In this Machine Learning tutorial video, we will provide a clear explanation of logistic regression. Logistic regression is a statistical method used for predicting binary outcomes. It is widely used in many machine learning applications, including image recognition, fraud detection, and customer churn prediction. In this video, we’ll explore the theory behind logistic regression, its assumptions, and end-to-end implementation using Python. We’ll also cover various evaluation metrics, such as accuracy, recall, and precision, which are important in evaluating the performance of logistic regression models. This tutorial is suitable for beginners and intermediate-level machine learning enthusiasts who want to understand the concept of logistic regression from scratch. Join us in this exciting journey towards mastering logistic regression and take your data science skills to the next level!
Logistic Regression, a traditional statistical technique and also one of the most popular machine-learning models is explained clearly in this video.
In this Docker tutorial, we will learn about Containerization: Docker and Kubernetes for Machine Learning. Unleashing the Power of Docker and Kubernetes for Machine Learning Success
In the vast realm of technology, where innovation is the cornerstone of progress, containerization has emerged as a game-changer. With its ability to encapsulate applications and their dependencies into portable and lightweight units, containerization has revolutionized software development and machine learning.
Two titans of this containerization revolution, Docker and Kubernetes, have risen to prominence, reshaping how we build and scale applications. In the world of machine learning, where complexity and scalability are paramount, containerization offers an invaluable solution.
In this article, we will embark on a journey to explore the world of containerization, uncovering the wonders of Docker and Kubernetes and unraveling their profound importance and advantages in the context of machine learning.
A container serves as a standardized software unit that encompasses code and its dependencies, facilitating efficient and reliable execution across different computing environments. It consists of a lightweight, independent package known as a container image, which contains all the necessary components for running an application, such as code, runtime, system tools, libraries, and configurations.
Containers possess built-in isolation, ensuring each container operates independently and includes its own software, libraries, and configuration files. They can communicate with one another through well-defined channels while being executed by a single operating system kernel. This approach optimizes resource utilization compared to virtual machines, as it allows multiple isolated user-space instances, referred to as containers, to run on a single control host.
Containerization is highly important in the field of machine learning due to its numerous advantages. Here are some key benefits:
Containers encapsulate the entire software stack, ensuring consistent deployment and easy portability of ML models across different environments.
Dependencies are isolated within containers, preventing conflicts and simplifying dependency management, making it easier to work with different library versions.
Container orchestration platforms like Kubernetes enable efficient resource utilization and scaling of ML workloads, improving performance and reducing costs.
Check out DataCamp’s Docker cheat sheet.
Docker, often hailed as the pioneer of containerization, has transformed the landscape of software development and deployment. At its core, Docker provides a platform for creating and managing lightweight, isolated containers that encapsulate applications and their dependencies.
Docker achieves this by utilizing container images, which are self-contained packages that include everything needed to run an application, from the code to the system libraries and dependencies. Docker images can be easily created, shared, and deployed, allowing developers to focus on building applications rather than dealing with complex configuration and deployment processes.
Containerizing an application refers to the process of encapsulating the application and its dependencies into a Docker container. The initial step involves generating a
Dockerfile within the project directory. A Dockerfile is a text file that contains a series of instructions for building a Docker image. It serves as a blueprint for creating a container that includes the application code, dependencies, and configuration settings. Let’s see an example Dockerfile:
# Use the official Python base image with version 3.9 FROM python:3.9 # Set the working directory within the container WORKDIR /app # Copy the requirements file to the container COPY requirements.txt . # Install the dependencies RUN pip install -r requirements.txt # Copy the application code to the container COPY . . # Set the command to run the application CMD ["python", "app.py"]
If you want to learn more about common Docker commands and industry-wide best practices, then check out our blog, Docker for Data Science: An Introduction.
This Dockerfile follows a simple structure. It begins by specifying the base image as the official Python 3.9 version. The working directory inside the container is set to "/app". The file "requirements.txt" is copied into the container to install the necessary dependencies using the "RUN" instruction. The application code is then copied into the container. Lastly, the "CMD" instruction defines the command that will be executed when a container based on this image is run, typically starting the application with the command
Once you have a Dockerfile, you can build the image from this file by running the following command in the terminal. For this, you must have Docker installed on your computer. Follow these instructions to install Docker if you already haven’t done so.
docker build -t image-name:tag
Running this command may take a long time. As the image is being built you will see the logs printed on the terminal. The docker build command constructs an image, while the
-t flag assigns a name and tag to the image. The name represents the desired identifier for the image, and the tag signifies a version or label. The
. denotes the current directory where the Dockerfile is located, indicating to Docker that it should use the Dockerfile in the present directory as the blueprint for image construction.
Once the image is built, you can run
docker images command on terminal to confirm:
Example by author
Take the next step in your journey to mastering Docker with DataCamp's Introduction to Docker course. In this comprehensive course, you'll learn the fundamentals of containerization, explore the power of Docker, and gain hands-on experience with real-world examples.
