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8 Best Machine Learning Tools For Data Scientists

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Machine learning (ML) has changed how humans interact with machines, technologies, and data. What started as a niche industry has now grown into a billion-dollar market. ML is already part of several industries, including healthcare, manufacturing, finance, retail, and entertainment. It has become crucial for this business to embrace machine learning to increase revenue, cut costs, and automate operations. 

Netflix saved $1 billion because of its ML algorithm for the combined effect of content recommendations and personalization. And 65% of companies planning to adopt machine learning say the technology will help them in decision-making. 

This article will feature the eight best machine learning tools for data scientists in any industry. 

1. Pandas

pandas logo

Pandas is an open-source data manipulation and analysis library for Python. It provides easy-to-use data structures and analysis tools for handling structured data, like tabular or time series data. With Pandas, data scientists can perform various tasks such as data cleaning, exploration, aggregation, and visualization.

Pros: 

  • Built for Python:
  • Excellent representation of data
  • Less coding done, more work accomplished
  • Efficient handling of massive data
  • Extensive feature set
  • The flexibility of data and easy customization

Cons:

  • It has a complex syntax that is not always compatible with Python
  • Steep learning curve
  • Poor documentation
  • Poor 3D matrix compatibility

2. NumPy 

numpy logo

NumPy stands for “Numerical Python.” It is a powerful open-source numerical computing library for Python. NumPy enables efficient mathematical operations on large, multi-dimensional arrays and supports various mathematical and logical operations.

Pros:

  • Numpy arrays consume less memory space. It provides better runtime speed when compared with similar data structures in Python.
  • Numpy supports some specific scientific functions, such as linear algebra. 
  • Because of their similar functionalities, it is an excellent substitute for MATLAB, OCTAVE, etc.
  • NumPy is perfect for data analysis.

Cons:

  • Numpy is not suitable for large-scale distributed computing
  • More efficient than some other libraries.
  • Numpy does not support GPU acceleration, which can be a limitation for some applications.

3. Matplotlib

Matplotlib website

Matplotlib is an open-source data visualization library for Python. It provides comprehensive functions and tools for creating high-quality static, animated, and interactive visualizations. Matplotlib allows data scientists, researchers, and developers to represent their data in various forms, such as line plots, scatter plots, bar plots, histograms, pie charts, and more.

Pros:

  • Versatile and accessible
  • Customizable
  • Good documentation 
  • A universal tool that plugs into many backends

Cons:

  • Steep learning curve
  • Users need to learn Python programming before using the tool
  • Users need to understand the syntax of Matplotlib, which is based on the software, MATLAB

4. Scikit Learn

Scikit-learn website

Scikit-learn is a machine learning tool built on top of other scientific Python libraries like NumPy, SciPy, and Matplotlib. It provides a comprehensive suite of tools and algorithms for various machine learning tasks, including classification, regression, clustering, dimensionality reduction, and model selection.

Pros: 

  • User-friendly and handy tool that can do multiple things such as predicting customer behavior, and creating neuroimage.
  • It is easy and free to use.
  • The contributor and the international online community update the Scikit Learn library.
  • Scikit learn library provides the API documentation for the user who wants to integrate the algorithm with his platform.
  • Extensive documentation 

Cons: 

  • Scikit-learn is not the best choice for in-depth learning.

5. TensorFlow

tensorflow website

TensorFlow is another machine learning library for Python. It works with NumPy, SciPy, and Matplotlib. It’s an open-source machine learning framework developed by Google. In addition, it facilitates the development and deployment of machine learning models, particularly neural networks.

Pros:

  • Features various classification, regression, and clustering algorithms
  • Models are trained and tested on different datasets than one used for preparing data using a train-test split
  • Implements the non-neural net-based algorithm

Cons:

  • TensorFlow’s frequent updates increase the overhead for users to install and bind it with the existing system.
  • It uses homonyms with varying meanings, making it inconsistent with its usability.
  • TensorFlow has low speed compared with other machine learning tools. 
  • It offers little support for Windows Operating System users. 

6. PyTorch

pytorch website

One of the critical features of PyTorch is its dynamic computational graph, which allows users to define and modify models on-the-fly during runtime. This dynamic nature makes experimenting with different architectures, control flow, and algorithms easy, making PyTorch particularly popular among researchers and developers.

Pros:

  • Cloud support
  • Considered as NumPy extension of GPUs
  • Easy to debug and understand

Cons: 

  • It was released in 2016, so it’s new compared to others and has fewer users.
  • Absence of monitoring and visualization tools 
  • Smaller developer community is small compared to other frameworks.

