Machine Learning & Artificial Intelligence | Data Science Free Courses

“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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🔗 Machine Learning libraries

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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 😄👍

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☄️ What is an Artificial Neural Networks?

Artificial neural networks (ANN) give machines the ability to process data similar to the human brain and make decisions or take actions based on the data. While there’s still more to develop before machines have similar imaginations and reasoning power as humans, ANNs help machines complete and learn from the tasks they perform.

▶️Content: ➿➿➿➿➿➿➿➿➿➿➿➿ 001 What is Deep Learning 002 Plan of Attack 003 The Neuron 004 The Activation Function 005 How do Neural Networks work 006 How do Neural Networks learn 007 Gradient Descent 008 Stochastic Gradient Descent 009 Back propagation ➿➿➿➿➿➿➿➿➿➿➿➿ Deep Learning Complete Course

Some essential concepts every data scientist should understand: ### 1. Statistics and Probability - Purpose: Understanding data distributions and making inferences. - Core Concepts: Descriptive statistics (mean, median, mode), inferential statistics, probability distributions (normal, binomial), hypothesis testing, p-values, confidence intervals. ### 2. Programming Languages - Purpose: Implementing data analysis and machine learning algorithms. - Popular Languages: Python, R. - Libraries: NumPy, Pandas, Scikit-learn (Python), dplyr, ggplot2 (R). ### 3. Data Wrangling - Purpose: Cleaning and transforming raw data into a usable format. - Techniques: Handling missing values, data normalization, feature engineering, data aggregation. ### 4. Exploratory Data Analysis (EDA) - Purpose: Summarizing the main characteristics of a dataset, often using visual methods. - Tools: Matplotlib, Seaborn (Python), ggplot2 (R). - Techniques: Histograms, scatter plots, box plots, correlation matrices. ### 5. Machine Learning - Purpose: Building models to make predictions or find patterns in data. - Core Concepts: Supervised learning (regression, classification), unsupervised learning (clustering, dimensionality reduction), model evaluation (accuracy, precision, recall, F1 score). - Algorithms: Linear regression, logistic regression, decision trees, random forests, support vector machines, k-means clustering, principal component analysis (PCA). ### 6. Deep Learning - Purpose: Advanced machine learning techniques using neural networks. - Core Concepts: Neural networks, backpropagation, activation functions, overfitting, dropout. - Frameworks: TensorFlow, Keras, PyTorch. ### 7. Natural Language Processing (NLP) - Purpose: Analyzing and modeling textual data. - Core Concepts: Tokenization, stemming, lemmatization, TF-IDF, word embeddings. - Techniques: Sentiment analysis, topic modeling, named entity recognition (NER). ### 8. Data Visualization - Purpose: Communicating insights through graphical representations. - Tools: Matplotlib, Seaborn, Plotly (Python), ggplot2, Shiny (R), Tableau. - Techniques: Bar charts, line graphs, heatmaps, interactive dashboards. ### 9. Big Data Technologies - Purpose: Handling and analyzing large volumes of data. - Technologies: Hadoop, Spark. - Core Concepts: Distributed computing, MapReduce, parallel processing. ### 10. Databases - Purpose: Storing and retrieving data efficiently. - Types: SQL databases (MySQL, PostgreSQL), NoSQL databases (MongoDB, Cassandra). - Core Concepts: Querying, indexing, normalization, transactions. ### 11. Time Series Analysis - Purpose: Analyzing data points collected or recorded at specific time intervals. - Core Concepts: Trend analysis, seasonal decomposition, ARIMA models, exponential smoothing. ### 12. Model Deployment and Productionization - Purpose: Integrating machine learning models into production environments. - Techniques: API development, containerization (Docker), model serving (Flask, FastAPI). - Tools: MLflow, TensorFlow Serving, Kubernetes. ### 13. Data Ethics and Privacy - Purpose: Ensuring ethical use and privacy of data. - Core Concepts: Bias in data, ethical considerations, data anonymization, GDPR compliance. ### 14. Business Acumen - Purpose: Aligning data science projects with business goals. - Core Concepts: Understanding key performance indicators (KPIs), domain knowledge, stakeholder communication. ### 15. Collaboration and Version Control - Purpose: Managing code changes and collaborative work. - Tools: Git, GitHub, GitLab. - Practices: Version control, code reviews, collaborative development. Best Data Science & Machine Learning Resources: https://topmate.io/coding/914624 ENJOY LEARNING 👍👍

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