🔍Welcome to the Machine learning repo project! 🌟...
This is complete beginner-friendly repo for gssoc beginners and new contributors will be given priority unlike FCFS issue on other repos.
Repeated issue creation for more scores will be considered has flag.
If later found out the points will be deducted. you cant be earning more than 60 points from this repo. any techincal feature addition is excluded
This link gives comprehensive video tutorials covering the fundamentals of linear algebra, including vectors, matrices, transformations, and more which is provided by Khan academy.
This course is provided by MIT gives a comprehensive introduction to the calculus of functions of one variable. It covers the fundamental principles and applications of single-variable calculus, which is essential for advanced studies in mathematics, science, and engineering.
This course provided by MIT focuses on calculus involving multiple variables, an essential area for understanding more complex mathematical models. Topics include vectors and matrices, partial derivatives, multiple integrals, vector calculus.
This course is provided by MIT and covers the fundamentals of probability and statistics, including random variables, probability distributions, expectation, and inference. It includes lecture notes, assignments, exams, and video lectures.
This course is provied by the Geeks for Geeks and is perfect for both beginners and coding enthusiasts and covers essential Python fundamentals, including Object-Oriented Programming (OOPs), data structures, and Python libraries.
This 12 hrs video provided Freecodecamp give you the fundamental knowledge required for the data science using python including the introduction of pandas,numpy and matplotlib
This video by intellipaat will gives you clear understanding for the visualization of data using python,This video is suitable for both beginners and a intermediate level programmer as well.
This video by Freecodecamp is a good introduction to SQL (Structured Query Language), covering essential concepts and commands used in database management. It explains the basics of creating, reading, updating, and deleting data within a database.
This course is provied by the Geeks for Geeks and is perfect for both beginners and coding enthusiasts and covers essential Python fundamentals, including Object-Oriented Programming (OOPs), data structures, and Python libraries.
The Real Python article on Jupyter Notebooks provides an in-depth introduction to using Jupyter Notebooks for data science, Python programming, and interactive computing. The tutorial covers the basics of setting up and running Jupyter Notebooks, including how to install Jupyter via Anaconda or pip, and how to launch and navigate the notebook interface.
The Google Colab introductory notebook provides a comprehensive guide on how to use Google Colab for interactive Python programming. It covers the basics of creating and running code cells, integrating with Google Drive for storage, and using Colab's powerful computing resources.
This course is provied by the Geeks for Geeks and is perfect for both beginners and coding enthusiasts and covers essential Python fundamentals, including Object-Oriented Programming (OOPs), data structures, and Python libraries.
The W3Schools Pandas tutorial offers a good introduction to the Pandas library, a powerful tool for data analysis and manipulation in Python. The tutorial covers a wide range of topics, including how to install Pandas, basic operations like creating and manipulating DataFrames and Series, and more
The Matplotlib documentation site provides a comprehensive guide to using the pyplot module, which is a part of the Matplotlib library used for creating static, animated, and interactive visualizations in Python.
The TensorFlow Tutorials page offers a variety of tutorials designed to help users learn and apply machine learning with TensorFlow. It includes beginner-friendly guides using the Keras API, advanced tutorials on custom training, distributed training, and specialized applications such as computer vision, natural language processing, and reinforcement learning.
The PyTorch tutorials website provides a comprehensive set of resources for learning and using PyTorch, a popular open-source machine learning library. The tutorials are designed for users at various skill levels, from beginners to advanced practitioners, and cover a wide range of topics
That documentation is a great resource for anyone looking to get started with Keras, a popular deep learning framework. Keras provides a user-friendly interface for building and training deep learning models. Whether you're a beginner or an experienced practitioner, Keras offers a lot of flexibility and ease of use.
This documentation is the best for learning Scikit-learn. Scikit-learn is another fantastic library, primarily used for machine learning tasks such as classification, regression, clustering, and more. Its simple and efficient tools make it accessible to both beginners and experts in the field.
Seaborn is an amazing visualization library for statistical graphics plotting in Python. It provides beautiful default styles and color palettes to make statistical plots more attractive.
