Why Use Python For AI And Machine Learning?

Artificial Intelligence (AI) and Machine Learning (ML) are the new blacks of the technology domain. The development of these technologies has been overwhelming, and it has sent strides across different industries. The growing applications of AI and ML across the business spectrum puts forward a new wave of change that the industry is undergoing. While most of the tech discussion hang around AI and ML application, we also need to have a look at powerful programming languages like Python that backs the algorithms and coding instigating seamless functioning of AI and ML models. The prevalence of this programming language in AI and ML is worth the attention.

Why ML and AI with Python?

If we have to pick one of the most popular and universal programming languages, Python will win with the majority. It has emerged as the holy grail for the developers.

Role of Python in AI and ML

Python's role in AI and ML is vital. Its features, like ease of use, readability, and versatility, make it apt for Artificial Intelligence and Machine Learning. Here are some of the key features that make Python a popular programming language:

1. Versatility: The extensive libraries of Python and its framework provide various tools for tasks like NLP, data manipulation, and Machine Learning. This usage flexibility gives the AI developers the leverage to integrate different components and develop an intricate AI system.

2. Powerful Libraries and Frameworks: Another significant feature of Python is its host of powerful libraries. These are specially designed for AI development. For example, Keras, TensorFlow, PyTorch, and Scikit-learn. These have pre-built algorithms for deep learning, neural network, and data analysis. Thus it saves time that goes into the implementation of AI models.

3. Readability and Ease of Use: Python has a simple and readable syntax. Thus making it a beginner-friendly language. Clear coding enhances collaboration among AI developers, thus making it easier to update AI projects. The seamless integration that Python offers makes AI development easier. It simplifies AI applications' interaction with different data sources like APIs, thereby ensuring better decision-making.

4. Rapid Prototyping and Experimentation: The fast-prototyping capabilities of Python make it ideal for AI experimentation. The Jupyter notebooks facilitate faster testing and development, thus aiding AI developers to experiment with different models and algorithms. This catalyzes the AI development process.

5. Support for Big Data Processing: Powerful Python libraries like pandas and Numpy enable efficient handling and analysis of large volumes of data. This helps in the efficient analysis of data. With Python for AI, the developers have the leverage to process and work on large volumes of data, derive meaningful insights, and train the AI models on extensive data sets.

6. Availability of AI-specific Tools: The wide range of AI-specific tools and frameworks present with Python makes it easier to implement AI techniques. Some of the examples include:

NLTK and spaCy – It provides Natural Language Processing capabilities

OpenCV- It offers computer vision functionalities.

With the help of these tools, the developers have the leverage to experiment with the development of AI applications.

"AI with Python is a powerful and effective way to develop AI-driven applications. Its versatility, and powerful libraries makes Artificial Intelligence, and Machine Learning with Python easy," quotes Mr. Naveen Jain, CEO Pickl.AI.

In the times to come, the AI and ML application horizons will expand. So, this is the right time to master the skills and enhance proficiency in Python. If you also want to know how Machine Learning is implemented with Python or you want to learn AI with Python, connect with Pickl.AI. In addition, you can also enroll for Python for Data Science course, wherein you will get a comprehensive overview of the application of Python, thus empowering you to create innovative, sophisticated and flawless AI solutions.

Disclaimer: This article is a paid publication and does not have journalistic/editorial involvement of Hindustan Times. Hindustan Times does not endorse/subscribe to the content(s) of the article/advertisement and/or view(s) expressed herein. Hindustan Times shall not in any manner, be responsible and/or liable in any manner whatsoever for all that is stated in the article and/or also with regard to the view(s), opinion(s), announcement(s), declaration(s), affirmation(s) etc., stated/featured in the same. 

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Updated: 13 Jun 2023, 04:44 PM IST

The 7 Best Python Libraries And Tools For Web Scraping

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There are several Python libraries and frameworks to extract data from the web. Everyone starts with a particular tool until they realize it might not be the best fit for their next project. Although it's highly unlikely that you'll use all the Python tools in a single project, you should know which ones to keep handy in your web scraping toolbox.