7. NLTK

NLTK website

NLTK Stands for Natural Language Toolkit. It is used to work with human language data. NTLK’s libraries and programs for symbolic and statistical natural language processing for English written in Python.

Pros:

  • NLTK fully supports the English language
  • It consists of algorithms such as tokenizing, parts of speech, stemming, topic segmentation
  • Efficient at analyzing large datasets

Cons:

  • NLTK can be clunky and slow if you need to familiarize yourself with NLP.
  • It’s more academic because of its origins in teaching and research
  • It may not provide out-of-the-box thinking for some innovative web processes and startup needs.

8. Tableau

machine learning tools example

Tableau is a popular data visualization and business intelligence software that allows users to explore and analyze data through interactive visualizations, dashboards, and reports. It provides a user-friendly interface and drag-and-drop approach to create visually appealing and interactive visualizations without extensive coding or programming knowledge.

With Tableau, users can connect to various data sources, including spreadsheets, databases, cloud services, and big data platforms. The software supports multiple data types and offers powerful data blending and transformation capabilities, enabling users to clean, combine, and reshape data for analysis.

Pros:

  • Provides beautiful dashboards and reports
  • Automate Reporting
  • Perform ETL(Explore, Transform, and Load) operations quickly

Cons: 

  • Tableau is expensive.
  • The steeper learning curve for advanced features
  • Performance limitations in large datasets. 
  • Limited statistical analysis and modeling
  • Limited customization options

Conclusion

The machine learning tools discussed in this article offer valuable resources and capabilities for data scientists. Each agency brings unique strengths and features, empowering users to efficiently tackle complex data analysis, model training, and predictive tasks.

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6 Companies That Use Chatbots For Marketing and Customer Service

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Chatbots have emerged as powerful tools that bridge the gap between humans and machines. These intelligent virtual assistants have transformed how we interact with technology, providing seamless and engaging conversational experiences across the web. We will explore popular chatbots that have made their mark on the web. These chatbots have unique capabilities and applications like customer service, e-commerce, healthcare, and productivity.

What are Chatbots? 

a robot looking at the screen

Chatbots are computer programs that simulate human-like conversations through text or voice interactions. They use artificial intelligence (AI) technologies such as natural language processing (NLP) and machine learning to understand and respond to user queries and requests. 

Chatbots can be deployed across various platforms, including websites, messaging applications, and voice assistants. Today, chatbots are widely used as a tool for customer service, information retrieval, personal assistants, and more. 

How Do Chatbots Work?

images of headphones, cellphone, smartwatch and the name SIri

At the center of chatbot technology lies NLP, the same technology that enables voice recognition systems used by virtual assistants like Apple’s Siri and Microsoft’s Cortana.

Chatbots process the user’s text prompt before responding based on algorithms that interpret and identify what the user said. Then, it infers what they mean or want and determines appropriate responses based on this information. 

While chatbot technology differs from NLP technology, the former can only advance as quickly as the latter. With continued developments in NLP, chatbots remain independent of algorithms’ ability to detect the subtle nuances in written and spoken dialogue.

This is where most applications of NLP need help, not just chatbots. Any app that relies upon a machine’s ability to parse human speech will likely struggle with the complexities inherent in elements of speech, such as metaphors and similes. Despite these considerable limitations, chatbots are becoming increasingly sophisticated and responsive. 

To further show chatbots’ benefits, six companies use advanced technology for marketing and customer support. 

1. Endurance Robots 

endurance chatbot
Image Source: Endurance

Russian technology company Endurance developed its companion chatbot. Some patients with dementia can’t engage in meaningful conversations anymore. But many people with the disease retain much of their conversational abilities as their illness progresses. However, the shame and frustration that many people with dementia experience often make routine, everyday talks with even close family members challenging.

The Endurance chatbot aims to determine differences in conversational models that may indicate immediate recollection problems. Even though this is quite an ambitious technical challenge for an NLP-based system.

Since the chatbot is a cloud-based solution, physicians and family members can review communication logs taken from the bot. The process helps identify potential degradation of memory function and communicative obstacles that could signify deterioration of the patient’s condition.

2. Casper

casper chatbot

Enter the fantastic Insomnobot 3000, a conversational agent that aims to give insomniacs someone to talk to while the rest of the world rests easy. It was created by a digital agency called ACNE in partnership with the mattress company Casper.