This video by Edureka on "Introduction To Machine Learning" will help you understand the basics of Machine Learning like how,what,when and how it can be used to solve real-world problems.
The GeeksforGeeks article on supervised machine learning is the best resource. Their tutorials often break down complex topics into understandable explanations and provide code examples to illustrate concepts. Supervised learning is a fundamental concept in machine learning, where models are trained on labeled data to make predictions or decisions..
In this article on GeeksforGeeks, they delve deeper into different types of machine learning, expanding beyond supervised learning to cover unsupervised learning, semi-supervised learning, reinforcement learning, and more. Understanding the various types of machine learning is essential for choosing the right approach for different tasks and problems.
This geeksforgeeks article on reinforcement learning is the best to understand the RL.RL has applications in various domains, such as robotics, game playing, recommendation systems, and autonomous vehicle control, among others.
This guide on data collection for machine learning projects, which is a crucial aspect of building effective machine learning models. Data collection involves gathering, cleaning, and preparing data that will be used to train and evaluate machine learning algorithms.
This video by codebasics helps you to understand how data collection process is done by collecting the data in real time and gaining some hands-on experience.
This video helps get knowledge about where to collect data for Machine Learning; and Where to collect Data for Machine Learning. I Have also explained about Kaggle, UCI Machine Learning Repository and Google Dataset Search.
This video helps you break down the crucial steps and best practices to ensure your datasets are primed for machine learning success. From handling missing values and outliers to feature scaling and encoding categorical variables etc.
This article from Machine Learning Mastery provides a comprehensive guide on preparing data for machine learning, Which includes data cleaning, transforming, and organizing data to make it suitable for training machine learning models.
The Google's Machine Learning Data Preparation guide is a valuable resource for understanding best practices and techniques for preparing data for machine learning projects. Effective data preparation is crucial for building accurate and reliable machine learning models,
"A Gentle Introduction to Model Selection for Machine Learning" from Machine Learning Mastery sounds like a great resource for anyone looking to understand how to choose the right model for their machine learning task.
This Edureka video on Model Selection and Boosting, gives you Step by step guide to select and boost your models in Machine Learning, including need For Model Evaluation,Resampling techniques and more
This video is about how to choose the right machine learning model, and in this video he had also explained about Cross Validation which is used for Model Selection.
The article "Training a Machine Learning Model" from ProjectPro seems like a useful guide for anyone looking to understand the process of training machine learning models. Training a machine learning model involves feeding it with labeled data to learn patterns and make predictions or decisions.
This video by Microsoft Azure helps you to understand how to utilize the right compute on Microsoft Azure to scale your training of the model efficiently.
This GeeksforGeeks offers a clear guide on machine learning model evaluation, a crucial step in the machine learning workflow to ensure that models perform well on unseen data.
This Medium article is about the resource discussing various model evaluation metrics in machine learning which are crucial for understanding their performance and making informed decisions about model selection and deployment
The link provided leads to an article on Aporia's website discussing the basics of machine learning optimization and seven essential techniques used in this process and understanding these techniques is essential for improving model performance
Theis article from Towards Data Science is a comprehensive guide on understanding optimization algorithms in machine learning. Optimization algorithms play a crucial role in training machine learning models by iteratively adjusting model parameters to minimize a loss function..
This link will lead to an article on Built In discussing model deployment in the context of machine learning. Model deployment is a crucial step in the machine learning lifecycle, where the trained model is deployed into production to make predictions or decisions on new data
The article from Towards Data Science will focus on machine learning model deployment strategies, which are crucial for ensuring that trained models can be effectively deployed and used in real-world applications.
These two videos by Techwithtim channel will give you a clear explaination and understanding of the Linear regressing model,which is also the basic model in the machine learning.
This video by codebasics will give you a brief understanding of logistic regression and also how to use sklearn logistic regression class. At the end we have an interesting exercise for you to solve.
This video, will teach you few important concepts in machine learning such as cost function, gradient descent, learning rate and mean squared error and more. This helps you to python code to implement gradient descent for linear regression in python
This video gives you the comprehensive knowledge for the SVC and covers different parameters such as gamma, regularization and how to fine tune svm classifier using these parameters and more.
