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 can't be earning more than 60 points from this repo. Any technical feature addition is excluded.
- Roadmap
- Latest Trends in Machine Learning
- Tutorials or Courses
- Books
- Datasets
- GitHub Repositories
- YouTube Channels
- Machine Learning Forums
- Courses
- Projects
- Internship Programs and Opportunities
- Interview
- Others
- Conclusion
Machine learning (ML) is a subset of artificial intelligence that focuses on building systems capable of learning from data, identifying patterns, and making decisions with minimal human intervention. The machine learning workflow is a structured approach that guides practitioners through the stages of developing effective models.
The first step involves gathering relevant data from various sources, such as databases, APIs, or web scraping. Quality data is crucial, as it directly impacts the performance of the machine learning model.
Data preprocessing is essential for cleaning the data and preparing it for analysis. This step involves handling missing values, removing duplicates, and normalizing or standardizing features to ensure consistent scales across the dataset.
EDA involves analyzing data distributions and relationships through visualization techniques, such as histograms, scatter plots, and box plots. This step helps identify patterns, trends, and anomalies within the data.
Feature engineering is the process of creating new features or transforming existing ones to improve the model's performance. This may involve techniques such as one-hot encoding for categorical variables, polynomial feature expansion, or domain-specific transformations.
Choosing the right algorithm is critical to the success of the machine learning project. This step involves selecting algorithms based on the problem type (e.g., classification, regression) and the nature of the data.
In this stage, the selected model is trained using a portion of the dataset (training data). The model learns patterns and relationships in the data through various optimization techniques.
Once the model is trained, it is evaluated using a separate portion of the dataset (validation/test data). Common evaluation metrics include accuracy, precision, recall, F1 score, and ROC-AUC, which help assess the model's performance.
After validation, the model can be deployed into production, making it accessible for real-world applications. This step includes integrating the model into existing systems and ensuring it can handle live data.
Post-deployment, continuous monitoring of the model's performance is necessary to ensure its effectiveness. This involves tracking model accuracy, updating it with new data, and retraining when necessary to adapt to changing conditions.
Through these stages, the machine learning workflow provides a systematic approach to building, validating, and deploying models that can yield valuable insights and drive decision-making across various domains.
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This is a roadmap, we can refer to for starting with Machine Learning.
This is a roadmap, we can refer to for starting with Machine Learning.
| Resource Name | Description |
|---|---|
| Machine Learning Roadmap | This roadmap provided by Scaler gives you a clear-cut roadmap for studying/learning Machine Learning |
| ML Engineer Roadmap | This roadmap gives you a clear-cut roadmap for becoming ready for the ML Engineer Job Profile |
Roadmap.sh
contains community-curated roadmaps, study plans, paths, and resources for developers.
- Offers clear visual representations of career paths.
- Provides step-by-step guidance for various tech roles.
- Allows users to track their progress and customize paths.
- Features feedback from industry professionals.
| Machine Learning Roadmaps | Description |
|---|---|
| AI and Data Scientist | Step-by-step guide to becoming an AI and Data Scientist in 2024 |
| Data Analyst | Step-by-step guide to becoming an Data Analyst in 2024 |
| MLOps | Step-by-step guide to learn MLOps in 2024 |
| Prompt Engineering | Step-by-step guide to learning Prompt Engineering |
Explore/Customize Roadmaps browse the ever-growing list of up-to-date, community driven roadmaps.
This is a roadmap, we can refer to for starting with Machine Learning.
This is a roadmap, we can refer to for starting with Machine Learning.
- AI Democratization: Making AI more accessible to developers and organizations.
- Edge Computing: Bringing Machine Learning models closer to data collection points.
- Explainable AI (XAI): Enhancing model transparency and interpretability.
- Federated Learning: Training models collaboratively across devices without data exchange.
- AI Ethics and Fairness: Focus on ethical AI development and minimizing biases.
- Generative AI: Technologies that create new content (text, images, music) using models like GANs, VAEs, and diffusion models.
- Large Language Models (LLMs): Models like GPT and BERT that excel in natural language processing and support applications like chatbots and content generation.
Discover a collection of tutorials and courses for learning the Mathematics, Fundamentals, Algorithms and more which are requied for Machine Learning.
