Artificial Intelligence

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Master the art of Machine Learning and python by Learning from Real World Advanced Projects designed & developed by real developers. 󰞪Are you one who knows the basics of Python/Machine Learning/Deep Learning/ Data Science/ Computer Vision or NLP but has not practiced on Real World Advanced Projects? 󰞪Are you in the final year of your college but have not made any real-world projects till now? Or are you a professional who wants to switch to a Data-Oriented job but does not have any real-world projects to showcase in your resume? Don’t worry dude... We have something for you. This Dragon bundle is divided into two modules, 📌Module 1: 350+ Machine Learning Projects. 📌Module 2: 200+ Advanced Python Projects. ➡️ Get complete list of projects from our website (Link is in bio). ⚫But Why? ➡️ Learn by working like a professional developer. ➡️ Build a GitHub portfolio that impress the recruiters. ➡️ Stand out with real-proof of work. ➡️ Its more than 550+ real-world projects, almost in every areas you are going to work in the field of Machine Learning. All Projects come with source code in Python3 and dataset. Also, each project comes with proper documentation, requirements, and the HowtoRun instructions file so that even beginners can understand the logic and workflow behind the projects. For more details, visit our website. Link: https://tensorprojects.com/dragonbundle

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Many Data Science aspirants struggle to find good projects to get a start in Data science or Machine Learning. Here is the list of few Data Science projects (found on kaggle), it covers Basics of Python, Advanced Statistics, Supervised Learning (Regression and Classification problems) 1. Basic python and statistics Pima Indians :- https://www.kaggle.com/uciml/pima-indians-diabetes-database Cardio Goodness fit :- https://www.kaggle.com/saurav9786/cardiogoodfitness Automobile :- https://www.kaggle.com/toramky/automobile-dataset 2. Advanced Statistics Game of Thrones:-https://www.kaggle.com/mylesoneill/game-of-thrones World University Ranking:-https://www.kaggle.com/mylesoneill/world-university-rankings IMDB Movie Dataset:- https://www.kaggle.com/carolzhangdc/imdb-5000-movie-dataset 3. Supervised Learning a) Regression Problems How much did it rain :- https://www.kaggle.com/c/how-much-did-it-rain-ii/overview Inventory Demand:- https://www.kaggle.com/c/grupo-bimbo-inventory-demand Property Inspection predictiion:- https://www.kaggle.com/c/liberty-mutual-group-property-inspection-prediction Restaurant Revenue prediction:- https://www.kaggle.com/c/restaurant-revenue-prediction/data IMDB Box office Prediction:-https://www.kaggle.com/c/tmdb-box-office-prediction/overview b) Classification problems Employee Access challenge :- https://www.kaggle.com/c/amazon-employee-access-challenge/overview Titanic :- https://www.kaggle.com/c/titanic San Francisco crime:- https://www.kaggle.com/c/sf-crime Customer satisfcation:-https://www.kaggle.com/c/santander-customer-satisfaction Trip type classification:- https://www.kaggle.com/c/walmart-recruiting-trip-type-classification Categorize cusine:- https://www.kaggle.com/c/whats-cooking These are the links of competitions, from there previous notebooks can be checked to begin with, Hope it will be helpful 😊😊

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To become a Machine Learning Engineer: • Python • numpy, pandas, matplotlib, Scikit-Learn • TensorFlow or PyTorch • Jupyter, Colab • Analysis > Code • 99%: Foundational algorithms • 1%: Other algorithms • Solve problems ← This is key • Teaching = 2 × Learning • Have fun!