Clear Explanation of Logistic Regression in Machine Learning

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.

#machine-learning 

Machine Learning with Docker and Kubernetes: Containerization Guide

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.

What is a Container?

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.

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Why Do Containers Matter for Modern Applications?

Containerization is highly important in the field of machine learning due to its numerous advantages. Here are some key benefits:

1. Reproducibility and portability

Containers encapsulate the entire software stack, ensuring consistent deployment and easy portability of ML models across different environments.

2. Isolation and dependency management

Dependencies are isolated within containers, preventing conflicts and simplifying dependency management, making it easier to work with different library versions.

3. Scalability and resource management

Container orchestration platforms like Kubernetes enable efficient resource utilization and scaling of ML workloads, improving performance and reducing costs.

Why Use Docker?

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.

Creating a Dockerfile in your project

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 python app.py.

Building Docker Image from Dockerfile

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.

Why Use Kubernetes?

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.

Understanding Kubernetes components for machine learning: Pods, Services, Deployments

Kubernetes provides several key components that are vital for deploying and managing machine learning applications efficiently. These components include Pods, Services, and Deployments.

1. Pods

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.

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2. Services

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.

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3. Deployments

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.

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Writing a Kubernetes Configuration File for an ML project

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.

Kubernetes for machine learning

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.

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Conclusion

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

#docker #machine-learning 

NLP-progress: Tracking Progress in Natural Language Processing

Tracking Progress in Natural Language Processing

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 nlpprogress.com or nlpsota.com in your browser.

Contributing

Guidelines

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.

Adding a new result

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".

Adding a new dataset or task

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:

1. If your task is completely new, create a new file and link to it in the table of contents above.
2. If not, add your task or dataset to the respective section of the corresponding file (in alphabetical order).
3. Briefly describe the dataset/task and include relevant references.
4. Describe the evaluation setting and evaluation metric.
5. Show how an annotated example of the dataset/task looks like.
6. Add a download link if available.
7. Copy the below table and fill in at least two results (including the state-of-the-art) for your dataset/task (change Score to the metric of your dataset). If your dataset/task has multiple metrics, add them to the right of Score.
8. Submit your change as a pull request.

Wish list

These are tasks and datasets that are still missing:

• Bilingual dictionary induction
• Discourse parsing
• Keyphrase extraction
• Knowledge base population (KBP)
• More dialogue tasks
• Semi-supervised learning
• Frame-semantic parsing (FrameNet full-sentence analysis)

Exporting into a structured format

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 site locally

Instructions for building the website locally using Jekyll can be found here.

English

• Automatic speech recognition
• CCG
• Common sense
• Constituency parsing
• Coreference resolution
• Data-to-Text Generation
• Dependency parsing
• Dialogue
• Domain adaptation
• Entity linking
• Grammatical error correction
• Information extraction
• Intent Detection and Slot Filling
• Language modeling
• Lexical normalization
• Machine translation
• Missing elements
• Multi-task learning
• Multi-modal
• Named entity recognition
• Natural language inference
• Part-of-speech tagging
• Paraphrase Generation
• Question answering
• Relation prediction
• Relationship extraction
• Semantic textual similarity
• Semantic parsing
• Semantic role labeling
• Sentiment analysis
• Shallow syntax
• Simplification
• Stance detection
• Summarization
• Taxonomy learning
• Temporal processing
• Text classification
• Word sense disambiguation

Vietnamese

• Dependency parsing
• Intent detection and Slot filling
• Machine translation
• Named entity recognition
• Part-of-speech tagging
• Semantic parsing
• Word segmentation

Hindi

• Chunking
• Part-of-speech tagging
• Machine Translation

Chinese

• Entity linking
• Chinese word segmentation
• Question answering

For more tasks, datasets and results in Chinese, check out the Chinese NLP website.

French

• Question answering
• Summarization

Russian

• Question answering
• Sentiment Analysis
• Summarization

Spanish

• Named Entity Recognition
• Entity linking
• Summarization

Portuguese

• Question Answering

Korean

• Question Answering

Nepali

• Machine Translation

Bengali

• Part-of-speech Tagging
• Sentiment Analysis

Persian

• Named entity recognition
• Natural language inference
• Summarization

Turkish

• Summarization

German

• Question Answering
• Summarization

Arabic

• Language modeling

Download Details:

Author: Sebastianruder
Source Code: https://github.com/sebastianruder/NLP-progress 
License: MIT license

#machinelearning #machine #nlp 

Course PYTHON AND MACHINE LEARNING for Beginner : Numpy (Day 4)

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:  
https://github.com/ShapeAI/Python-and-Machine-Learning/blob/main/Numpy.ipynb

#Python #machine-learning 

How to Fighting Financial Crime with Machine Learning Like a pro

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?

#machine-learning 

Course PYTHON AND MACHINE LEARNING for Beginner: Python Contd. (Day 2)

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:  
https://github.com/ShapeAI/Python-and-Machine-Learning/blob/main/Data_Types_Operators.ipynb

#Python #machine-learning 

Hands-On Guide To Word Embeddings Using GloVe

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/

#artificial-intelligence #machine-learning 

Logistic Regression Details Pt 3: R-squared and p-value

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.

#machine-learning 

Logistic Regression Details Pt 2: Maximum Likelihood

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 

Learn About Saturation Modeling and Bias Statistics

In this video we will learn about saturation modeling and deviation statistics.

#statquest #statistics #machine-learning 

Learn Bayes's Theorem | An Intuitive and Short Explanation of Bayes's

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 

Neural Networks and Machine Learning for Beginner

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.
 

#machine-learning #Neural-Networks

Learn About Decision Tree Regression Algorithm in Python

In this video, you will learn about decision tree regression algorithm in python Other important playlists
 

#decisiontree  #regression  #python #machine-learning 

Here Are All The Machine Learning Algorithms

Mathematics for machine learning will teach you all of the maths you need for machine learning. And it's available for free!

#machine-learning #algorithm 

Here Is one Of The Best Machine Learning Books

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.

#machine-learning