Google Crash Machine Learning

Logistic Regression | Machine Learning | Google for …

Logistic Regression. Instead of predicting exactly 0 or 1, logistic regression generates a probability—a value between 0 and 1, exclusive. For example, consider a logistic regression model for spam …

Neural Networks | Machine Learning | Google for Developers

Neural Networks. bookmark_border. Neural networks are a more sophisticated version of feature crosses. In essence, neural networks learn the appropriate feature crosses for you. Estimated Time: 3 minutes. Learning Objectives. Develop some intuition about neural networks, particularly about: hidden layers. activation functions.

Fairness | Machine Learning | Google for Developers

Fairness. bookmark_border. Estimated Time: 5 minutes. Learning Objectives. Become aware of common human biases that can inadvertently be reproduced by ML algorithms. Proactively explore data to identify sources of bias before training a model. Evaluate model predictions for bias. Evaluating a machine learning model responsibly …

My learning experience with Google's Machine Learning Crash Course

Jun 25, 2018. --. Machine Learning Crash Course. I came to know about Google's Machine Learning Crash Course (MLCC) from Sundar Pichai's tweet. I then enquired about it with some close ...

Reducing Loss | Machine Learning | Google for Developers

An iterative approach is one widely used method for reducing loss, and is as easy and efficient as walking down a hill. Estimated Time: 5 minutes. Learning Objectives. Discover how to train a model using an iterative approach. Understand full gradient descent and some variants, including: mini-batch gradient descent. stochastic gradient descent.

Classification: Precision and Recall | Machine Learning | Google …

Precision = T P T P + F P = 8 8 + 2 = 0.8. Recall measures the percentage of actual spam emails that were correctly classified—that is, the percentage of green dots that are to the right of the threshold line in Figure 1: Recall = T P T P + F N = 8 8 + 3 = 0.73. Figure 2 illustrates the effect of increasing the classification threshold.

Fairness: Types of Bias | Machine Learning

Fairness: Types of Bias. Estimated Time: 5 minutes. Machine learning models are not inherently objective. Engineers train models by feeding them a data set of training examples, and human involvement in the provision and curation of this data can make a model's predictions susceptible to bias. When building models, it's important to …

Introduction to TensorFlow | Machine Learning | Google for Developers

TensorFlow is a rich system for managing all aspects of a machine learning system; however, this class focuses on using a particular TensorFlow API to develop and train machine learning models. See the TensorFlow documentation for complete details on the broader TensorFlow system. TensorFlow APIs are arranged …

Google is offering free machine learning crash course; enroll now

Embark on an exciting journey into the world of machine learning with Google's latest offering a free machine learning crash course. This course gives a wonderful chance to investigate the complexities of machine learning for free cost, making it suitable for students of all experience levels who are interested in delving into the …

Classification: Accuracy | Machine Learning | Google for Developers

Formally, accuracy has the following definition: Accuracy = Number of correct predictions Total number of predictions. For binary classification, accuracy can also be calculated in terms of positives and negatives as follows: Accuracy = T P + T N T P + T N + F P + F N. Where TP = True Positives, TN = True Negatives, FP = False Positives, …

Training Neural Networks | Machine Learning

Helps gradient descent converge; avoid NaN trap. Avoiding outlier values can also help. Can use a few standard methods: Hard cap (clipping) to max, min. Dropout: Another form of regularization, useful for NNs. Works by randomly "dropping out" units in a network for a single gradient step. There's a connection to ensemble models here.

ML Universal Guides | Google for Developers

Deep Learning Tuning Playbook. This guide explains a scientific way to optimize the training of deep learning models. Simple step-by-step walkthroughs to solve common machine learning problems using best practices.

Generalization: Peril of Overfitting | Machine Learning | Google for

While the theoretical analysis provides formal guarantees under idealized assumptions, they can be difficult to apply in practice. Machine Learning Crash Course focuses instead on empirical evaluation to judge a model's ability to generalize to new data. A machine learning model aims to make good predictions on new, previously unseen data.

