Evaluating Machine Learning Models with Python Metrics

I would add this observation i posted on Mastodon: "Use any python library and you quickly find yourself buried under layers upon layers of encapsulated OOP inheritance crap, the tracebacks are order of magnitude worse than tidyverse NSE tracebacks before the rlang package (this is when the module author allows you to see traces). If you add to this the terrible docs you can understand the nihilist and p(y)edantic (pun intended) type annotations drive"

It may be a good language for data science, but it’s not a great one…I think people way over-index Python as the language for data science. It has limitations that I think are quite noteworthy. There are many data-science tasks I’d much rather do in R than in Python…I believe the reason Python is so widely used in data science is a historical accident, plus it being sort-of Ok at most things, rather than an expression of its inherent suitability for data-science work… https://lnkd.in/dpHfvu69

Not all Python libraries are created equal. Skip the trial and error! ✅Here's a breakdown of the best AI libraries for every stage of your machine learning project. NumPy, PyTorch, Scikit-learn, Hugging Face, MLFlow... which ones actually matter for your use case? Read the full guide ➡️ https://lnkd.in/dDS9imcF #MachineLearning #Python #ArtificialIntelligence

Day 18 of My 45-Day Python & DSA Journey Topic: Sorting Algorithms – Bubble Sort, Selection Sort & Insertion Sort Today, I stepped into another key part of DSA — sorting algorithms, which arrange data in a specific order (ascending or descending). Sorting improves the efficiency of many operations like searching and analysis. 🔹 What I Learned: 1. Bubble Sort Compares adjacent elements and swaps them if they’re in the wrong order. def bubble_sort(arr): n = len(arr) for i in range(n): for j in range(0, n-i-1): if arr[j] > arr[j+1]: arr[j], arr[j+1] = arr[j+1], arr[j] return arr print(bubble_sort([5, 2, 9, 1])) Time Complexity: O(n²) Simple but not efficient for large datasets. 2. Selection Sort Selects the smallest element and places it in the correct position. def selection_sort(arr): for i in range(len(arr)): min_idx = i for j in range(i+1, len(arr)): if arr[j] < arr[min_idx]: min_idx = j arr[i], arr[min_idx] = arr[min_idx], arr[i] return arr Time Complexity: O(n²) Fewer swaps compared to Bubble Sort. 3. Insertion Sort Builds the sorted array one element at a time by inserting elements into their correct position. def insertion_sort(arr): for i in range(1, len(arr)): key = arr[i] j = i - 1 while j >= 0 and key < arr[j]: arr[j+1] = arr[j] j -= 1 arr[j+1] = key return arr Time Complexity: O(n²) Efficient for small or nearly sorted arrays. Reflection: Today’s practice showed me how sorting is the foundation of data organization. Each algorithm has a unique approach — simple logic, yet deep impact. Key Takeaway: “Sorting teaches patience — step-by-step logic leads to order from chaos.” Next: I’ll explore Advanced Sorting Algorithms — Merge Sort and Quick Sort, which are faster and widely used in real-world systems. #Python #DSA #SortingAlgorithms #BubbleSort #InsertionSort #SelectionSort #CodingJourney #LearningInPublic #CodeEveryday