While Docker revolutionized containerization, Kubernetes emerged as the orchestrator enabling the seamless management and scaling of containerized applications. Kubernetes, often referred to as K8s, automates the deployment, scaling, and management of containers across a cluster of nodes.
At its core, Kubernetes provides a robust set of features for container orchestration. It allows developers to define and declare the desired state of their applications using YAML manifests. Kubernetes then ensures that the desired state is maintained, automatically handling tasks such as scheduling containers, scaling applications based on demand, and managing container health and availability.
With Kubernetes, developers can seamlessly scale their applications to handle increased traffic and workload without worrying about the underlying infrastructure. It provides a declarative approach to infrastructure management, empowering developers to focus on building and improving their applications rather than managing the intricacies of container deployments.
Kubernetes provides several key components that are vital for deploying and managing machine learning applications efficiently. These components include Pods, Services, and Deployments.
In Kubernetes, a Pod is the smallest unit of deployment. It represents a single instance of a running process within the cluster. In the context of machine learning, a Pod typically encapsulates a containerized ML model or a specific component of the ML workflow. Pods can consist of one or more containers that work together and share the same network and storage resources.
Services enable communication and networking between different Pods. A Service defines a stable network endpoint to access one or more Pods. In machine learning scenarios, Services can be used to expose ML models or components as endpoints for data input or model inference. They provide load balancing and discovery mechanisms, making it easier for other applications or services to interact with the ML components.
Deployments provide a declarative way to manage the creation and scaling of Pods. A Deployment ensures that a specified number of replicas of a Pod are running at all times. It allows for easy scaling, rolling updates, and rollbacks of applications. Deployments are particularly useful for managing ML workloads that require dynamic scaling based on demand or when updates need to be applied without downtime.
To deploy an ML project in Kubernetes, a Kubernetes configuration file, typically written in YAML format, is used. This file specifies the desired state of the application, including information about the Pods, Services, Deployments, and other Kubernetes resources.
The configuration file describes the containers, environment variables, resource requirements, and networking aspects required for running the ML application. It defines the desired number of replicas, port bindings, volume mounts, and any specific configurations unique to the ML project.
Example Configuration yaml file for Kubernetes setup
apiVersion: v1 kind: Pod metadata: name: ml-model-pod spec: containers: - name: ml-model-container image: your-image-name:tag ports: - containerPort: 8080 env: - name: ENV_VAR_1 value: value1 - name: ENV_VAR_2 value: value2
In this example, various elements are used to configure a Pod in Kubernetes. These include specifying the Kubernetes API version, defining the resource type as a Pod, providing metadata like the Pod's name, and outlining the Pod's specifications in the spec section.
Once the Kubernetes configuration file is defined, deploying an ML model is a straightforward process. Using the kubectl command-line tool, the configuration file can be applied to the Kubernetes cluster to create the specified Pods, Services, and Deployments.
Kubernetes will ensure that the desired state is achieved, automatically creating and managing the required resources. This includes scheduling Pods on appropriate nodes, managing networking, and providing load balancing for Services.
Kubernetes excels at scaling and managing ML workloads. With horizontal scaling, more replicas of Pods can be easily created to handle increased demand or to parallelize ML computations. Kubernetes automatically manages the load distribution across Pods and ensures efficient resource utilization.
Containerization, powered by Docker and Kubernetes, has revolutionized the field of machine learning by offering numerous advantages and capabilities. Docker provides a platform for creating and managing lightweight, isolated containers that encapsulate applications and their dependencies. It simplifies the deployment process, allowing developers to focus on building applications rather than dealing with complex configurations.
Kubernetes, on the other hand, acts as the orchestrator that automates the deployment, scaling, and management of containerized applications. It ensures the desired state of the application is maintained, handles tasks such as scheduling containers, scaling applications based on demand, and manages container health and availability. Kubernetes enables efficient resource utilization and allows seamless scaling of machine learning workloads, providing a declarative approach to infrastructure management.
The combination of Docker and Kubernetes offers a powerful solution for managing machine learning applications. Docker provides reproducibility, portability, and easy dependency management, while Kubernetes enables efficient scaling, resource management, and orchestration of containers. Together, they allow organizations to unlock the full potential of machine learning in a scalable and reliable manner.
Article source: https://www.datacamp.com
This document aims to track the progress in Natural Language Processing (NLP) and give an overview of the state-of-the-art (SOTA) across the most common NLP tasks and their corresponding datasets.
It aims to cover both traditional and core NLP tasks such as dependency parsing and part-of-speech tagging as well as more recent ones such as reading comprehension and natural language inference. The main objective is to provide the reader with a quick overview of benchmark datasets and the state-of-the-art for their task of interest, which serves as a stepping stone for further research. To this end, if there is a place where results for a task are already published and regularly maintained, such as a public leaderboard, the reader will be pointed there.
If you want to find this document again in the future, just go to
nlpsota.com in your browser.
