JAVA WITH DSA BINARY BATCH & MERN FULL STACK DEVELOPMENT

Additional Resources To Assist Research https://www.reddit.com/r/MachineLearning/ • https://www.reddit.com/r/deeplearning/ • https://paperswithcode.com/ • https://papers.nips.cc/ • https://icml.cc/ • https://iclr.cc/ • https://www.researchgate.net/

Logistic regression fits a logistic model to data and makes predictions about the probability of an event (between 0 and 1). Naive Bayes uses Bayes Theorem to model the conditional relationship of each attribute to the class variable. The k-Nearest Neighbor (kNN) method makes predictions by locating similar cases to a given data instance (using a similarity function) and returning the average or majority of the most similar data instances. The kNN algorithm can be used for classification or regression. Classification and Regression Trees (CART) are constructed from a dataset by making splits that best separate the data for the classes or predictions being made. The CART algorithm can be used for classification or regression. Support Vector Machines (SVM) are a method that uses points in a transformed problem space that best separate classes into two groups. Classification for multiple classes is supported by a one-vs-all method. SVM also supports regression by modeling the function with a minimum amount of allowable error.

Some useful PYTHON libraries for data science NumPy stands for Numerical Python. The most powerful feature of NumPy is n-dimensional array. This library also contains basic linear algebra functions, Fourier transforms,  advanced random number capabilities and tools for integration with other low level languages like Fortran, C and C++ SciPy stands for Scientific Python. SciPy is built on NumPy. It is one of the most useful library for variety of high level science and engineering modules like discrete Fourier transform, Linear Algebra, Optimization and Sparse matrices. Matplotlib for plotting vast variety of graphs, starting from histograms to line plots to heat plots.. You can use Pylab feature in ipython notebook (ipython notebook –pylab = inline) to use these plotting features inline. If you ignore the inline option, then pylab converts ipython environment to an environment, very similar to Matlab. You can also use Latex commands to add math to your plot. Pandas for structured data operations and manipulations. It is extensively used for data munging and preparation. Pandas were added relatively recently to Python and have been instrumental in boosting Python’s usage in data scientist community. Scikit Learn for machine learning. Built on NumPy, SciPy and matplotlib, this library contains a lot of efficient tools for machine learning and statistical modeling including classification, regression, clustering and dimensionality reduction. Statsmodels for statistical modeling. Statsmodels is a Python module that allows users to explore data, estimate statistical models, and perform statistical tests. An extensive list of descriptive statistics, statistical tests, plotting functions, and result statistics are available for different types of data and each estimator. Seaborn for statistical data visualization. Seaborn is a library for making attractive and informative statistical graphics in Python. It is based on matplotlib. Seaborn aims to make visualization a central part of exploring and understanding data. Bokeh for creating interactive plots, dashboards and data applications on modern web-browsers. It empowers the user to generate elegant and concise graphics in the style of D3.js. Moreover, it has the capability of high-performance interactivity over very large or streaming datasets. Blaze for extending the capability of Numpy and Pandas to distributed and streaming datasets. It can be used to access data from a multitude of sources including Bcolz, MongoDB, SQLAlchemy, Apache Spark, PyTables, etc. Together with Bokeh, Blaze can act as a very powerful tool for creating effective visualizations and dashboards on huge chunks of data. Scrapy for web crawling. It is a very useful framework for getting specific patterns of data. It has the capability to start at a website home url and then dig through web-pages within the website to gather information. SymPy for symbolic computation. It has wide-ranging capabilities from basic symbolic arithmetic to calculus, algebra, discrete mathematics and quantum physics. Another useful feature is the capability of formatting the result of the computations as LaTeX code. Requests for accessing the web. It works similar to the the standard python library urllib2 but is much easier to code. You will find subtle differences with urllib2 but for beginners, Requests might be more convenient. Additional libraries, you might need: os for Operating system and file operations networkx and igraph for graph based data manipulations regular expressions for finding patterns in text data BeautifulSoup for scrapping web. It is inferior to Scrapy as it will extract information from just a single webpage in a run.

📢 Important Update ❣️ ⚡ There Are Some Reports Circulating About the Telegram CEO's Arrest ⚡ Always Good To Have A Backup So 👇 My YOUTUBE HANDLE- https://youtu.be/BSAh7nCzYyQ?si=ENogZEVFY_XawsNK https://youtu.be/BSAh7nCzYyQ?si=ENogZEVFY_XawsNK Follow karke rakho..Future me kuch bhi help chaye to Ya dosti hi rakhni ho to !! Note : Koi telegram ban nahi ho raha hai fake news se savdhan rahe 👍

◾HANDWRITTEN NOTES ✍️◾️ 🔺DATA STRUCTURE SHORT NOTES 🔺DATA STRUCTURE INTERVIEW SERIES 🔹(PART - 1) 🔺DATA STRUCTURE INTERVIEW SERIES 🔹(PART - 2) 🔺DATA STRUCTURE INTERVIEW SERIES 🔹(PART - 3) 🔺DBMS (DATABASE MANAGEMENT SYSTEM)NOTES 🔺C PROGRAMMING SHORT NOTES

