Practical Machine Learning with TensorFlow 2.0 and Scikit-Learn [Video]
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Free ChapterInstalling Scikit-Learn and TensorFlow 2.0
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ML Fundamentals: Scikit-Learn Introduction
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Applied Scikit-Learn: Supervised Learning Models
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Unsupervised Learning
- K-means and Hierarchical Clustering
- Connectivity and Density Clustering
- Gaussian Mixture Models
- Variational Bayesian Gaussian Mixture Models
- Decomposing Signals into Components
- Signal Decomposition with Factor and Independent Component Analysis
- Novelty Detection
- Outlier Detection
- Locally Linear Embedded Manifolds
- Multi-Dimensional Scaling and t-SNE Manifolds
- Density Estimation
- Restricted Boltzmann Machine
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TensorFlow 2.0 Essentials for ML
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Applied Deep Learning for Computer Vision Tasks
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Natural Language Processing and Sequential Data
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Applied Sequence to Sequence and Transformer Models
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Working with Reinforcement Learning
About this
video
Have you been looking for a course that teaches you effective machine learning in scikit-learn and TensorFlow 2.0? Or have you always wanted an efficient and skilled working knowledge of how to solve problems that can't be explicitly programmed through the latest machine learning techniques?
If you're familiar with pandas and NumPy, this course will give you up-to-date and detailed knowledge of all practical machine learning methods, which you can use to tackle most tasks that cannot easily be explicitly programmed; you'll also be able to use algorithms that learn and make predictions or decisions based on data.
The theory will be underpinned with plenty of practical examples, and code example walk-throughs in Jupyter notebooks. The course aims to make you highly efficient at constructing algorithms and models that perform with the highest possible accuracy based on the success output or hypothesis you've defined for a given task.
By the end of this course, you will be able to comfortably solve an array of industry-based machine learning problems by training, optimizing, and deploying models into production. Being able to do this effectively will allow you to create successful prediction and decisions for the task in hand (for example, creating an algorithm to read a labeled dataset of handwritten digits).
The code bundle for this course is available at https://github.com/PacktPublishing/Practical-Machine-Learning-with-TensorFlow-2.0-and-Scikit-Learn
- Publication date:
- June 2020
- Publisher
- Packt
- Duration
- 10 hours 28 minutes
- ISBN
- 9781789959161
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