Machine Learning & Artificial Intelligence | Data Science Free Courses

Artificial Intelligence and Chatbots 101 👇👇 https://t.me/machinelearning_deeplearning/234

Top 10 Machine Learning Algorithms 👇👇 https://t.me/pythonspecialist/241

FIVE TOP IMAGE RECOGNITION SOFTWARE 2024 Image recognition software is a computer program that uses deep learning algorithms and AI to identify objects, scenes, people, text, and activities in images and videos. The software works by extracting pixel features from an image, preparing labeled images for training, training the model to recognize images, and then using the trained model to identify objects in new images. 1. Meltwater Image Search: Meltwater's image recognition software offers social media monitoring capabilities with AI-powered computer vision models. It can search for images in non-verbal and non-textual content, detect demographics, celebrities, scenes, objects, and visual emotions. It also includes features like optical character recognition (OCR) and logo detection. 2. Google Reverse Image Search: Google's Reverse Image Search allows users to find more information about images by uploading them. It can identify objects in the image, provide similar images, and show websites with the same or similar images. 3. Clarifai: Clarifai's AI-powered computer vision software enables processing of images, videos, texts, and audio files. It can filter unwanted content, recommend relevant products, and manage unstructured data. Customizable AI models can be created for specific use cases. 4. Imagga: Imagga offers image recognition tools for sorting, organizing, and displaying images based on tags or categories. Its powerful API enables features such as product discoverability, facial recognition, and automated thumbnail generation. 5. Amazon Rekognition: Amazon Rekognition is a user-friendly image recognition software that provides insights on still images and videos. It offers features like activity recognition, face analysis, content moderation for unsafe and inappropriate content, and text detection for street names, image captions, and license plate numbers.

They predicted yesterday the DUMP of Bitcoin Already in the channel published the dates of the next BTC PUMP! Click 👉 CHECK NEXT PUMP DATES 👈 Click 👉 CHECK NEXT PUMP DATES 👈 Click 👉 CHECK NEXT PUMP DATES 👈 JOIN FAST! Only the first 1000 people will be accepted! 🔥

Ad 👇👇

Exciting Opportunity Alert! 🚀 NextLevel is Hiring Fresher, Experience Candidate🔥 Job Details: 🌐 Role: Software Developer 💰 Salary: Up to 20 LPA 🎓 Qualification: Any Graduation 📅 Graduation Year: 2014 - 2024 🌍 Location: Pan India 📣 Test Date: To be notified via Email Note: ✅💥 🎓 Limited Seats! Selection on a First Come First Serve Basis. 🔗 Apply Here: https://next-level.onelink.me/vJGp/dhx7djq9 Important Note - Registration is Mandatory for Test Link Don't miss out on this incredible opportunity!

Follow the Data Science and Machine Learning channel on WhatsApp: https://whatsapp.com/channel/0029Va8v3eo1NCrQfGMseL2D

AI Engineer Roadmap 👇👇 https://t.me/generativeai_gpt/15

Learn Data Science in 2024 𝟭. 𝗔𝗽𝗽𝗹𝘆 𝗣𝗮𝗿𝗲𝘁𝗼'𝘀 𝗟𝗮𝘄 𝘁𝗼 𝗟𝗲𝗮𝗿𝗻 𝗝𝘂𝘀𝘁 𝗘𝗻𝗼𝘂𝗴𝗵 📚 Pareto's Law states that "that 80% of consequences come from 20% of the causes". This law should serve as a guiding framework for the volume of content you need to know to be proficient in data science. Often rookies make the mistake of overspending their time learning algorithms that are rarely applied in production. Learning about advanced algorithms such as XLNet, Bayesian SVD++, and BiLSTMs, are cool to learn. But, in reality, you will rarely apply such algorithms in production (unless your job demands research and application of state-of-the-art algos). For most ML applications in production - especially in the MVP phase, simple algos like logistic regression, K-Means, random forest, and XGBoost provide the biggest bang for the buck because of their simplicity in training, interpretation and productionization. So, invest more time learning topics that provide immediate value now, not a year later. 𝟮. 𝗙𝗶𝗻𝗱 𝗮 𝗠𝗲𝗻𝘁𝗼𝗿 ⚡ There’s a Japanese proverb that says “Better than a thousand days of diligent study is one day with a great teacher.” This proverb directly applies to learning data science quickly. Mentors can teach you about how to build a model in production and how to manage stakeholders - stuff that you don’t often read about in courses and books. So, find a mentor who can teach you practical knowledge in data science. 𝟯. 𝗗𝗲𝗹𝗶𝗯𝗲𝗿𝗮𝘁𝗲 𝗣𝗿𝗮𝗰𝘁𝗶𝗰𝗲 ✍️ If you are serious about growing your excelling in data science, you have to put in the time to nurture your knowledge. This means that you need to spend less time watching mindless videos on TikTok and spend more time reading books and watching video lectures. Join @datasciencefree for more ENJOY LEARNING 👍👍

🚀There is news that a young guy together with a team of the best programmers in Europe, invented a unique way to earn money, thanks to which everyone can earn from 300 dollars a day, having only a smartphone. 🔥 Together they created a Telegram channel, a closed community, where they tell about their strategy. Training is fast and easy, absolutely everyone will master it, you do not need special skills and knowledge. 👌The creator of the channel told us that he earns from 1000 dollars a day and it is absolutely real and available to everyone. He was able to change his life, drives expensive cars, travels, buys himself anything he wants and he assures that he can help to reach a high income to all those in need. ☄️Now there is a new recruitment for training in the team, all you need to do is to subscribe to his Telegram channel, hurry up, the number of places is limited. 👉 https://t.me/+m2VfkimmwUU5OWE1 

Ad 👇👇

Machine Learning with Python Free Course 👇👇 https://www.freecodecamp.org/learn/machine-learning-with-python/

©How fresher can get a job as a data scientist?© Job market is highly resistant to hire data scientist as a fresher. Everyone out there asks for at least 2 years of experience, but then the question is where will we get the two years experience from? The important thing here to build a portfolio. As you are a fresher I would assume you had learnt data science through online courses. They only teach you the basics, the analytical skills required to clean the data and apply machine learning algorithms to them comes only from practice. Do some real-world data science projects, participate in Kaggle competition. kaggle provides data sets for practice as well. Whatever projects you do, create a GitHub repository for it. Place all your projects there so when a recruiter is looking at your profile they know you have hands-on practice and do know the basics. This will take you a long way. All the major data science jobs for freshers will only be available through off-campus interviews. Some companies that hires data scientists are: Siemens Accenture IBM Cerner Creating a technical portfolio will showcase the knowledge you have already gained and that is essential while you got out there as a fresher and try to find a data scientist job. Credits: https://t.me/datasciencefun

Who's here?  We've asked for a free link to a paid channel, for our subs. x2-x3 Signals here 👉 CLICK HERE TO JOIN 👈 👉 CLICK HERE TO JOIN 👈 👉 CLICK HERE TO JOIN 👈 ❗️JOIN FAST! FIRST 1000 SUBS WILL BE ACCEPTED