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Machine Learning Crash Course From Google

May 22, 2018. We've been talking a lot about machine learning lately. People are using it for speech generation and recognition, computer …

Numerical data: Qualities of good numerical features | Machine Learning …

This unit has explored ways to map raw data into suitable feature vectors.Good numerical features share the qualities described in this section.. Clearly named. Each feature should have a clear, sensible, and obvious meaning to …

Introduction | Machine Learning | Google for Developers

Completed Machine Learning Crash Course either in-person or self-study, or you have equivalent knowledge. Familiarity with linear algebra (inner product, matrix-vector product). Happy Learning!

Google's Free Machine Learning Crash Course

Ultimately, Google is a prestigious, renowned organization for good reason, and their free machine learning crash course is a no-brainer. Just make sure you execute on what you learned. Machine ...

Classification: ROC and AUC | Machine Learning

(Optional, advanced) Precision-recall curve. AUC and ROC work well for comparing models when the dataset is roughly balanced between classes. When the dataset is imbalanced, precision-recall …

Production ML systems: Monitoring pipelines | Machine Learning | Google

Learn techniques for monitoring production ML pipelines in production, including writing data schemes, writing unit tests, checking for training-server skew, and checking for label leakage.

Fairness: Types of bias | Machine Learning

Note: The following inventory of biases provides just a small selection of biases that are often uncovered in machine learning datasets; this list is not intended to be exhaustive. Wikipedia's catalog of cognitive biases enumerates over 100 different types of human bias that can affect our judgment.

Embeddings | Machine Learning | Google for Developers

You want to develop a machine learning (ML) model that can predict food similarity, so your app can make high quality recommendations ("Since you like pancakes, we recommend crepes"). To train your model, you curate a dataset of 5,000 popular meal items, including borscht, hot dog, salad, pizza, and shawarma .

Overfitting | Machine Learning | Google for Developers

Overfitting means creating a model that matches (memorizes) the training set so closely that the model fails to make correct predictions on new data. An overfit model is analogous to an invention that performs well in the lab but is worthless in the real world. Tip: Overfitting is a common problem in machine learning, not an academic …

Logistic Regression | Machine Learning | Google for …

Estimated module length: 35 minutes Learning Objectives Identify use cases for performing logistic regression. Explain how logistic regression models use the sigmoid function to calculate probability.

Neural Networks: Training using backpropagation | Machine Learning

Many machine learning code libraries (such as Keras) handle backpropagation automatically, so you don't need to perform any of the underlying calculations yourself. Check out the following video for a conceptual overview of how backpropagation works:

Linear regression: Loss | Machine Learning

In statistics and machine learning, loss measures the difference between the predicted and actual values. Loss focuses on the distance between the values, not the direction. For example, if a model predicts 2, but the actual value is 5, we don't care that the loss is negative $ -3 $ ($ 2-5=-3 $).

Working with numerical data | Machine Learning

This course module teaches fundamental concepts and best practices for working with numerical data, from how data is ingested into a model using feature vectors to feature engineering techniques such as normalization, binning, scrubbing, and creating synthetic features with polynomial transforms.

Machine Learning on Google Cloud | Coursera

Offered by Google Cloud. Learn machine learning with Google Cloud. Real-world experimentation with end-to-end ML Enroll for free.

Learn with Google AI: Making ML education …

Learn with Google AI also features a new, free course called Machine Learning Crash Course (MLCC). The course provides …

Investigating Factors Influencing Crash Severity on …

Due to poor road design, challenging terrain, and difficult geological conditions, traffic accidents on mountainous two-lane roads are more frequent and severe. This …

Fairness: Test your knowledge | Machine Learning

Introduction (3 min) How a model ingests data with feature vectors (5 min) First steps (5 min) Programming exercises (10 min) Normalization (20 min)

Machine learning education

Google Developers Machine Learning Crash Course The Machine Learning Crash Course with TensorFlow APIs is a self-study guide for aspiring machine learning practitioners. It features a series of lessons with video lectures, real-world case studies, and hands-on practice exercises. Free ...

Machine Learning | Google for Developers

The advanced courses teach tools and techniques for solving a variety of machine learning problems. The courses are structured independently. Take them based on interest or problem domain.

Linear regression: Hyperparameters | Machine Learning | Google …

Hyperparameters are variables that control different aspects of training. Three common hyperparameters are: Learning rate; Batch size; Epochs; In contrast, parameters are the variables, like the weights and bias, that are part of the model itself. In other words, hyperparameters are values that you control; parameters are values that …

Introduction | Machine Learning | Google for Developers

Generative adversarial networks (GANs) are an exciting recent innovation in machine learning. GANs are generative models: they create new data instances that resemble your training data. For example, GANs can create images that look like photographs of human faces, even though the faces don't belong to any real person.

Classification: Multi-class classification | Machine Learning | Google

Multi-class classification can be treated as an extension of binary classification to more than two classes. If each example can only be assigned to one class, then the classification problem can be handled as a binary classification problem, where one class contains one of the multiple classes, and the other class contains all the other …

AutoML: Getting started | Machine Learning

When you are using AutoML, ensure that the tool you choose can support the objectives of your ML project. Most AutoML tools support a variety of supervised machine learning algorithms and input data types. For more information about problem framing, take a look at the module on Introduction to Machine Learning Problem …

Machine Learning Operations (MLOps) with Vertex AI: Model …

This course equips machine learning practitioners with the essential tools, techniques, and best practices for evaluating both generative and predictive AI models. Model …

Thresholds and the confusion matrix | Machine Learning | Google …

Learn how a classification threshold can be set to convert a logistic regression model into a binary classification model, and how to use a confusion matrix to assess the four types of predictions: true positive (TP), true …

My learning experience with Google's Machine Learning Crash Course

Machine Learning Crash Course. I came to know about Google's Machine Learning Crash Course (MLCC) from Sundar Pichai's tweet.

Neural networks | Machine Learning | Google for Developers

This course module teaches the basics of neural networks: the key components of neural network architectures (nodes, hidden layers, activation functions), how neural network inference is performed, how neural networks are trained using backpropagation, and how neural networks can be used for multi-class classification …

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About Machine Learning Crash Course

Machine Learning Crash Course (MLCC) teaches the basics of machine learning and large language models through a series of lessons that include: Approachable text written specifically for machine learning newcomers; Interactive educational widgets; Videos to reinforce lessons; Challenging multiple choice questions; Optional programming exercises

Datasets, generalization, and overfitting | Machine Learning | Google …

This course module provides guidelines for preparing data for machine learning model training, including how to identify unreliable data; how to discard and impute data; how to improve labels; how to split data into training, validation and test sets; and how to prevent overfitting and ensure models can generalize using regularization techniques.

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پست الکترونیک

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