Discover a collection of tutorials and courses for learning the Mathematics, Fundamentals, Algorithms and more which are requied for Machine Learning.
| Resource Name | Description |
|---|---|
| Linear Algebra | This link gives comprehensive video tutorials covering the fundamentals of linear algebra, including vectors, matrices, transformations, and more which is provided by Khan academy. |
| Calculus 1 (single variable) | 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. |
| Calculus 2 (multi variable) | 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. |
| Probability and statistics | 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. |
| Resource Name | Description |
|---|---|
| Python Fundamentals | This course is provided by the GeeksforGeeks and is perfect for both beginners and coding enthusiasts and covers essential Python fundamentals, including Object-Oriented Programming (OOPs), data structures, and Python libraries. |
| Python for Data Science | 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 |
| Data Visualization using Python | This video by intellipaat will gives you clear understanding for the visualization of data using python,This video is suitable for both beginners and an intermediate level programmer as well. |
| SQL Fundamentals | 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. |
| SQL for Data Analysis | This course is provided by the GeeksforGeeks and is perfect for both beginners and coding enthusiasts and covers essential Python fundamentals, including Object-Oriented Programming (OOPs), data structures, and Python libraries. |
| Jupyter Notebook | 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. |
| Google colab | 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. |
| Resource Name | Description |
|---|---|
| Numpy | This course is provided by the GeeksforGeeks, and is perfect for both beginners and coding enthusiasts and covers essential Python fundamentals, including Object-Oriented Programming (OOPs), data structures, and Python libraries. |
| Pandas | 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, and basic operations such as creating and manipulating DataFrames and Series, and more |
| Matplotlib | 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. |
| Tensorflow | The TensorFlow Tutorials page offers a variety of tutorials 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. |
| Pytorch | 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, cover a wide range of topics from beginners to advanced practitioners, and other varios topics |
| Keras | 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. |
| Scikit-learn | This documentation is the best resource 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 | 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. |
| SciPy | SciPy is a library used for scientific and technical computing. It builds on NumPy and provides a large number of mathematical algorithms and functions for optimization, integration, interpolation, eigenvalue problems, and more. |
| Resource Name | Description |
|---|---|
| Introduction to Machine Learning | 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. |
| Resource Name | Description |
|---|---|
| Andrew Ng's Machine Learning Specialization | This course by Andrew Ng on Coursera offers a comprehensive introduction to machine learning. It is an excellent starting point for beginners in the field, covering fundamental concepts and practical applications. The specialization consists of three courses: Supervised Machine Learning: Regression and Classification , Advanced Learning Algorithms and Unsupervised Learning, Recommenders, and Reinforcement Learning. |
| Resource Name | Description |
|---|---|
| Supervised Learning | 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.. |
| Unsupervised Learning | 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. |
| Reinforcement learning | 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. |
| Resource Name | Description |
|---|---|
| Data collection - guide | 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. |
| Introduction to Data collection | 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. |
| Data collection - video | 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. |
| Resource Name | Description |
|---|---|
| Introduction to Data Preparation | 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. |
| Data Preparation - Article | 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. |
| Data Preparation by Google developers | 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, |
| Resource Name | Description |
|---|---|
| Introduction to Model selection | "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. |
| Model selection process | 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 |
| Model selection - video | This video is about how to choose the right Machine Learning model, and in this video he also explains about Cross Validation which is used for Model Selection. |
| Resource Name | Description |
|---|---|
| Introduction to Model training | 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. |
| Model training - Video | This Edureka video on 'Data Modeling - Feature Engineering' gives a brief introduction to how the model is trained using Machine Learning algorithms. |
| Model training - Video | 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. |
| Resource Name | Description |
|---|---|
| Introduction to Model Evaluation | 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. |
| Model Evaluation - Article | 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 |
| Model Evaluation - Video | This video by AssemblyAI helps you to understand about the most commonly used evaluation metrics for classification and regression tasks and more. |
| Resource Name | Description |
|---|---|
| Introduction to Model Optimization | 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 |
| Model Optimization - Article | 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.. |
| Model Optimization - Video | This beginners friendly video by Brandon Rohrer gives you a brief understanding about how optimization for Machine Learning works and more. |
| Resource Name | Description |
|---|---|
| Introduction to Model Deployment - Article | 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 |
| Model Deployment Strategies | 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. |
| Model Deployment | This video by Microsoft Azure helps you to understand the various deployment options and optimizations for large-scale model inferencing. |
These are some Machine Learning algorithm, you can learn.