Descending into ML | Machine Learning | Google for …

Descending into ML. Linear regression is a method for finding the straight line or hyperplane that best fits a set of points. This module explores linear regression intuitively before laying the groundwork for a …

Grow your ML skills with free offer from Coursera

We're partnering with Coursera, one of the largest online learning platforms in the world, on a new ML Academy to help you sharpen your machine learning (ML) skills and learn about the latest ML technologies from Google Cloud at no-cost.The academy has three core components for you to take advantage of in July and August: Join the ML …

Regularization for Simplicity: L₂ Regularization

Machine Learning Crash Course focuses on two common (and somewhat related) ways to think of model complexity: Model complexity as a function of the weights of all the features in the model. …

ML Systems in the Real World: Guidelines | Machine Learning | Google

Here's a quick synopsis of effective ML guidelines: Keep your first model simple. Focus on ensuring data pipeline correctness. Use a simple, observable metric for training & evaluation. Own and monitor your input features. Treat your model configuration as code: review it, check it in. Write down the results of all experiments, especially ...

Machine Learning

Introduction to prompt design. Writing well structured prompts is an essential part of ensuring accurate, high quality responses from a language model. Check out the best practices for designing prompts. Gen AI. Learn Gen AI.

10 Best Machine Learning Courses to Take in 2022

The crash course is broken down into three large sections: (1) machine learning concepts, (2) machine learning engineering, and (3) machine learning systems in the real world. Each section consists of video lectures presented by Google researchers, readings, and quizzes for self-assessment.

Classification: ROC Curve and AUC | Machine Learning | Google …

ROC curvereceiver operating characteristic curve) is a graph showing the performance of a classification model at all classification thresholds. This curve plots two parameters: An ROC curve plots TPR vs. FPR at different classification thresholds. Lowering the classification threshold classifies more items as positive, thus increasing both ...

Jeff Dean (Google): Exciting Trends in Machine Learning

January 27, 2024. Day. Week. Month. List All Upcoming Events. A Ken Kennedy Institute Distinguished Lecture "Exciting Trends in Machine Learning" by Jeff Dean, Chief …

How To Take Advantage From Google Machine …

The google crash course on machine learning consists of: 25 lessons; Total of 15 hours of classes; Over 40 exercises; All lessons are taught by Google researchers; Real-world case studies; Interactive …

Framing | Machine Learning | Google for Developers

This module investigates how to frame a task as a machine learning problem, and covers many of the basic vocabulary terms shared across a wide range of machine learning (ML) methods. Estimated Time: 2 minutes. Learning Objectives. Refresh the fundamental machine learning terms. Explore various uses of machine learning.

About Machine Learning Crash Course

Machine Learning Crash Course (MLCC) teaches the basics of machine learning through a series of lessons that include: video lectures from researchers at Google. text written specifically for newcomers to ML. interactive visualizations of algorithms in action. real-world case studies. While learning new concepts, you'll immediately put them into ...

Machine Learning Glossary | Google for Developers

An open-source machine learning compiler for GPUs, CPUs, and ML accelerators. The XLA compiler takes models from popular ML frameworks such as PyTorch, TensorFlow, and JAX, and optimizes them for high-performance execution across different hardware platforms including GPUs, CPUs, and ML accelerators.

Introduction | Machine Learning | Google for Developers

Introduction. 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.

Learn with Google AI: Making ML education available …

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

Machine Learning | Google for Developers

A brief introduction to machine learning. A hands-on course to explore the critical basics of machine learning. A course to help you map real-world problems to machine learning solutions. An introduction to preparing your data for ML workflows. Strategies for testing and debugging machine learning models and pipelines.

litaotao/machine-learning-crash-course

machine-learning-crash-course from google 6 stars 4 forks Branches Tags Activity. Star Notifications Code; Issues 0; Pull requests 0; Actions; Projects 0; Security; Insights; …

Machine Learning in OpenCV (7-Day Mini-Course)

Machine learning is an amazing tool for many tasks. OpenCV is a great library for manipulating images. It would be great if we can put them together. In this 7-part …