These two videos by codebasics gives you the brief understanding of Naive bayes and also teaches you about sklearn library and python for this beginners machine learning model.
This video helps you understand how K nearest neighbors algorithm work and also write python code using sklearn library to build a knn (K nearest neighbors) model to have hands-on experience.
This video will help you to solve a employee salary prediction problem using decision tree, and teahes you how to use the sklearn class to apply the decision tree model using python.
This video teaches you about Random forest a popular regression and classification algorithm, this video also helps you to problem using sklearn RandomForestClassifier in python.
This video gives you a comprehensive knowledge about K Means clustering algorithm which is a unsupervised machine learning technique used to cluster data points, and this video also helps you to solve a clustering problem using sklearn, kmeans and python.
This video provides a comprehensive introduction to neural networks, covering fundamental concepts, training processes, and practical applications. It explains forward and backward propagation, deep learning techniques, and the use of convolutional neural networks (CNNs) for image processing. Additionally, it demonstrates implementing neural networks using Python, TensorFlow, and other libraries, including examples such as stock price prediction and image classification.
Books
Discover a diverse collection of valuable books for Machine Learning.
The Hands-On Machine Learning with Scikit-Learn and TensorFlow is a popular book by Aurélien Géron that covers various machine learning concepts and practical implementations using Scikit-Learn and TensorFlow.
This book, authored by Andriy Burkov, provides a concise yet comprehensive overview of machine learning concepts and techniques. It's highly regarded for its accessibility and clarity, making it a valuable resource for both beginners and experienced practitioners
"Data Mining: Practical Machine Learning Tools and Techniques" provides a comprehensive overview of the field of data mining and machine learning. Authored by Ian H. Witten, Eibe Frank, and Mark A. Hall, this book is widely regarded as an essential resource for students, researchers, and practitioners in the field.
free
Datasets
These are some datasets that can help you practice machine learning
Kaggle Datasets is a platform where users can explore, access, and share datasets for a wide range of topics and purposes. Kaggle is a popular community-driven platform for data science and machine learning competitions, and its Datasets section extends its offerings to provide access to a diverse collection of datasets contributed by users worldwide.
Microsoft Research Tools is a platform offering a diverse range of tools,datasets and resources for researchers and developers. These tools are designed to facilitate various aspects of research, including data analysis, machine learning, natural language processing, computer vision, and more.
Google Dataset Search is a tool provided by Google that allows users to search for datasets across a wide range of topics and domains. It helps researchers, data scientists, journalists, and other users discover datasets that are relevant to their interests or research needs.
This GitHub repo is a curated list of publicly available datasets covering a wide range of topics and domains. This repository serves as a valuable resource for researchers, data scientists, developers, and anyone else interested in accessing and working with real-world datasets.
The UCI Machine Learning Repository, hosted at the URL you provided, is a collection of datasets for machine learning research and experimentation. It's maintained by the Center for Machine Learning and Intelligent Systems at the University of California, Irvine (UCI).
Data.gov, a US government website, is invaluable for machine learning enthusiasts with its vast collection of nearly 300,000 datasets. It provides high-quality, reliable training data from various sectors, enabling innovative applications in public health, economics, and environmental science. The open data is freely available, eliminating licensing costs and allowing unrestricted use. Its authoritative sources ensure improved accuracy and reliability in machine learning models.
### GitHub Repositories
> These are some GitHub repositories you can refer to
The GitHub repository "ML-For-Beginners" is an educational resource provided by Microsoft, aimed at beginners who are interested in learning about machine learning (ML) concepts and techniques.
The GitHub repository "Machine-Learning-Tutorials" by ujjwalkarn is a comprehensive collection of tutorials, resources, and educational materials for individuals interested in learning about machine learning (ML).
This GitHub repository by DataTalksClub is a collection of materials and resources associated with the Machine Learning Zoomcamp, an educational initiative aimed at teaching machine learning concepts and techniques through live Zoom sessions.