Insomnobot 3000 is available through text messaging and is designed to keep users company during the late hours when they may be struggling with sleeplessness. The chatbot engages in casual conversations, provides distractions, shares stories, and offers a listening ear to help alleviate feelings of loneliness or anxiety accompanying insomnia.

While Insomnobot 3000 can provide some comfort and distraction, it is essential to note that the chatbot is not a replacement for professional medical advice or treatment. If you are experiencing chronic or severe sleep issues, it is still best to consult a healthcare professional for proper evaluation and guidance.

3. Marvel

marvel chatbot

Marvel’s cinematic universe is expanding even faster than the boundaries of the observable universe itself. Interestingly, Marvel turned to chatbots to further immerse fans in their favorite comic-book storylines in real life. Marvel’s chatbot that lets comic-book geeks talk to Star-Lord himself is also quite good.

4. UNICEF

unicef ureport chatbot

International child advocacy nonprofit United Nations Children’s Fund (UNICEF) also uses chatbots to help people living in developing countries discuss the most urgent needs in their communities. The bot, called U-Report, focuses on large-scale data gathering via polls – this isn’t a bot for the talkative. U-Report regularly sends out prepared polls on various urgent social issues. The users, known as “U-Reporters,” can respond with their input. Then, UNICEF uses this feedback as the basis for potential policy recommendations.

5. MedWhat 

medwhat chatbot

If you have WebMD bookmarked for similar reasons, visiting our MedWhat site or app might be worth checking out. MedWhat applies data science techniques to healthcare data stored in 2D medical images, 3D medical images, electronic health records, and wearable devices.

This chatbot aims to make medical diagnoses faster, easier, and more transparent for patients and physicians – think of it as an intelligent version of WebMD that you can talk to. MedWhat runs under a sophisticated machine learning system that offers accurate responses to user questions based on behaviors that it “learns” by interacting with human beings. The bot also draws upon vast medical research and peer-reviewed scientific papers to expand its already considerable medical expertise.

6. Roof AI

roofai chatbot

Roof Ai is a chatbot that helps real-estate marketers to automate interacting with potential leads and lead assignments via social media. The bot identifies potential leads through Facebook, then responds almost instantaneously in a friendly and conversational tone that resembles a real person’s. Based on user input, Roof Ai prompts potential leads to provide more information before automatically assigning the lead to a sales agent. 

The chatbot is still under development, though interested users can reserve access to Roof Ai via the company’s website.

Conclusion

Chatbots have emerged as powerful tools in communication and customer service. Their ability to simulate human-like conversations and provide instant assistance has revolutionized how businesses and individuals interact with technology. While chatbots excel in many areas, it’s important to acknowledge their limitations. They often lack the emotional intelligence and empathy of a human counterpart. However, the ongoing development can improve chatbot technology to bridge these gaps and provide even more personalized user experiences.

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10 Viral Ad Campaigns and What We Can Learn from Them

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On the internet, anything can go viral. But that doesn’t mean that there aren’t tried and tested recipes for success. Here, we break down some of the most viral ad campaigns in the last few years. 

As you’ll learn, the top viral marketing campaigns use a mix of social media listening, trendspotting, creativity, and sincerity. From video landing page ideas to social media marketing, these campaigns integrated the best elements that took the internet by storm.  

1. Apple: Shot on iPhone

viral ad campaigns example

One of the best ways to go viral online is to call for user-generated content. And Apple’s annual Shot on iPhone ad campaigns just gets that sweet spot right. 

What we can learn from the campaign: No true ads person will just put out a call for content and call it a day. Give the audience something that they want to make content with, and you’re golden. With Apple, their Iphone’s features are enough to make a whole generation of individuals experiment with their creativity. 

2. Gillette: #SayPubic Campaign

viral ad campaigns examples

In 2022, Gillette Venus released The Pube Song to start a conversation and normalize pubic hair in women’s bodies. It’s one of the brand’s more viral ad campaigns because of its playful tone and strong self-love message.  

What we can learn from the campaign: It can be a calculated marketing risk to talk about what’s taboo. But their song campaign manages to balance the weighty message with a lighthearted delivery that doesn’t take away from the issues it’s discussing. 

3. Airbnb: Ukraine

viral ad campaigns example

Airbnb’s Ukraine campaign is also a moment that smashed everyone’s screens. In 2022, the company announced that global users could lend support to Ukrainians by renting out Airbnbs. 