Here are the best Python libraries, frameworks, and other tools that will help you scrape data from the web, effortlessly.

1. Beautiful Soup

Starting off the list with the best web scraping library for beginners: Beautiful Soup. It's essentially a tool that extracts data from parsed HTML and XML files by transforming the document into a Python object.

The "beauty" of Beautiful Soup lies in its simplicity. It's easy to set up and you can get started with your first web scraping project within minutes. Beautiful Soup uses a hierarchical approach to extracting data from an HTML document. You can extract elements using tags, classes, IDs, names, and other HTML attributes.

Expecting more from Beautiful Soup would be taking it too far, though. There's no built-in support for middlewares and other advanced functionalities such as proxy rotation or multi-threading. With Beautiful Soup, you need libraries to send HTTP requests, parse the downloaded document, and export the scraped information to an output file.

2. Requests

requests is undoubtedly the most used Python library for handling HTTP requests. The tool stands up to its tagline: HTTP for Humans™. It supports multiple HTTP request types, ranging from GET and POST to PATCH and DELETE. Not only this, you can control almost every aspect of a request, including headers and responses.

If that sounds easy, rest assured as requests also caters to advanced users with its multitude of features. You can play around with a request and customize its headers, upload a file to a server using POST, and handle timeouts, redirects, and sessions, among other things.

requests is usually associated with Beautiful Soup when it comes to web scraping as other Python frameworks have built-in support for handling HTTP requests. To get the HTML for a web page, you'd use requests to send a GET request to the server, then extract the text data from the response and pass it on to Beautiful Soup.

3. Scrapy

As the name suggests, Scrapy is a Python framework for developing large-scale web scrapers. It's the swiss-army-knife to extract data from the web. Scrapy handles everything from sending requests and implementing proxies to data extraction and export.

Unlike Beautiful Soup, the true power of Scrapy is its sophisticated mechanism. But don't let that complexity intimidate you. Scrapy is the most efficient web scraping framework on this list, in terms of speed, efficiency, and features. It comes with selectors that let you select data from an HTML document using XPath or CSS elements.

An added advantage is the speed at which Scrapy sends requests and extracts the data. It sends and processes requests asynchronously, and this is what sets it apart from other web scraping tools.

Apart from the basic features, you also get support for middlewares, which is a framework of hooks that injects additional functionality to the default Scrapy mechanism. You can't scrape JavaScript-driven websites with Scrapy out of the box, but you can use middlewares like scrapy-selenium, scrapy-splash, and scrapy-scrapingbee to implement that functionality into your project.

Finally, when you're done extracting the data, you can export it in various file formats; CSV, JSON, and XML, to name a few.

Scrapy is one of the many reasons why Python is the best programming language for anyone into web scraping. Setting up your first Scrapy project can take some time, especially if you don't have experience with Python classes and frameworks. Scrapy's workflow is segregated into multiple files and for beginners, that might come off as unsolicited complexity.

4. Selenium

If you're looking to scrape dynamic, JavaScript-rendered content, then Selenium is what you need. As a cross-platform web testing framework, Selenium helps you render HTML, CSS, and JavaScript and extract what's required. You can also mimic real user interactions by hard-coding keyboard and mouse actions, which is a complete game-changer.

Selenium spawns a browser instance using the web driver and loads the page. Some popular browsers supported by Selenium are Google Chrome, Mozilla Firefox, Opera, Microsoft Edge, Apple Safari, and Internet Explorer. It employs CSS and XPath locators, similar to Scrapy selectors, to find and extract content from HTML elements on the page.

If you're not experienced with Python but know other programming languages, you can use Selenium with C#, JavaScript, PHP, Perl, Ruby, and Java.

