#ScikitLearn

[eduanta]

Creating a Machine Learning-Powered Recommendation System with FastAPI and Scikit-learn

🔍 Interested in building a recommendation engine? Learn how to use FastAPI and Scikit-learn to create and deploy a machine learning-powered recommendation system as a REST API. We offer help with:

Setting up FastAPI and Scikit-learn.

Developing the recommendation model.

Deploying your system as a REST API.

💬 Contact us on WhatsApp at +971501618774 for personalized support!

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Top 5 AI tools

TensorFlow: TensorFlow is an open-source machine learning framework developed by Google. It provides a comprehensive ecosystem of tools, libraries, and resources for building and deploying AI models. TensorFlow supports deep learning, neural networks, and other numerical computations, making it a widely used tool in the AI community.

PyTorch: PyTorch is another popular open-source deep learning framework that is widely adopted by researchers and practitioners. Developed by Facebook's AI Research Lab, PyTorch offers a dynamic computational graph that allows for easy model prototyping and debugging. It provides extensive support for neural networks and is known for its intuitive interface.

Scikit-learn: Scikit-learn is a widely used Python library for machine learning. It provides a range of supervised and unsupervised learning algorithms, including classification, regression, clustering, and dimensionality reduction. Scikit-learn offers a user-friendly interface, making it accessible for beginners while still providing advanced features for experienced practitioners.

Keras: Keras is a high-level neural network API written in Python. It serves as an interface to several deep learning frameworks, including TensorFlow and Theano. Keras simplifies the process of building and training neural networks by providing a user-friendly and intuitive API. It is widely used for rapid prototyping and experimentation in AI research and development.

Jupyter Notebook: Jupyter Notebook is an open-source web application that allows you to create and share documents containing live code, equations, visualisations, and narrative text. It supports multiple programming languages, including Python, R, and Julia, making it a popular choice for AI development. Jupyter Notebook provides an interactive and collaborative environment for data exploration, model development, and experimentation.

[excelworld]

[atozofsoftwareengineering]

Exploring Popular Machine Learning Tools and Their Impactful Case Studies

Hey friends! Check out this insightful blog on popular machine learning tools like #TensorFlow, #PyTorch, #ScikitLearn, #Keras, and #ApacheSparkMLlib. Explore their features, use cases, and how they enable us to build powerful machine learning models.

In recent years, the field of machine learning has witnessed remarkable growth and advancement, enabling transformative changes in various industries. One of the driving forces behind this progress is the availability of powerful machine learning tools. These tools facilitate the development and deployment of complex machine learning models, making it easier for researchers, data scientists, and…

[pandeypankaj]

Can somebody provide step by step to learn Python for data science?

Absolutely the right decision—to learn Python for data science. Segmenting it into something doable may be a good way to go about it honestly. Let the following guide you through a structured way.

1. Learning Basic Python

Syntax and semantics: Get introduced to the basics in syntax, variables, data types, operators, and some basic control flow.

Functions and modules: You will be learning how to define functions, call functions, utilize built-in functions, and import modules.

Data Structures: Comfortable with lists, tuples, dictionaries, and sets.

File I/O: Practice reading from and writing to files.

Resources: Automate the Boring Stuff with Python book.

2. Mastering Python for Data Science Libraries

NumPy: Learn to use NumPy for numerical operations and array manipulations.

Pandas: The course would revolve around data manipulation through the Pandas library, series, and data frames. Try out the cleaning, transformation, and analysis of data.

Familiarize yourself with data visualization libraries: Matplotlib/Seaborn. Learn to make plots, charts, and graphs.

Resources: 

NumPy: official NumPy documentation, DataCamp's NumPy Course

Pandas: pandas documentation, DataCamp's Pandas Course

Matplotlib/Seaborn: matplotlib documentation, seaborn documentation, Python Data Science Handbook" by Jake VanderPlas

3. Understand Data Analysis and Manipulation

Exploratory Data Analysis: Techniques to summarize and understand data distributions

Data Cleaning: missing values, outliers, data inconsistencies.

Feature Engineering: Discover how to create and select the features used in your machine learning models.

Resources: Kaggle's micro-courses, "Data Science Handbook" by Jake VanderPlas

4. Be able to apply Data Visualization Techniques

Basic Visualizations: Learn to create line plots, bar charts, histograms and scatter plots

Advanced Visualizations: Learn heatmaps, pair plots, and interactive visualizations using libraries like Plotly.

Communicate Your Findings Effectively: Discover how to communicate your findings in the clearest and most effective way.

Resource: " Storytelling with Data" – Cole Nussbaumer Knaflic.

5. Dive into Machine Learning

Scikitlearn: Using this package, the learning of concepts in supervised and unsupervised learning algorithms will be covered, such as regression and classification, clustering, and model evaluation.

Model Evaluation: It defines accuracy, precision, recall, F1 score, ROC-AUC, etc.

Hyperparameter Tuning: GridSearch, RandomSearch

For basic learning, Coursera's Machine Learning by Andrew Ng.

6. Real Projects

Kaggle Competitions: Practice what's learned by involving in Kaggle competitions and learn from others.

Personal Projects: Make projects on things that interest you—that is scraping, analyzing, and model building.

Collaboration: Work on a project with other students so as to get the feeling of working at a company.

Tools: Datasets, competitions, and the community provided in Kaggle, GitHub for project collaboration

7. Continue Learning

Advanced topics: Learn deep learning using TensorFlow or PyTorch, Natural Language Processing, and Big Data Technologies such as Spark.