Abhi last 50 students or form fill kar payege 👆👆👆 Koi piche mat rah Jana....fastttt 🔻 https://bit.ly/3Sb5ER0 🔻 https://bit.ly/3Sb5ER0

https://bit.ly/3Sb5ER0 https://bit.ly/3Sb5ER0 👆👆👆👆👆👆👆👆👆👆 Kar liye sab register free of cost ??? Kal se paid ho raha hai esiliye ❤️

🔻Today Is Last Day....For Free Enroll ⛔️ It may be your last drop! So don't take chance & Learn how to crack it from the ones who have done it 😀 LINK👇👇👇 https://bit.ly/3Sb5ER0 https://bit.ly/3Sb5ER0 Thank me later ❤️❤️

📢 Biggest Olympiad ever 💻 🔥 We are very happy to announce that we are holding competition among you all 🎁 Gifts 1-2 Prize is APPLE IPAD 💻 3 Portable led lamp ✨ 4-5th Prize is chat gpt based P.A.⭐️ 6-10th Chat gpt subscription ✅ 11-100th CASH PRIZE 🏆 💸 4️⃣ ⌨️⌨️⌨️⌨️ CASH PRIZE ✅ REGISTER FOR FREE ❤️ All participants will get certificate and free coding classes 🔻Link https://bit.ly/3Sb5ER0 https://bit.ly/3Sb5ER0

Every aspirant is working hard and we are helping them succeed. Don’t wait. Join us and start getting mentored. https://bit.ly/3Sb5ER0 https://bit.ly/3Sb5ER0 https://bit.ly/3Sb5ER0 [JALDI SE REGISTER KRO DOSTO bcz fir bad me moka na milega] Join fast fast guys 💪

Kar liye join sab 👆👆👆

Kya kar rahe ho yaar aao jaldi https://t.me/+QhiMvJnpq_I2Yjdl https://t.me/+QhiMvJnpq_I2Yjdl https://t.me/+QhiMvJnpq_I2Yjdl

✔️Join For Getting Big Discounts Offers, Deals On Your Online Shopping & Save Your Hard Earned Money

It Will Be Very Helpful For Students & Aspirants

https://t.me/+QhiMvJnpq_I2Yjdl https://t.me/+QhiMvJnpq_I2Yjdl https://t.me/+QhiMvJnpq_I2Yjdl Join This Channel And Be Active Because Deals Like This Are Active For Only A Short Period of Time.

COMPUTER SKILL COURSES https://t.me/+JhJydc6Sib0zNWI1 https://t.me/+JhJydc6Sib0zNWI1 CODERS HUB COURSES https://t.me/+TBHSqvS5FkdjZWRl https://t.me/+TBHSqvS5FkdjZWRl SKILLS COLLEGE STUDENTS COURSES https://t.me/+WHPs7oWDZZdiNGI9 https://t.me/+WHPs7oWDZZdiNGI9 Scamler Acamdemy https://t.me/+8bMzjd7KAxs5ZTE9 https://t.me/+8bMzjd7KAxs5ZTE9

np.cumsum() is a useful function when it comes to doing big data cumulative sums. See it, learn it, and use it 💪 . . . 👨‍💻#NumPy

. np.tile() is one of the most beautiful yet super useful functions there is in NumPy and Python! Happy weekend!! 🎉👌 . Typo alert: 4th slide should say np.array([[6], [7]]) for the picture to match! . 👨‍💻#NumPy

Part 10 🎉 of Intro to NumPy, what a journey guys, thanks for sticking around for these posts!! On to part 100 shall we?? . Follow u0040bigdataguru for tutorials and instructional posts on AI, machine learning and deep learning! . . 👨‍💻#NumPy

🗣 Turn on post notifications to be always in the know . NumPy part 9: np.where() . np.where() returns the indices where the condition is met (not the elements themselves) 👌 . . . 👨‍💻#NumPy

TODAY SPECIAL Gift 💕 Check your Rank & College for Free ❤️‍🔥 Link 🔗 » https://bit.ly/Snehil6_CollegePredictor https://bit.ly/Snehil6_CollegePredictor https://bit.ly/Snehil6_CollegePredictor 📞 No Spam calls or any other bullsh!t [HIGHLY RECOMMENDED TO NEET & JEE ASPIRENT]

‼️‼️ FILL THIS FORM EVERYONE 🌐 JEE MAINS COLLEGE PREDICTOR 🔗 LINK https://bit.ly/Snehil4_CollegePredictor https://bit.ly/Snehil4_CollegePredictor

➡️ Personalised prediction ➡️ Seat availability ➡️Opening & closing rank ➡️ Expert guidance

FREE FOR EARLY BIRDS 🥺

✅ fatafat aao guys 👆👆👆 All the Updates for the Upcoming Sessions will be provided in this group. https://t.me/+4LxWl8LGTopjMDE1 https://t.me/+4LxWl8LGTopjMDE1 https://t.me/+4LxWl8LGTopjMDE1