Results Results reported in published papers are preferred; an exception may be made for influential preprints.
Datasets Datasets should have been used for evaluation in at least one published paper besides the one that introduced the dataset.
Code We recommend to add a link to an implementation if available. You can add a
Code column (see below) to the table if it does not exist. In the
Code column, indicate an official implementation with Official. If an unofficial implementation is available, use Link (see below). If no implementation is available, you can leave the cell empty.
If you would like to add a new result, you can just click on the small edit button in the top-right corner of the file for the respective task (see below).
This allows you to edit the file in Markdown. Simply add a row to the corresponding table in the same format. Make sure that the table stays sorted (with the best result on top). After you've made your change, make sure that the table still looks ok by clicking on the "Preview changes" tab at the top of the page. If everything looks good, go to the bottom of the page, where you see the below form.
Add a name for your proposed change, an optional description, indicate that you would like to "Create a new branch for this commit and start a pull request", and click on "Propose file change".
For adding a new dataset or task, you can also follow the steps above. Alternatively, you can fork the repository. In both cases, follow the steps below:
|Model||Score||Paper / Source||Code|
These are tasks and datasets that are still missing:
You can extract all the data into a structured, machine-readable JSON format with parsed tasks, descriptions and SOTA tables.
The instructions are in structured/README.md.
Instructions for building the website locally using Jekyll can be found here.
For more tasks, datasets and results in Chinese, check out the Chinese NLP website.
Source Code: https://github.com/sebastianruder/NLP-progress
License: MIT license
7 Days Free Bootcamp on PYTHON AND MACHINE LEARNING in collaboration with Microsoft Learn Student Ambassador Program and AWS Students Club.
Link to the notebook:
There are two approaches to detecting fraud. And today we will talk about them, the most common one - using of rules and the more effective one - machine learning
0:00 Intro about travel reports in banks.
1:16 Two approaches to catching fraud
2:06 Rule-based fraud detection
4:17 How machine learning accelerates fraud detection
4:56 Step 1 - Understanding what is normal
7:00 Step 2 - Finding anomalies
8:40 Step 3 - Eliminating mistakes
9:25 Deep neural networks
10:00 Why does fraud still happen?
7 Days Free Bootcamp on PYTHON AND MACHINE LEARNING in collaboration with Microsoft Learn Student Ambassador Program and AWS Students Club.
Link to the notebook:
Word embeddings use an algorithm to train fixed-length dense vectors and continuous-valued vectors based on a large text corpus. Each word represents a point in vector space, and these points are learned and moved around the target word by preserving semantic relationships.
Read more: https://analyticsindiamag.com/hands-on-guide-to-word-embeddings-using-glove/
This video follows from where we left off in Part 2 in this series on the details of Logistic Regression. Last time we saw how to fit a squiggly line to the data. This time we’ll learn how to evaluate if that squiggly line is worth anything. In short, we’ll calculate the R-squared value and it’s associated p-value.
> NOTE: The formula at 13:58 should be 2[(LL(saturated) - LL(overall)) - (LL(saturated) - LL(fit))]. I got the terms flipped.
This video follows from where we left off in Part 1 in this series on the details of Logistic Regression. This time we’re going to talk about how the squiggly line is optimized to best fit the data.
NOTE: In statistics, machine learning and most programming languages, the default base for the log() function is 'e'. In other words, when I write, "log()", I mean "natural log()", or "ln()". Thus, the log to the base 'e' of 2.717 = 1.
#statquest #logistic #MLE #machine-learning
In this video we will learn about saturation modeling and deviation statistics.
#statquest #statistics #machine-learning
Bayes' Theorem is the foundation of Bayesian Statistics. This video was you through, step-by-step, how it is easily derived and why it is useful.
⭐ NOTE: When I code, I use Kite, a free AI-powered coding assistant that will help you code faster and smarter. The Kite plugin integrates with all the top editors and IDEs to give you smart completions and documentation while you’re typing. I love it! https://www.kite.com/get-kite/?utm_medium=referral&utm_source=youtube&utm_campaign=statquest&utm_content=description-only
0:00 Awesome song and introduction
3:05 A note about notation
5:21 Deriving Bayes' Theorem
9:12 Why Bayes' Theorem is useful
11:39 Another note about notation
#Probability #Bayesian #machine-learning
Here among all the layers of a network and output layer makes predictions after going through a humongous amount of calculations. It is a computational model that has a network architecture. So that it learn all the patterns based on the trained data and when it is performing it gives prediction with better accuracy.
In this video, you will learn about decision tree regression algorithm in python Other important playlists
#decisiontree #regression #python #machine-learning
Mathematics for machine learning will teach you all of the maths you need for machine learning. And it's available for free!
An Introduction to Statistical Learning by Gareth James, Daniela Witten, Trevor Hastie and Robert Tibshirani is one of the best books on the subject and it is free. Watch the video to see why I like it so much. And then get the pdf for yourself.