The only limitation is since Selenium launches a web browser in the background, the resources required to execute the scraper increase significantly, in comparison to Scrapy or Beautiful Soup. But given the additional features Selenium brings to the table, it's completely justified.

5. Urllib

The Python urllib library is a simple yet essential tool to have in your web scraping arsenal. It lets you handle and process URLs in your Python scripts.

An apt practical application of urllib is URL modification. Consider you're scraping a website with multiple pages and need to modify a part of the URL to get to the next page.

urllib can help you parse the URL and divide it into multiple parts, which you can then modify and unparse to create a new URL. While using a library to parse strings might seem like an overkill, urllib is a lifesaver for people who code web scrapers for fun and don't want to get into the nitty gritty of data structures.

Also, if you want to examine a website's robots.Txt, which is a text file containing access rules for the Google crawler and other scrapers, urllib can help you with that too. It's recommended that you follow a website's robots.Txt and only scrape the pages that are allowed.

6. JSON, CSV, and XML Libraries

Since Beautiful Soup or Selenium don't have built-in features to export the data, you'd need a Python library to export the data into a JSON, CSV, or XML file. Luckily, there are a plethora of libraries you can do to achieve this, and the most basic ones are recommended, namely json, csv, and xml for JSON, CSV, and XML files, respectively.

Such libraries allow you to create a file, add data to it, and then finally, export the file to your local storage or remote server.

7. MechanicalSoup

MechanicalSoup? Is this a cheap Beautiful Soup ripoff? No. Inspired by Mechanize and based on Python requests and Beautiful Soup, MechanicalSoup helps you automate human behavior and extract data from a web page. You can consider it halfway between Beautiful Soup and Selenium. The only catch? It doesn't handle JavaScript.

While the names are similar, MechanicalSoup's syntax and workflow are extremely different. You create a browser session using MechanicalSoup and when the page is downloaded, you use Beautiful Soup's methods like find() and find_all() to extract data from the HTML document.

Another impressive feature of MechanicalSoup is that it lets you fill out forms using a script. This is especially helpful when you need to enter something in a field (a search bar, for instance) to get to the page you want to scrape. MechanicalSoup's request handling is magnificent as it can automatically handle redirects and follow links on a page, saving you the effort of manually coding a section to do that.

Since it's based on Beautiful Soup, there's a significant overlap in the drawbacks of both these libraries. For example, no built-in method to handle data output, proxy rotation, and JavaScript rendering. The only Beautiful Soup issue MechanicalSoup has remedied is support for handling requests, which has been solved by coding a wrapper for the Python requests library.

Web Scraping in Python Made Easier

Python is a powerful programming language for scraping the web, no doubt, but the tools used are only part of the problem. The most prominent issue people face when coding a scraper is learning HTML document hierarchy.

Understanding the structure of a web page and knowing how to locate an element quickly is a must if you want to develop advanced web scrapers.

Galileo Launches LLM Diagnostics And Explainability Platform To Reduce Model Hallucinations

SAN FRANCISCO, June 21, 2023 — Galileo, a machine-learning (ML) data intelligence company for LLMs and Computer Vision, announced a suite of new tools called Galileo Llm Studio — now available for waitlist signups. As organizations of all sizes and across industries begin to consider the potential applications of generative AI, it is more important than ever for data science teams to have access to tools to quickly and easily evaluate the results of these Large Language Models (LLMs) and optimize their performance.

Specially designed for high-performance data science teams, the Galileo LLM Studio will serve as a one-stop platform for LLM analysis and prompt management. Individual LLM Studio users will have access to two free tools to improve LLM performance and accuracy: the Galileo Prompt Inspector, which enables users to identify potential model hallucinations; and the Galileo LLM Debugger, which allows users to fine-tune LLMs with their own proprietary data.