Continual Learning: Next comes following blogs, research papers, and online courses that can help you track the most current trends and technologies in data science.

Resources: "Deep Learning" by Ian Goodfellow, Yoshua Bengio, and Aaron Courville, Fast.ai for practical deep learning courses.

Additional Tips

Practice regularly: The more you code and solve real problems, the better you will be at it.

Join Communities: Join as many online forums as possible, attend meetups, and join data science communities to learn from peers.

In summary, take those steps and employ the outlined resources to grow in building a solid base in Python for data science and be well on your way to be proficient in the subject.

[techaircraft]

Techaircraft

Dive into the world of Artificial Intelligence with Python! 🐍💡 Whether you're a seasoned coder or just starting, Python’s versatile libraries like Tensor Flow, Kera's, and sci-kit-learn make it easier than ever to build intelligent systems. 🤖 From developing predictive models to creating advanced neural networks, Python is your gateway to the future of technology. 📈🔍 Explore data analysis, natural language processing, and machine learning with hands-on projects that unlock endless possibilities. 🌐💻 Ready to level up your AI skills? Follow along for tutorials, tips, and inspiration to turn your innovative ideas into reality. . 𝐖𝐞𝐛𝐬𝐢𝐭𝐞 - www.techaircraft.com

𝐓𝐞𝐜𝐡𝐚𝐢𝐫𝐜𝐫𝐚𝐟𝐭 𝐬𝐮𝐩𝐩𝐨𝐫𝐭 𝐝𝐞𝐭𝐚𝐢𝐥𝐬:

𝐌𝐨𝐛𝐢𝐥𝐞 𝐍𝐮𝐦𝐛𝐞𝐫 - 8686069898

#ArtificialIntelligence#PythonProgramming#MachineLearning#DataScience#TechInnovation#NeuralNetworks#DeepLearning#CodingLife#PythonDeveloper#AIProjects#FutureOfTech#TechTrends#Programming#DataAnalysis#TensorFlow#Keras#ScikitLearn#LearnToCode#AICommunity#Innovation

[classychaosnachos]

DATA SCIENCE COURSE IN CHANDIGARH

The Data Science course in chandigarh offered by ThinkNext is a comprehensive program that covers a wide range of topics relevant to data science, utilizing Python as a primary tool. The course is structured to cater to both beginners and advanced learners, aiming to master data science skills.

Key features of the ThinkNext Data Science course include:

A detailed curriculum that starts with an introduction to Data Science, covering analytics, data warehousing, OLAP, MIS reporting, and the relevance of analytics in various industries. It also discusses the critical success drivers and provides an overview of popular analytics tools.

The course delves into core Python programming, including syntax, variables, data types, operators, conditional statements, and more advanced topics like function & modules, file handling, exception handling, and OOP concepts in Python.

It covers Python libraries and modules essential for Data Science, such as Numpy, Scify, pandas, scikitlearn, statmodels, and nltk, ensuring students are well-versed in data manipulation, cleansing, and analysis.

The program includes modules on data analysis and visualization, statistics, predictive modeling, data exploration for modeling, data preparation, solving segmentation problems, linear regression, logistic regression, and time series forecasting.

Additional benefits of the course include life-time validity learning and placement card, practical and personalized training with live projects, multiple job interviews with 100% job assistance, and the opportunity to work on live projects.

ThinkNext also offers a professional online course with international certifications from Microsoft and Hewlett Packard, providing step-by-step live demonstrations, personalized study and training plans, 100% placement support, and grooming sessions for personality development and spoken English.

The course has received recognition and awards, highlighting its quality and the institute's commitment to providing valuable learning experiences​​​​​​.

Contact us for more Information:

Company Name: ThinkNEXT Technologies Private Limited

Corporate Office (India) Address: S.C.F. 113, Sector-65, Mohali (Chandigarh)

Contact no: 78374-02000

Email id: info@thinknext.co.in

best data science institute in chandigarh

[craigbrownphd-blog-blog]

skops: a new library to improve scikit-learn in production https://www.kdnuggets.com/2023/02/skops-new-library-improve-scikitlearn-production.html?utm_source=dlvr.it&utm_medium=tumblr&utm_campaign=skops-a-new-library-to-improve-scikit-learn-in-production

[pythonfan-blog]

Build A Beautiful Machine Learning Web App With Streamlit And Scikit-learn    https://morioh.com/p/676c5ad0a240 #morioh #python #scikitlearn

[myitcertificate]

Machine learning is a skill that is growing in demand as businesses strive to make data-driven decisions. Once you have mastered the basics, you can start applying your skills to real-world problems.

For more details visit https://myitcertificate.com/courses.php?type=Machine%20Learning%20AI

[jobseekhs-blog]

How and When to Use a Calibrated Classification Model with scikit-learn

How and When to Use a Calibrated Classification Model with scikit-learn

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Instead of predicting class values directly for a classification problem, it can be convenient to predict the probability of an observation belonging to each possible class.

Predicting probabilities allows some flexibility including deciding how to interpret the probabilities, presenting predictions with uncertainty, and providing more nuanced ways to evaluate the skill of the model.

Pre…

[mohitverma0491]

Introduction to K Nearest Neighbours Classifier using Scikit Learn.

[excelworld]

[sovitdc]

In this article, we will take a practical approach to the k-Nearest Neighbor in machine learning. For implementation purposes of the k-Nearest Neighbor, we will use the Scikit-Learn library.