| Resource Name | Description |
|---|---|
| Linear Regression-1,Linear Regression-2 | These two videos by Techwithtim channel will give you a clear explanation and understanding of the Linear regression model, which is also the basic model in the Machine Learning. |
| Logistic Regression | 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. |
| Gradient Descent | 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 |
| Support Vector Machines | This video gives you the comprehensive knowledge for SVC and covers different parameters such as gamma, regularization and how to fine tune svm classifier using these parameters and more. |
| Naive Bayes-1,Naive Bayes-2 | 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. |
| K Nearest Neighbors | 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. |
| Decision Trees | This video will help you to solve a employee salary prediction problem using decision tree, and teaches you how to use the sklearn class to apply the decision tree model using python. |
| Random Forest | This video teaches you about Random forest a popular regression and classification algorithm, this video also helps you to solve problems using sklearn RandomForestClassifier in python. |
| KMeans Clustering | This video gives you a comprehensive knowledge about K Means clustering algorithm which is an 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. |
| Neural Network | 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. |
> Discover a diverse collection of valuable books for Machine Learning.
| Resource Name | Description | Cost |
|---|---|---|
| Hands-On Machine Learning with Scikit-Learn and TensorFlow | 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. | Free |
| The Hundred-Page Machine Learning Book | 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. | Free |
| Data Mining: Practical Machine Learning Tools and Techniques | '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 |
| Pattern Recognition and Machine Learning | Christopher Bishop's 'Pattern Recognition and Machine Learning' is a classic in the field, offering deep insights into the theoretical foundations of machine learning, pattern recognition, and probabilistic models. | Free |
| Machine Learning: A Probabilistic Perspective | Written by Kevin P. Murphy, this book offers an in-depth look at machine learning from a probabilistic viewpoint. It provides detailed explanations of machine learning methods along with mathematical principles. | Free |
| Deep Learning | Authored by Ian Goodfellow, Yoshua Bengio, and Aaron Courville, 'Deep Learning' is a foundational text covering the principles and practices of deep neural networks, making it essential for anyone serious about machine learning and AI. | Free |
| An Introduction to Statistical Learning | Written by Gareth James, Daniela Witten, Trevor Hastie, and Robert Tibshirani, this book provides an accessible introduction to statistical learning methods, particularly useful for students and practitioners new to the field. | Free |
These are some datasets that can help you practice Machine Learning
These are some datasets that can help you practice Machine Learning
| Resource Name | Description |
|---|---|
| Kaggle Datasets | 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 worldwide users. |
| Microsoft Datasets & Tools | 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 Datasets | 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. |
| Awesome Data Repo | 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. |
| UCI 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 | 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. |
| Hugging Face | Hugging Face is a powerful platform for machine learning engineers. Its array of features, including Datasets, Models, and Spaces, streamline the process of working with ML data, making it more accessible and efficient. |
These are some GitHub repositories you can refer to
These are some GitHub repositories you can refer to
| Resource Name | Description |
|---|---|
| ML-for-Beginners by Microsoft | 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. |
| Machine Learning Tutorial | 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). |
| ML by Zoomcamp | 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. |
| ML YouTube Courses | 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. |
Explore amazing YouTubers specializing in web development.
Explore amazing YouTubers specializing in web development.
| Resource Name | Description |
|---|---|
| Deep Learning AI | Deep Learning AI 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. |
| Machine Learning with Phil | 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. |
| Sent Dex | 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. |
| Abhishek Thakur | 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. |
| Dataschool | 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, |
| codebasics | The YouTube channel "codebasics," hosted by codebasics, offers a variety of tutorials and resources focused on programming, data science, Machine Learning, and artificial intelligence. |
| CampusX | The YouTube channel "CampusX," hosted by Nitish Singh (Founder), offers a variety of tutorials, courses, daily skill-building activities, hands-on projects, assignments, interactions with industry experts, and resources focused on Data Science, Machine Learning, and Artificial Intelligence. |
| Krish Naik | The YouTube channel features a variety of topics on machine learning, deep learning, and AI, presented through real-world problem scenarios. With over 30 tech talks delivered at meet-ups and technical institutions, the channel aims to make ML and AI accessible to everyone. The creator is passionate about new technology and offers all videos for free, promising engaging and informative content for viewers. |
Here are valuable resources to help you excel in your web development interview. You'll find videos, articles, and more to aid your preparation.
Here are valuable resources to help you excel in your web development interview. You'll find videos, articles, and more to aid your preparation.
| Resource Name | Description |
|---|---|
| Machine Learning - reddit | 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. |
| Machine Learning discussions - kaggle | 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. |
| Machine Learning Q/A - stack overflow | 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. |
| Machine Learning organisations - DEV community | 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. |
| Machine Learning communities - IBM | 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. |
These are Some valuable resources for learning Machine Learning.