This GitHub repository is a collection of resources related to machine learning (ML) courses available on YouTube, and provides links to the YouTube videos or playlists for each course, making it easy for learners to access the course content directly from YouTube.
YouTube Channels
Explore amazing YouTubers specializing in web development.
Web Dev Simplified is all about teaching web development skills and techniques in an efficient and practical manner. If you are just getting started in web development Web Dev Simplified has all the tools you need to learn the newest and most popular technologies to convert you from a no stack to full stack developer. Web Dev Simplified also deep dives into advanced topics using the latest best practices for you seasoned web developers.
The YouTube channel "Deeplearning.ai" hosts a variety of educational content related to artificial intelligence (AI) and machine learning (ML) created by Andrew Ng and his team at Deeplearning.ai.
The YouTube channel "sentdex," hosted by Harrison Kinsley, offers a diverse range of educational content primarily focused on Python programming, machine learning, game development, hardware projects,robotics and more.
The YouTube channel "Abhishek Thakur (Abhi)" is hosted by Abhishek Thakur, a well-known figure in the machine learning and data science community.This channel is primarly related to Machine leanring.
The YouTube channel "Data School," hosted by Kevin Markham, offers a wide range of tutorials and resources related to data science, machine learning, and Python programming, covering topics such as data manipulation with pandas, data visualization with Matplotlib and Seaborn,
The YouTube channel "codebasics," hosted by codebasics, offers a variety of tutorials and resources focused on programming, data science, machine learning, and artificial intelligence.
Machine Learning Forums
Here are valuable resources to help you excel in your web development interview. You'll find videos, articles, and more to aid your preparation.
The subreddit r/MachineLearning is a popular online community on Reddit dedicated to discussions, news, research, and resources related to machine learning and artificial intelligence.
The Kaggle Discussions forum is a community-driven platform where data scientists, machine learning practitioners, and enthusiasts engage in discussions, seek help, share insights, and collaborate on projects related to data science and machine learning.
The "machine-learning" tag on Stack Overflow is a popular destination for developers, data scientists, and machine learning practitioners seeking assistance, sharing insights, and discussing topics related to machine learning.
DEV Community platform for articles related to "machine learning" from organizations. DEV Community is a community-driven platform for developers where they can share their knowledge, experiences, and insights through articles, discussions, and tutorials.
The IBM Community for AI and Data Science provides a valuable platform for professionals and enthusiasts to learn, collaborate, and stay informed about the latest developments in artificial intelligence, data science, and related fields.
Courses
These are Some valuable resources for learning Machine learning.
This youtube playlist by Edureka on machine learning is the best resource to learn machine learning from beginners level to advanced level that too for free.
The "Machine Learning with Python" course on FreeCodeCamp provides a valuable learning resource for individuals interested in diving into the field of machine learning using Python, this course offers a structured path to learn machine learning concepts and develop practical skills through hands-on projects and exercises.
This course on Coursera provides a high-quality learning experience for individuals who want to dive deep into the field of machine learning and acquire practical skills that are in high demand in today's job market.
This ML program offered by upGrad in collaboration with IIIT Bangalore is designed to provide students with a comprehensive education in machine learning and artificial intelligence, preparing them for careers in this rapidly growing and exciting field.
This course provided directly to the edX platform's "Machine Learning with Python: from Linear Models to Deep Learning" course offered by the Massachusetts Institute of Technology (MIT).
Projects
These Projects help you gain real time exprience for building machine learning models.
This link which navigates to geekforgeeks article focuses on machine learning projects page on which serves as a valuable resource for individuals looking to explore, learn, and practice machine learning concepts through hands-on projects.
This GitHub repo maintained by Ashish Patel offers a comprehensive collection of machine learning and AI projects, providing valuable resources and learning opportunities for enthusiasts, students, researchers, and practitioners interested in exploring ML.
This link which navigates to geekforgeeks article focuses on machine learning Interview questions
for both freshers and experienced individuals, ensuring thorough preparation for ML interview. This ML questions is also beneficial for individuals who are looking for a quick revision of their machine-learning concepts.
This article by Bharathi Priya shared her Machine Learning experiences provided the questions which were asked in her interview and provided tips and tricks to crack any machine leaning interview.