What we can learn from the campaign: Sometimes, the best marketing move is to just rise to the occasion, get in touch with your own humanity, and do something nice. In addition, viral ad campaigns like this allowed millions to extend tangible support to Ukrainians. 

4. Netflix: Wednesday

viral ad campaigns example

Netflix is always goofing around with its marketing campaigns, but they didn’t come to play with their Wednesday promotions. Specifically, their release of ‘The Thing’ upon random New Yorkers really lighted up everyone’s week. 

What we can learn from the campaign: It’s okay to be fun and think outside of the box! You don’t have to stick to the same strategies if you want to go viral. In fact, when you do things on a grand scale, the audience does the virality for you. 

5. Dunkin: Ben Affleck 

viral ad campaigns example

This recent Dunkin ad is short, but it’s comedy gold. It features the actor in a meta-scene where he talks about acting for a Meta ad. It’s short, snappy, and only 30 seconds, which may be why it quickly made rounds in social media. 

What we can learn from the campaign: It’s all about references here. This Dunkin campaign wouldn’t be effective if it weren’t for the well-known fact that Ben Affleck loves his Dunkin Donuts. Instead of brands simply ignoring this fact, it’s the perfect opportunity for them to collaborate and show the audience that they’re listening. 

6. Doritos: Jack’s New Angle

viral ad campaigns example

This star-studded ad uses the signature Dorito triangle as the star of the story. It combines the elements of music and food together, so the 2023 Doritos Super Bowl ad was truly on brand. 

What we can learn from the campaign

The ad works for the sheer spontaneity of it. Jack Harlow, Missy Elliot, and Elton John in one video? Now that’s star power. The narrative is also compact, with a playful and unexpected twist delivered at the end. 

7. Milo: Park Seo-joon

viral ad campaigns example

The Korean actor paired up with Milo Indonesia for a simple ad campaign. But there’s more. Milo also released a limited edition packaging that had netizens scrambling to get their hands on one. 

What we can learn from the campaign: We know that the K-wave is rising high, but that will only get your interactions so far. However, what made this viral marketing strategy work was that it had the actor plastered on the packaging. 

8. Rare Beauty: What are you made of?

viral ad campaigns example

Meanwhile, Rare Beauty’s ‘What are you made of’ campaign isn’t just about beauty and makeup. It’s about giving the community a safe space to speak about their lives. The curated content from Selena Gomez’ following is touching, but it also pays homage to Rare Beauty’s brand values. 

What we can learn from the campaign: Well, you could argue that Rare Beauty content always goes viral. But this specific campaign didn’t just blow up for no reason. For one, they used people with their authentic beauty stories. In fact, the spotlight on the Latino community makes this even sweeter. 

9. Dove: #DetoxYourFeed

viral ad campaigns example

This lengthy campaign by Dove guides parents on how to talk about complex body image issues for teenage girls. The company also released a short film to show how much social media can harm young girls. 

What we can learn from the campaign: Dove’s hard-hitting campaign hits us where it hurts. After doing body positivity campaigns with inclusive models from different backgrounds, they’re now addressing the root of the problem: social media. 

10. Mcdo: BTS 

viral ad campaigns example

Mcdo partnered with global K-pop artists BTS to release limited-edition BTS Meals with their signature purple color.

What we can learn from the campaign: We don’t think this campaign warrants any more explanation. But just so you know, employing artists with a global audience will always pay off when the product collaboration is affordable and accessible.

Also, did we mention that fans were selling the used packaging in online forums? It was a crazy time. To date, we think this is one of Mcdo’s most viral campaigns.  

And there you have it, some viral campaign examples to inspire your next marketing strategy. We just wanted to remind you in case this wasn’t clear yet, though. Viral ad campaigns work because the appeal isn’t surface-level.

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Top 8 Free AI Stock Trading Bots

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ai stock trading bot

The term artificial intelligence is currently making waves as it has proven to make lives easier. From writing to game development, it has become a massive part of doing business, and many are now discovering its varied uses. Stock trading isn’t exempt as we find many free AI stock trading bots, and we found the ten best just for you. Here they are:

1. SpeedBot

ai stock trading bot example

An advanced AI stock trading bot SpeedBot empowers traders with cutting-edge features and algorithms. It boasts speed and accuracy in analyzing market data, identifying patterns, and executing trades rapidly. It features an intuitive user interface that makes it suitable for beginners.

Experienced traders will enjoy its customizable settings that let them adapt the bot to their unique trading preferences. SpeedBot is a comprehensive solution that provides you with automated strategies and real-time market insights.

