Data science/ML/AI

CS109 Data Science By Harvard University ⌛️ 12 weeks ✅ Video lectures ✅ Slides ✅ Lab exercises 🔗 http://cs109.github.io/2015/pages/videos.html Note: i have issues with first video link but others are fine. #datascience #python #harvard ➖➖➖➖➖➖➖➖➖➖ Join @datascience_bds for more cool data science materials. *This channel belongs to @bigdataspecialist group

Source codes for data science projects from my Instagram post: https://www.instagram.com/p/CJwDIpCA0nc/ 1. Build chatbots: https://dzone.com/articles/python-chatbot-project-build-your-first-python-pro 2. Credit card fraud detection: https://www.kaggle.com/renjithmadhavan/credit-card-fraud-detection-using-python 3. Fake news detection https://data-flair.training/blogs/advanced-python-project-detecting-fake-news/ 4.Driver Drowsiness Detection https://data-flair.training/blogs/python-project-driver-drowsiness-detection-system/ 5. Recommender Systems (Movie Recommendation) https://data-flair.training/blogs/data-science-r-movie-recommendation/ 6. Sentiment Analysis https://data-flair.training/blogs/data-science-r-sentiment-analysis-project/ 7. Gender Detection & Age Prediction https://www.pyimagesearch.com/2020/04/13/opencv-age-detection-with-deep-learning/ #data_science #projects ➖➖➖➖➖➖➖➖➖➖➖➖➖➖ Join @datascience_bds for more cool data science materials. *This channel belongs to @bigdataspecialist group

Python course by kaggle Learn the most important language for data science. 🎬 8 lessons ⏰ 5 hours https://www.kaggle.com/learn/python #python ➖➖➖➖➖➖➖➖➖➖ Join @bigdataspecialist for more

Hey folks, some of you probably already know that, I have Instagram page where i share educational posts about data science and machine learning. Your support in form of follow and possibly engagement on my posts would be very appreciated. Instagram Page Link: http://Instagram.com/bigdataspecialist ➖➖➖➖➖➖➖➖➖➖➖➖➖➖ Join @datascience_bds for more cool data science materials. *This channel belongs to @bigdataspecialist group

👩‍💻 5 FREE DATA SCIENCE COURSES FOR BEGINNERS 👩‍🏫 CS109 Data Science (Harvard) - http://cs109.github.io/2015/pages/videos.html Data-Driven Decision Making (PwC) - https://www.coursera.org/learn/decision-making Machine Learning (Stanford) - https://www.coursera.org/learn/machine-learning Data Science Foundations (IBM) - https://cognitiveclass.ai/learn/data-science Data Science Specialization (JHU) - https://www.coursera.org/specializations/jhu-data-science Subscribe for more helpful data science learning materials and free courses #data_science ➖➖➖➖➖➖➖➖➖➖➖➖➖➖ Join @datascience_bds for more cool data science materials. *This channel belongs to @bigdataspecialist group

Best Statistic books for data science Practical statistics for data scientists by Peter Bruce and Andrew Bruce 🔗 Book Link Think Stats by Allen B. Downey 🔗 Book Link Computer Age Statistical Inference by Bradley Efron and Trevor Hastie 🔗 Book Link Statistics in Plain English by Timothy C. Urdan 🔗 Book Link #Statistics #books #data_science ➖➖➖➖➖➖➖➖➖➖➖➖➖➖ Join @datascience_bds for more cool data science materials. *This channel belongs to @bigdataspecialist group

Hey, of course, If i find nice graphical representation I will send you, but now i can tell you how did I use every of these algorithms at my work. I used SVM for text and product classification (Some article belongs to sport category, some to business, medicine etc, similar with products, I used it to classify products into categories similar to what you have on Amazon. I used KNN for simple classification problems, but generally we don't use it much in production as there are more advanced ones. I used regression to predict continuous value as price of product. I used random forest (and Gradient boosting algorithms like LightGBM and XGBOOST) for predicting possibility that person will convert on some ad (for example that person will buy a product advertised in an ad). Both Random Forest and Gradient Boosting are based on decision trees, they are very similar but gradient boosting is more advanced. I used CNN for image recognition (finding patterns in images to recognize objects). I haven't used RNN (Recurrent neural networks ) much but they are used for problems that are recursive by their nature. For example good usage of it in my work would be for some NLP tasks (sentences could be considered as recursive so its used on text and speech data). Also they are used to simulate neuron activity in our brain). I used K-means for clusterization of articles or products into different unlabeled clusters. It helps to determine which articles/products are similar to each other. I used PCA (Principal Component Analysis) to reduce number of dimensions for datasets that have too many of them. It also helped me to remove personal data from some datasets and model them as doubles (instead of names, surnames, date of birth etc). I hope this helps. I will send this to main channel in case somebody else find it useful.