The O'Reilly Data Show Podcast, hosted on the O'Reilly Radar platform, is a podcast series dedicated to exploring various topics of data science, machine learning, artificial intelligence, and related fields.
The TWIML AI Podcast, hosted on the TWIML AI platform, is a podcast series focused on exploring the latest developments, trends, and innovations in the fields of machine learning and artificial intelligence.
"Talk Python to Me" provides a valuable platform for Python enthusiasts, developers, and learners to stay informed, inspired, and connected within the vibrant and growing Python community.
The Practical AI podcast offers a valuable platform for individuals interested in practical applications of AI and ML technologies. this podcast provides informative and engaging content to help you stay informed and inspired in the rapidly evolving field
The "Talking Machines" offers a valuable platform for individuals interested in staying informed, inspired, and engaged in the dynamic field of machine learning, this podcast provides informative and engaging content on ML.
MachineHack is an online platform that offers data science and machine learning competitions. It provides a collaborative environment for data scientists, machine learning practitioners, and enthusiasts to solve real-world business problems through predictive modeling and data analysis.
### Conclusion
Machine learning is an exciting and rapidly evolving field that offers endless opportunities for innovation and discovery. Its ability to analyze vast amounts of data and uncover patterns makes it indispensable for various applications, from predictive analytics and natural language processing to computer vision and autonomous systems. The wealth of libraries and frameworks available, such as TensorFlow, PyTorch, and scikit-learn, empowers developers and data scientists to build sophisticated models with relative ease. A strong community provides extensive resources, including tutorials, forums, and documentation, to support learners and professionals alike. To truly excel in machine learning, consistent practice is essential—engage in coding challenges, contribute to open-source projects, and apply your knowledge to real-world problems. This hands-on experience not only hones your skills but also opens doors to numerous career opportunities in tech, research, and beyond.
I would like to add a project on iris flower classification as part of GSSOC'24 where I will use different models like KNN, Random Forest, and SVM. I have worked on this before, and I believe it can be completed in one day.
Clustering:
K-Means Clustering:
A simple example that demonstrates how to use the K-Means algorithm to cluster a dataset into different groups. To demonstrate K-Means Clustering using Scikit-learn, The generated dataset is visualized before and after clustering, showing the cluster assignments and cluster centers.
I am well-known in this topic can you please assign me this issue.
It's an suggestion to include a project which basically can perform prediction on X ray images and analyse the disease to provide a target value with the use of Neural Networks.
Detection of Lung cancer utilizing Convolutional Neural Networks (CNN) with strategically designed convolutional layers, max pooling layers ,dense Layer and fully connected layers in Deep Learning.
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Idea: To develop a logistic regression model to predict the likelihood of student placement based on features including CGPA, gender, 10th and 12th percentage, streams of education, degree percentage, and work experience. @sanjay-kv .
Please assign me this issue
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Explaining about various optimization techniques which is used to change model weights and learning rates, like Gradient Descent, Stochastic Gradient Descent, Stochastic Gradient descent with momentum, Mini-Batch Gradient Descent, AdaGrad, RMSProp, AdaDelta, and Adam. These optimization techniques play a critical role in the training of neural networks, as they help to improve the model by adjusting its parameters to minimize the loss of function value.
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I would like to add a deep fake analyzer that can detect whether an image is fake or real as part of GSSOC'24. I have already worked on this and achieved excellent results. Please assign this task to me. I have added a video to showcase how it works. It will take me approximately one hour to complete it, as I have already worked on it.
An ML model that allows users to classify social media texts (mainly tweets) based on whether it has sarcasm or irony.
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Overview:
Aim is to develop a predictive model for Plant disease using TensorFlow.
Objective:
The primary objective of this project is to create a machine-learning model that can accurately predict the disease by analyzing Plant's leaf Image.
I would like this issue to be assigned to me as part of GSSoC'24. Thank you.
Handwritten digit recognition involves classifying images of handwritten digits (0-9) into their respective categories. CNNs are particularly well-suited for this task due to their ability to automatically and adaptively learn spatial hierarchies of features through backpropagation by using multiple building blocks, such as convolution layers, pooling layers, and fully connected layers.