These are Some valuable resources for learning Machine Learning.
| Resource Name | Description |
|---|---|
| Machine Learning by Edureka | 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. |
| Machine Learning with python by Freecodecamp | The "Machine Learning with Python" course on FreeCodeCamp provides a valuable learning resource for individuals interested in diving into the dynamic 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. |
| Machine Learning by university of washington | 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. |
| Post Graduate Programme in Machine Learning & AI by upgrad | 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. |
| Machine Learning with python by MIT | 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). |
| Data Science Mentorship Program 2.0 - CampusX | Data Science Mentorship Program 2.0 is an intensive live session program offering over 500 hours of comprehensive content. It includes five end-to-end case studies and a dedicated job preparation module, ensuring participants gain practical experience and are well-prepared for job opportunities. The program provides three years of validity, allowing for ample time to learn and engage with the material. |
These Projects help you gain real time exprience for building Machine Learning models.
These Projects help you gain real time exprience for building Machine Learning models.
| Resource Name | Description |
|---|---|
| 100+ Machine Learning projects | 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. |
| 500 ML projects repo | 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. |
Where to Apply for Internships | Virtual Internship Programs | Shared Internship Opportunities
Where to Apply for Internships | Virtual Internship Programs | Shared Internship Opportunities
| Platform(s) | Link(s) |
|---|---|
| LinkedIn Jobs | linkedin.com/jobs |
| Cuvette | cuvette.tech |
| Internshala | internshala.com |
| Glassdoor | glassdoor.com |
| Naukri | naukri.com |
| WellFound | wellfound.com |
| YCombinator | ycombinator.com/jobs |
| Indeed | indeed.com |
| SimplyHired | simplyhired.com |
| Hired | hired.com |
| WayUp | wayup.com |
| Program Name(s) | Link(s) | Description |
|---|---|---|
| SmartInternz | smartinternz.com | Provides real-world projects with mentorship for hands-on experience through virtual internships in areas like AI and ML. |
| Internshala | internshala.com | Offers a variety of remote internships across India with opportunities in tech, business, design, and more. |
| Pianalytix | pianalytix.com | 8-week remote internship program focused on project-based learning with individual guidance in AI and data analytics. |
| iNeuron | ineuron.ai | Provides virtual internships for industry-aligned learning through AI-focused projects, exploring new tech applications. |
| Ybi Foundation | ybifoundation.org | Project-based learning internship program in AI and ML, including assignments and guidance for 30 days. |
| Kodacy | kodacy.com | A 30-day virtual internship offering immersive experience in AI, with real-world projects and mentorship. |
| iNeuBytes | ineubytes.com | Virtual internships with structured tasks and industrial mentorship in data science and AI. |
| 1Stop.ai | 1stop.ai | Offers comprehensive two-month training with live sessions and project-focused AI internships. |
| CodeClause | codeclause.com | A remote internship with flexible hours, industry expert sessions, and a focus on practical skills in tech and AI. |
| Teachnook | teachnook.com | Internship providing hands-on projects and mentorship for practical learning in tech and AI fields. |
| Bharatintern | bharatintern.live | Offers field-specific, hands-on internships to help shape the next generation of tech leaders. |
| AICTE Internships | aicte-india.org | Helps Indian students find internships across India, connecting organizations with skilled interns. |
Note: Below are some recent internship opportunities shared via LinkedIn posts. This list is not exhaustive, and more opportunities can be added as they are discovered. These references serve as a resource for current openings, which can change frequently. For up-to-date listings, consider regularly checking LinkedIn and other professional networks.
| Link(s) to Opportunity |
|---|
| LinkedIn Post 1 |
| LinkedIn Post 2 |
| LinkedIn Post 3 |
| LinkedIn Post 4 |
| LinkedIn Post 5 |
| LinkedIn Post 6 |
| LinkedIn Post 7 |
| LinkedIn Post 8 |
| LinkedIn Post 9 |
These are some interview preparation resources.
These are some interview preparation resources.
| Resource Name | Description |
|---|---|
| Machine Learning Interview questions by GeeksforGeeks | 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. |
| How to crack Machine Learning Interviews at FAANG! - Medium | 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 Learning interview. |
These are some other resources you can refer to.
These are some other resources you can refer to.
| Resource Name | Description |
|---|---|
| Oreilly data show podcast | 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. |
| TWIML AI Podcast | 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 | "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. |
| Practical AI | 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 | 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 | 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. |
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.
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