statistical learning with sparsity, including Lasso, Elastic Net, and Group Lasso.
Comprehensive guide to machine learning using scikit-learn. It covers all the major concepts of machine learning, including data preprocessing, feature engineering, model selection, and evaluation.
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Provides a comprehensive introduction to convex optimization. It covers all the major concepts of convex optimization, including linear programming, quadratic programming, and semidefinite programming.
Comprehensive introduction to reinforcement learning. It covers all the major concepts of reinforcement learning, including Markov decision processes, value functions, and policy gradient methods.
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Provides a comprehensive introduction to PyTorch for deep learning. It covers all the major concepts of PyTorch for deep learning, including neural networks, convolutional neural networks, and recurrent neural networks.
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Comprehensive introduction to bandit algorithms. It covers all the major concepts of bandit algorithms, including multi-armed bandits, contextual bandits, and Thompson sampling.
This practical guide provides a hands-on introduction to machine learning, including neural networks. It covers a wide range of topics, including data preprocessing, feature engineering, model selection, and evaluation. It is suitable for beginners and experienced practitioners alike.
Written by a pioneer in the field, this practical guide provides a comprehensive overview of machine learning, including neural networks. It is suitable for beginners and experienced practitioners alike, and covers topics such as supervised learning, unsupervised learning, and deep learning.
This advanced textbook provides a comprehensive and rigorous treatment of neural networks, covering topics such as supervised learning, unsupervised learning, and deep learning. It is suitable for graduate students and researchers with a strong background in mathematics and statistics.
This advanced textbook provides a comprehensive and rigorous treatment of pattern recognition and neural networks, covering topics such as supervised learning, unsupervised learning, and deep learning. It is suitable for graduate students and researchers with a strong background in mathematics and statistics.
This advanced textbook provides a comprehensive and rigorous treatment of neural network design, covering topics such as supervised learning, unsupervised learning, and deep learning. It is suitable for graduate students and researchers with a strong background in mathematics and statistics.
This practical guide provides a comprehensive overview of deep learning, using Fastai and PyTorch. It covers a wide range of topics, including convolutional neural networks, recurrent neural networks, and generative adversarial networks. It is suitable for beginners and experienced practitioners alike.
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This introductory textbook provides a comprehensive overview of deep learning, covering topics such as convolutional neural networks, recurrent neural networks, and generative adversarial networks. It is suitable for beginners and experienced practitioners alike.
Save
This practical guide provides a comprehensive overview of deep learning, using Python and the Keras library. It covers a wide range of topics, including convolutional neural networks, recurrent neural networks, and generative adversarial networks. It is suitable for beginners and experienced practitioners alike.
Save
Authored by three leading researchers in the field, this advanced textbook provides a comprehensive and rigorous treatment of deep learning, covering topics such as convolutional neural networks, recurrent neural networks, and generative adversarial networks. It is suitable for graduate students and researchers with a strong background in machine learning.
For more information about how these books relate to this course, visit:
OpenCourser.com/course/z2hlmk/building
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Time
1 hours
Via
Pluralsight
Instructor
Janani Ravi
This course belongs to a
collection
called
Building Machine Learning Solutions with scikit-learn. It's comprised of nine courses:
- Building Your First scikit-learn Solution
- Building Classification Models with scikit-learn
- Building Regression Models with scikit-learn
- Building Clustering Models with scikit-learn
- Building Neural Networks with scikit-learn (viewing)
- Reducing Dimensions in Data with scikit-learn
- Employing Ensemble Methods with scikit-learn
- Preparing Data for Modeling with scikit-learn
- Scaling scikit-learn Solutions
Good to know
Teaches how to use scikit-learn constructs to build neural networks, providing a solid foundation in deep learning
Suitable for learners with prior experience in machine learning and an interest in deep learning
Emphasizes the practical application of scikit-learn for neural network construction, making it relevant to industry
Covers advanced topics such as Restricted Boltzmann Machines for dimensionality reduction, expanding learners' knowledge in machine learning
Requires prior machine learning knowledge, making it less accessible to complete beginners
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