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Thatβs a wrap for Day 15! π Onwards to mastering more Pandas tools πΌ as part of the 20 Days of Pandas Challenge. Tomorrow, weβll dive into fetching rows and columns using iloc. See you then!
#PythonForML #MLJourney #Pandas
Over the past 6 months, I have been learning Machine Learning. If you want to succeed, follow this roadmap for learning it. π
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6/ Advantages of Generators
Generators are memory-efficient and fast for big data processing. No need to load entire datasets into memory at once. Game changer for #DataScience and #AI! β‘ #PythonForML #100DaysOfCode
4/ Generators
Moved on to generators! βοΈ Unlike lists, they generate values one at a time, saving memory. Perfect for handling large datasets in #DataScience. π± #PythonForML #GenerativeAI #100DaysOfCode
2/ Basic List Comprehension
Instead of writing loops to fill a list, list comprehensions let you do it in one line. Example: [x**2 for x in range(10)] creates a list of squares. Easy! π’ #PythonForML #AI #100DaysOfCode
4/ Inheritance
Inheritance in #OOP: creating new classes from existing ones. Makes code reusable and keeps things organized. 𧩠Ready to extend functionality! #PythonForML #DataScience
1/ Classes and Objects
Today's focus: #OOP in #Python!
Started with the basics of classes and objects. π οΈ Classes are blueprints, and objects are instances of those classes. π #PythonForML #DataScience
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