Machine Learning & Artificial Intelligence | Data Science Free Courses

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“The Best Public Datasets for Machine Learning and Data Science” by Stacy Stanford https://datasimplifier.com/best-data-analyst-projects-for-freshers/ https://toolbox.google.com/datasetsearch https://www.kaggle.com/datasets http://mlr.cs.umass.edu/ml/ https://www.visualdata.io/ https://guides.library.cmu.edu/machine-learning/datasets https://www.data.gov/ https://nces.ed.gov/ https://www.ukdataservice.ac.uk/ https://datausa.io/ https://www.cs.toronto.edu/~delve/data/boston/bostonDetail.html https://www.kaggle.com/xiuchengwang/python-dataset-download https://www.quandl.com/ https://data.worldbank.org/ https://www.imf.org/en/Data https://markets.ft.com/data/ https://trends.google.com/trends/?q=google&ctab=0&geo=all&date=all&sort=0 https://www.aeaweb.org/resources/data/us-macro-regional http://xviewdataset.org/#dataset http://labelme.csail.mit.edu/Release3.0/browserTools/php/dataset.php http://image-net.org/ http://cocodataset.org/ http://visualgenome.org/ https://ai.googleblog.com/2016/09/introducing-open-images-dataset.html?m=1 http://vis-www.cs.umass.edu/lfw/ http://vision.stanford.edu/aditya86/ImageNetDogs/ http://web.mit.edu/torralba/www/indoor.html http://www.cs.jhu.edu/~mdredze/datasets/sentiment/ http://ai.stanford.edu/~amaas/data/sentiment/ http://nlp.stanford.edu/sentiment/code.html http://help.sentiment140.com/for-students/ https://www.kaggle.com/crowdflower/twitter-airline-sentiment https://hotpotqa.github.io/ https://www.cs.cmu.edu/~./enron/ https://snap.stanford.edu/data/web-Amazon.html https://aws.amazon.com/datasets/google-books-ngrams/ http://u.cs.biu.ac.il/~koppel/BlogCorpus.htm https://code.google.com/archive/p/wiki-links/downloads http://www.dt.fee.unicamp.br/~tiago/smsspamcollection/ https://www.yelp.com/dataset https://t.me/DataPortfolio/2 https://archive.ics.uci.edu/ml/datasets/Spambase https://bdd-data.berkeley.edu/ http://apolloscape.auto/ https://archive.org/details/comma-dataset https://www.cityscapes-dataset.com/ http://aplicaciones.cimat.mx/Personal/jbhayet/ccsad-dataset http://www.vision.ee.ethz.ch/~timofter/traffic_signs/ http://cvrr.ucsd.edu/LISA/datasets.html https://hci.iwr.uni-heidelberg.de/node/6132 http://www.lara.prd.fr/benchmarks/trafficlightsrecognition http://computing.wpi.edu/dataset.html https://mimic.physionet.org/ ✅ Best Telegram channels to get free coding & data science resources https://t.me/addlist/4q2PYC0pH_VjZDk5 ✅ Free Courses with Certificate: https://t.me/free4unow_backup

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You don't need to spend several $𝟭𝟬𝟬𝟬𝘀 to learn Data Science.❌ Stanford University, Harvard University & Massachusetts Institute of Technology is providing free courses.💥 Here's 8 free Courses that'll teach you better than the paid ones: 1. CS50’s Introduction to Artificial Intelligence with Python (Harvard) https://pll.harvard.edu/course/cs50s-introduction-artificial-intelligence-python 2. Data Science: Machine Learning (Harvard) https://pll.harvard.edu/course/data-science-machine-learning 3. Artificial Intelligence (MIT) https://lnkd.in/dG5BCPen 4. Introduction to Computational Thinking and Data Science (MIT) https://lnkd.in/ddm5Ckk9 5. Machine Learning (MIT) https://lnkd.in/dJEjStCw 6. Matrix Methods in Data Analysis, Signal Processing, and Machine Learning (MIT) https://lnkd.in/dkpyt6qr 7. Statistical Learning (Stanford) https://online.stanford.edu/courses/sohs-ystatslearning-statistical-learning 8. Mining Massive Data Sets (Stanford) 📍https://online.stanford.edu/courses/soe-ycs0007-mining-massive-data-sets ENJOY LEARNING