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I want to add this project to this repository.This problem statement is basically a multiclassification problem statement.I will use different ml algorithms like Naivebyes,RandomForest,Xgboost,Decisiontrees.I will also deploy it in streamlit too.
It looks somewhat like this while the code (Python) is implemented. It is a new project suggested to be included in the repository to improve the quality. It can provide better colour to B/W images or degraded pictures.
The Housing Price Prediction model is a machine learning tool designed to predict the prices of houses based on various features such as location, size, number of rooms, and other relevant attributes. This model leverages advanced statistical techniques and machine learning algorithms to provide accurate and reliable price predictions.
Please assign me this issue, So that i can work on it
In the project the Customer Segmentation is done using a combination of three algorithms ( DBSCAN, Hierarchical and K-means),
The user can input the dataset and get the output in the form of graphs and can also download the csv files of individual clusters, Additionally Shilloute scores have been displayed to understand the efficiency of the clusters. I want to add this project to your repo as this would be a great application of machine learning that would help learners understand the practical applications. Please assign this to me under GSSOC'24. Thank you.
This issue involves implementing car detection using MobileNetSSD. MobileNetSSD is chosen for its efficiency and accuracy in real-time object detection tasks. The task includes:
Implementing MobileNetSSD for car detection
Fine-tuning the model on specific datasets
Processing input images to identify and localize cars
Post-processing with non-max suppression
Application in autonomous driving, traffic monitoring, and surveillance systems
Please assign this issue to me for implementation and refinement of the car detection system using MobileNetSSD.
The proposal is solely based on the project where various constraints are implemented to analyze a facial structure and display an analysed report. The project is aimed to be subjected in the Machine Learning and Data Science / Intermediate or Advanced folder.
It detects age, gender, facial expression (including angry, fear, neutral, sad, disgust, happy and surprise) and race (including asian, white, middle eastern, indian, latino and black) predictions. Result is going to be the size of faces appearing in the source image.
dataset link : https://data.nasa.gov/Aerospace/CMAPSS-Jet-Engine-Simulated-Data/ff5v-kuh6/about_data
This model aims to predict industrial equipment, in this case airplane engine before failure. This dataset provides time for failure during a particular duration and predicts the remaining useful life.
The model can be made for both classification and regression.
For classification, we can make 3 conditions of the machines : i) Good condition
ii) Moderate condition
iii) Warning condition
For regression, we predict the remaining useful life for the machine.
Different models will be implement in this project.
For this project, I will be using Logistic Regression model.
It will involve classifying transactions as either fraudulent or legitimate. This method leverages a statistical model to estimate the probability of fraud based on various transaction features, such as amount, time, and user behavior.
I would like to contribute a linear regression model that has been trained on the well-known diamond dataset. This model will help predict diamond prices based on various attributes such as carat, cut, color, clarity, and other relevant features
Dataset Overview:
The diamond dataset contains detailed information on the characteristics and prices of diamonds.
Key features include:
Carat: The weight of the diamond.
Cut: The quality of the cut (Fair, Good, Very Good, Premium, Ideal).
Color: Diamond color, with categories ranging from D (best) to J (worst).
Clarity: The clarity rating of the diamond.
Depth: Total depth percentage (a measure of the diamond's proportions).
Table: Width of the top of the diamond relative to the widest point.
Price: The price of the diamond.
Approach:
Data Preprocessing:
Clean and preprocess the dataset
Handle missing values
Encode categorical variables using One hot encoding and Ordinal encoding
Find the correlation between features
Drop duplicate rows
Normalize numerical features
Model Training:
Develop a linear regression model using appropriate libraries (e.g., scikit-learn).
Train the model on a subset of the dataset and validate its performance on a separate test set.
Performance Evaluation:
Evaluate the model using metrics such as
Mean Absolute Error (MAE),
Mean Squared Error (MSE), and
R-squared to ensure its accuracy and reliability.
This project would delve into the factors contributing to Parkinson's disease and develop a predictive model to identify individuals at risk.