"Adapting LLMs to specific real-world applications depends on data more than ever before. Today, an organization's data is its only differentiator. Galileo LLM Studio acts as a data force multiplier, enabling data scientists to fine-tune these models and use the best prompts with the right amount of context, to set appropriate guardrails and prevent hallucinations," said Yash Sheth, Galileo co-founder and chief product officer.

"A major factor in getting the best outputs from LLMs comes down to exploring the semantic search space of possible inputs that resolve to the accurate user intent," said Atindriyo Sanyal, Galileo co-founder and chief technology officer and an early engineer at Apple working on Siri, allowing iPhone app developers to build powerful natural language processing (NLP) applications leveraging Siri. "I started my career in artificial intelligence over a decade ago. And although models today are way more advanced and powerful, the principles determining the quality of language model outputs remain the same: preventing model hallucinations and reducing model bias by leveraging consensus from sources that are not biased by the model and data at hand. We designed Galileo LLM Studio with those principles in mind."

"The introduction of Galileo's LLM Studio has opened up exciting new possibilities across industries. Its comprehensive tools allow customers to fine-tune large language models using their own unique data, while effectively identifying and managing model hallucinations. This isn't just a time-saver; it's a game-changer, allowing companies to leverage generative AI more effectively and confidently and providing the right resources to ensure model accuracy and reliability," said Dharmesh Thakker, general partner at Battery Ventures, the technology-focused investment firm backing Galileo.

Galileo Prompt Inspector

With the Galileo Prompt Inspector, users can quickly and efficiently identify potential model hallucinations, or overconfident, incorrect predictions from the LLM. The Inspector provides a Hallucination Likelihood Score — surfacing where the model is hallucinating, or generating unreliable and spurious output, including factual inaccuracies. With this information, users are able to more quickly address hallucinations and other errors in their model, reducing the likelihood of customers encountering misinformation or other incorrect model output. Users will also be able to create, manage and evaluate prompts in one platform, then transfer prompts from Galileo to the application of their choice, such as Langchain, OpenAI, HuggingFace and many more.

Additional built-out product features in the Galileo Prompt Inspector include:

The ability to organize prompt projects, runs and queries to LLMs in one place;

Support for OpenAI and Hugging Face models;

Collaboration features to streamline prompt engineering across multiple teams;

Helps minimize the costs of prompt engineering by monitoring and estimating cost of calls to OpenAI while providing key signals on what isn't working; and

A/B comparison of prompts and their results.

Galileo LLM Debugger

With the Galileo LLM Debugger, users will be able to fine-tune LLMs with their own proprietary data, ensuring a high-performing model. Today, this process is frequently done manually with spreadsheets and Python scripts working with human-curated labels, which is time-intensive, costly and error-prone. Data science teams can connect LLMs directly to the Galileo LLM Debugger to instantly uncover and fix cumbersome errors in their dataset where their models are struggling; leading to better performing models faster, increasing team efficiency and reducing costs across the board.

Potential use cases of the Galileo LLM Debugger include:

A data science team in healthcare wants to build a smarter patient record summarizer. Leveraging an open-source LLM would yield generic results. Therefore, the team will need to train the LLM on their proprietary EMR data.

A consumer-facing enterprise wants to build a chatbot for answering their customer's questions related to their business, services and product offerings.

A financial institution wants to summarize company data (financials, macro trends and industry-wide news) to make effective risk assessments on lending to that business.

Galileo LLM Studio Waitlist and Webinar Demo

For more information on Galileo LLM Studio, sign up for the waitlist here and register for the Debugging LLMs: Best Practices for Better Prompts and Data Quality webinar on June 22nd here.

About Galileo

Galileo's mission is to create data intelligence tools for unstructured data ML practitioners. With more than 80% of the world's data being unstructured and recent model advancements massively lowering the barrier to utilizing the data for enterprise ML, there is an urgent need for the right data-focused tools to build high performing models fast. Galileo is based in San Francisco and backed by Battery Ventures, Walden Catalyst and The Factory.

Source: Galileo

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