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Creating a one-month data analytics roadmap requires a focused approach to cover essential concepts and skills. Here's a structured plan along with free resources: 🗓️Week 1: Foundation of Data Analytics ◾Day 1-2: Basics of Data Analytics Resource: Khan Academy's Introduction to Statistics Focus Areas: Understand descriptive statistics, types of data, and data distributions. ◾Day 3-4: Excel for Data Analysis Resource: Microsoft Excel tutorials on YouTube or Excel Easy Focus Areas: Learn essential Excel functions for data manipulation and analysis. ◾Day 5-7: Introduction to Python for Data Analysis Resource: Codecademy's Python course or Google's Python Class Focus Areas: Basic Python syntax, data structures, and libraries like NumPy and Pandas. 🗓️Week 2: Intermediate Data Analytics Skills ◾Day 8-10: Data Visualization Resource: Data Visualization with Matplotlib and Seaborn tutorials Focus Areas: Creating effective charts and graphs to communicate insights. ◾Day 11-12: Exploratory Data Analysis (EDA) Resource: Towards Data Science articles on EDA techniques Focus Areas: Techniques to summarize and explore datasets. ◾Day 13-14: SQL Fundamentals Resource: Mode Analytics SQL Tutorial or SQLZoo Focus Areas: Writing SQL queries for data manipulation. 🗓️Week 3: Advanced Techniques and Tools ◾Day 15-17: Machine Learning Basics Resource: Andrew Ng's Machine Learning course on Coursera Focus Areas: Understand key ML concepts like supervised learning and evaluation metrics. ◾Day 18-20: Data Cleaning and Preprocessing Resource: Data Cleaning with Python by Packt Focus Areas: Techniques to handle missing data, outliers, and normalization. ◾Day 21-22: Introduction to Big Data Resource: Big Data University's courses on Hadoop and Spark Focus Areas: Basics of distributed computing and big data technologies. 🗓️Week 4: Projects and Practice ◾Day 23-25: Real-World Data Analytics Projects Resource: Kaggle datasets and competitions Focus Areas: Apply learned skills to solve practical problems. ◾Day 26-28: Online Webinars and Community Engagement Resource: Data Science meetups and webinars (Meetup.com, Eventbrite) Focus Areas: Networking and learning from industry experts. ◾Day 29-30: Portfolio Building and Review Activity: Create a GitHub repository showcasing projects and code Focus Areas: Present projects and skills effectively for job applications. 👉Additional Resources: Books: "Python for Data Analysis" by Wes McKinney, "Data Science from Scratch" by Joel Grus. Online Platforms: DataSimplifier, Kaggle, Towards Data Science Tailor this roadmap to your learning pace and adjust the resources based on your preferences. Consistent practice and hands-on projects are crucial for mastering data analytics within a month. Good luck!

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Use this checklist to see if you’re truly JOB-READY. The more items you complete, the closer you are to landing your dream data science job! 😎 Check Your Skills with This Checklist! Python:- Master Python fundamentals Understand Pandas for data manipulation Learn data visualization with Matplotlib and Seaborn Practice error handling and debugging Statistics:- Grasp probability theory Know descriptive and inferential statistics Learn statistical machine learning concepts Exploratory Data Analysis (EDA):- Perform data summarization Work on data cleaning and transformation Visualize data effectively SQL:- Understand the BIG 6 SQL statements Practice joins and common table expressions (CTEs) Use window functions Learn to write stored procedures Machine Learning:- Master feature engineering Understand regression and classification techniques Learn clustering methods Model Evaluation:- Work with confusion matrices Understand precision, recall, and F1-score Practice cross-validation Learn about overfitting and underfitting Deep Learning:- Get familiar with Convolutional Neural Networks (CNNs) Understand transformers Learn PyTorch or TensorFlow basics Practice model training and optimization Resume:- Ensure your resume is ATS-friendly Customize for the job description Use the STAR method to highlight achievements Include a link to your portfolio AI-Enabled Mindset:- Develop Googling skills Use AI tools like ChatGPT or Bard for learning Commit to continuous learning Hone problem-solving abilities Communication:- Practice presenting insights clearly Write professional emails Manage stakeholder communication Utilize project management tools LinkedIn:- Have a good profile picture and banner Get 10+ endorsed skills Collect at least 3 recommendations Link your portfolio in your profile Portfolio:- Include 4+ business-related projects Showcase one project per tool you know Create an insights desk Prepare a video presentation Like if you need similar content 😄👍

6 Tips for Building a Robust Machine Learning Model 1. Understand the problem thoroughly before jumping into the model. ➝ Taking time to understand the problem helps build a solution aligned with business needs and goals. 2. Focus on feature engineering to improve accuracy. ➝ Well-engineered features make a big difference in model performance. Collaborating with data engineers on clean and well-structured data can simplify feature engineering. 3. Start simple, test assumptions, and iterate. ➝ Begin with straightforward models to test ideas quickly. Iteration and experimentation will lead to stronger results. 4. Keep track of versions for reproducibility.  ➝  Documenting versions of data and code helps maintain consistency, making it easier to reproduce results. 5. Regularly validate your model with new data. ➝ Models should be updated and validated as new data becomes available to avoid performance degradation. 6. Always prioritize interpretability alongside accuracy. ➝ Building interpretable models helps stakeholders understand and trust your results, making insights more actionable. Like if you need similar content 😄👍