Models to be Utilized and compared :-
Logistic Regression
K-Nearest Neighbors (KNN)
Random Forest Regressor
Decision Tree Regressor
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Is your feature request related to a problem? Please describe.
Currently, the facial recognition library only verifies the identity of a person but does not provide any means to distinguish between a live person and a spoof (such as a photograph or a video). This poses a security risk in applications requiring robust authentication mechanisms.
Describe the solution you'd like
Integrate a liveliness detection feature into the facial recognition library. This feature should be able to distinguish between live persons and spoof attacks (e.g., photos, videos, masks) by analyzing facial movements, skin texture, and other biometric indicators. Ideally, the solution should work in real-time and be compatible with existing functionality of the library.
Additional context
Liveliness detection is becoming a standard requirement in many facial recognition applications, such as mobile banking, secure access control, and identity verification systems. Implementing this feature will enhance the security and reliability of the library, making it more competitive and suitable for a broader range of applications.
This project will delve into the factors contributing to the classification of music genres and develop a predictive model to accurately identify the genre of a given piece of music.
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I want to work on this issue.
I would like to add a project of AIML which uses deep learning with image feature extractor and transformers to generate a caption for an image. The transformer model would be created from scratch using Tensorflow and Keras with a pretrained model of image extractor trained with imagenet images. The two will be combined where the latter extracts the features that can be provided as input in the former to generate caption.
Problem Statement
The objective of this project is to develop a machine learning model capable of predicting the prices of cars based on various features such as make, model, year, mileage, and other relevant attributes. Accurate car price predictions can significantly benefit stakeholders in the automotive industry, including buyers, sellers, dealers, and insurers, by providing realistic valuations and helping in decision-making processes.
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The objective of this project is to develop a machine learning model capable of detecting spam emails. With the increasing volume of email communication, distinguishing between spam and legitimate emails has become crucial. Effective spam detection can enhance email security and improve user experience by reducing unwanted and potentially harmful emails.
✔️ Involved training neural networks to accurately classify facial expressions using annotated facial image datasets, models were constructed and trained with PyTorch's deep learning capabilities.
✔️ Project was employed by convolutional neural networks (CNNs).
✔️ The success of the project was gauged through the model's proficiency in accurately identifying expressions on unseen data, with potential applications ranging from emotion-aware interfaces to behavioral analysis in psychology and market research.
Machine Learning starts with learning about Scikit-Learn Module learning. I would like to add about scikit learn module. Please assign me this task under GSSOC'2024
Transfer learning can significantly improve model performance with less training data. Adding examples using pre-trained models (e.g., VGG16, ResNet) for tasks like image classification or feature extraction.
Proposed Examples:
Include code for fine-tuning the pre-trained model on a new dataset
Demonstrate the process of feature extraction using pre-trained models
Provide detailed comments and explanations of each step
Benefits:
Enhances the repository by including advanced neural network techniques
Helps users understand and implement transfer learning in their projects
Shows the performance improvements achievable with transfer learning compared to models trained from scratch
Additional Context:
Transfer learning is particularly useful when dealing with limited data, as it leverages the knowledge gained from large datasets used to train the pre-trained models.
Examples can be implemented in Jupyter notebooks to provide an interactive learning experience.
@sanjay-kv I am GSSOC'24 Contributor and would like to work on this issue.
The repository lacks a proper documentation of the projects plus a systematic beginner friendly readme guiding the newbies as to how they're supposed to start. I wish to add the same along with a short vid tutorial.
I wish to work on this issue.
I will follow the proper code of conduct of GSSOC 2024.
I am a GSSOC 2024 contributor.
I thought if this is a ML repository then ofcourse beginners like me would also like to learn some basics about Datascience to learn ML right.So I would like to make a small notebook on learning matplotlib pyplot and its properties. :)
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The system enables users to control the computer cursor using hand movements, which are tracked through color caps. It supports various mouse operations like left-click, right-click, and cursor movement by recognizing hand gestures and detecting colors.
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React
Typescript
Django
Laravel
Facebook
Microsoft
Google
Alibaba
D3
Tencent