2. Pluto

ai stock trading bot example

If you’re looking to have advanced trading capabilities, Pluto is an excellent AI stock trading bot. It offers highly powerful algorithms and machine learning techniques to help you analyze data to identify potential trading opportunities.

Its user-friendly interface makes it an excellent option for newbies and experienced traders alike. It also lets them customize their trading strategies and risk preferences. It also gives you real-time market insights and automated trade executions.

3. Zorro

ai stock trading bot example

Offering a wide range of features, Zorro helps you develop and deploy trading strategies based on artificial intelligence and machine learning. Its key features include backtesting, optimization, and execution of trade algorithms. It has an extensive library of indicators and plugins that enable users to customize and fine-tune their strategies according to their specific needs.

Zorro is a robust and versatile platform that leverages the power of AI to optimize your stock trading performance. 

4. Composer

ai stock trading bot example

A free stock trading bot, Composer offers a wide array of advanced features for traders. It has an easy-to-use interface that allows users to create and deploy automated trading strategies powered by AI and machine learning. It lets you backtest your strategy with the use of historical data.

Composer also helps you with analyzing real-time market insights and executing your trades more effectively. Its algorithms help you identify potential trading opportunities, manage risks, and optimize your investment decisions.

5. EA Builder

ai stock trading bot example

A popular online platform, EA Builder allows you to build your own custom Expert Advisors (EAs) for automated stock trading. While its main focus is on forex trading, you can also use it for stock trading. The software lets you create EAs even without any knowledge of programming. 

With it, you can define trading rules, indicators, and conditions. EA Builders offers a free account, but you can avail yourself of all its best features when you pay a one-time fee of $97.

6. Options Road Backtester

ai stock trading bot example

A powerful tool, Options Road Backtester is designed for traders to backtest and analyze their trading strategies. Primarily for options trading, you can also use it for stock trading using options as part of your investment approach. It lets you input your trading strategy parameters and historical market data. 

This allows for simulating and evaluating the performance of your options strategies. It also gives you valuable insights into risk management, profitability, and potential outcomes that help you make informed decisions.

7. Capitalise.ai

ai stock trading bot example

Primarily a trading bot platform, Capitalise.ai may not fit the traditional definition of a standalone bot, but it has automated trading capabilities. It also offers the ability to create and deploy custom trading strategies without any knowledge of coding. You can set up rules, triggers, and conditions within the platform to automate your trading activities.

Capitalise has automation features that allow for executing trades based on predetermined criteria that make it comparable to a trading bot when speaking of functionality.

8. Equbot

ai stock trading bot example

Another AI-driven investment platform, Equbot blends AI technology with human expertise to analyze and select investment opportunities. It leverages machine learning and natural language processing to analyze vast amounts of data and identify potential investment opportunities in the stock market.

Equbot focuses on providing intelligent insights and actionable recommendations for your investment decisions. It takes into account financial statements, market trends, news sentiment, and other relevant data points to help you make decisions.

Factors to Consider When Choosing a Free AI Stock Trading Bot

Now that you’ve seen a list of free AI stock trading bots to choose from, how do you find the best one suited to your needs? While most of these bots offer similar features, some have tools that others don’t. Finding out which ones will suit you depends on your personal preferences and trading strategies and techniques. Below are a few considerations before getting one:

  • Features and Functionality – learn about the bot’s available tools and capabilities
  • Accuracy and Performance – assess its track record for reliable performance and predictions.
  • Ease of Use – check for the bot’s user-friendliness
  • Privacy and Security – make sure that it has proper safeguards in place and that your privacy and data are protected
  • Flexibility and Customization – know the extent to which the bot can be personalized to suit your trading preferences
  • Reviews and Reputation – check the feedback and reviews of other users
  • Terms and Conditions – review the bot’s terms and conditions and any potential limitations on its use

When you assess these factors, you can be sure to choose a free AI stock trading bot that will meet all your trading needs. It will also provide you with a seamless and user-friendly experience with reliable performance.

Final Thoughts

The availability of free AI stock trading bots gives traders an exciting opportunity to harness the power of AI in their investment endeavors. Exploring the list we made above gives you access to a wide range of functionalities. However, artificial intelligence isn’t as dependable as we want it to be.

To find the best AI stock trading bot, you’ll still need careful consideration. This is to ensure that your strategies, with the help of these bots, will result in more informed investment decisions in the ever-dynamic world of the stock market.

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