Perfect 👍 — you want a full explanation of Python functions including: ✅ Function definition ✅ Function arguments (required, keyword, default, variable-length) ✅ Return statement ✅ Lambda function ✅ Recursion Let’s go step-by-step with simple examples 👇 🐍 1️⃣ What is a Function in Python? 👉 A function is a block of code that performs a specific task. It is defined using the def keyword. def greet(): print("Hello, Welcome to Python!") greet() # function call ✅ Output: Hello, Welcome to Python! 🧠 Explanation: def greet(): defines the function. greet() calls the function. ⚙️ 2️⃣ Function Arguments (Parameters) Python functions can take different kinds of arguments: 🔸 (a) Required Arguments 👉 You must pass all values when calling the function. def add(a, b): print(a + b) add(5, 3) # ✅ works # add(5) ❌ error - missing one argument 🧠 Explanation: Both a and b are required parameters. 🔸 (b) Keyword Arguments 👉 Pass arguments using parameter names (order doesn’t matter). def student(name, age): print("Name:", name) print("Age:", age) student(age=21, name="Vaibhav") ✅ Output: Name: Vaibhav Age: 21 🔸 (c) Default Arguments 👉 Provide default values to parameters. def greet(name, msg="Good Morning"): print("Hello", name + ",", msg) greet("Vaibhav") greet("Priya", "Hi!") ✅ Output: Hello Vaibhav, Good Morning Hello Priya, Hi! 🔸 (d) Variable-length Arguments There are two types: (i) *args — multiple positional arguments def total(*numbers): print("Sum:", sum(numbers)) total(10, 20, 30) total(1, 2, 3, 4, 5) ✅ Output: Sum: 60 Sum: 15 (ii) **kwargs — multiple keyword arguments def info(**details): for key, value in details.items(): print(key, ":", value) info(name="Vaibhav", age=21, city="Mumbai") ✅ Output: name : Vaibhav age : 21 city : Mumbai 🔁 3️⃣ Return Statement 👉 The return keyword sends a value back from the function. def square(x): return x * x result = square(5) print("Square is:", result) ✅ Output: Square is: 25 🧠 Explanation: The function returns a value instead of printing it. ⚡ 4️⃣ Lambda Function 👉 A lambda is a small anonymous function (no name). Syntax: lambda arguments : expression Example: square = lambda x: x * x print(square(6)) ✅ Output: 36 🧠 Explanation: Lambda functions are used for short, simple operations. 🔄 5️⃣ Recursion Function 👉 A recursive function calls itself until a condition is met. Example: factorial using recursion def factorial(n): if n == 1: return 1 else: return n * factorial(n - 1) print("Factorial:", factorial(5)) ✅ Output: Factorial: 120 🧠 Explanation: The function calls itself with smaller values of n. Base condition if n == 1: stops recursion. 🧩 #PythonFunctions #FunctionArguments #KeywordArguments #DefaultArguments #VariableArguments #ArgsKwargs #ReturnStatement #LambdaFunction #Recursion

I’ve just published my latest blog on Medium: 🧠 “5 Python Libraries Every Machine Learning Student Should Know” As a student specializing in Artificial Intelligence and Machine Learning, I’ve explored how powerful libraries like NumPy, Pandas, Matplotlib, Scikit-learn, and TensorFlow can transform the way we approach data and model building. This blog covers: ✅ The core purpose of each library ✅ Why they’re essential for ML projects ✅ How beginners can start practicing today If you’re starting your ML journey or looking to strengthen your Python skills, this is for you! 🚀 👉 Read the full blog here: 🔗 https://lnkd.in/eRM-bDUc Would love your thoughts and feedback in the comments! 💬 #MachineLearning #AI #Python #DataScience #MLStudents #MediumBlog #TechWriting #ArtificialIntelligence

Last year, while leading a school analytics project in .NET, I hit a wall. The principal wanted to predict weak students early — not after they failed. I had the database ready, APIs built, reports automated… but when it came to machine learning and pattern detection, .NET didn’t feel like home turf. That’s when I discovered Python. At first, it felt unusual — indentation instead of braces, dynamic typing, and a syntax that looked too simple to handle real intelligence. But once I started exploring, I realized its simplicity was its superpower. 🔑 Python had everything I needed — from data analysis to AI model training — and endless libraries like Pandas, NumPy, Scikit-learn, and TensorFlow that did in minutes what used to take hours. Now, whenever I design systems, I think in two worlds: .NET for structure and scalability 🧱 Python for intelligence and automation 🤖 It’s the perfect partnership. --- 🟦 1️⃣ Learn Python — The Foundation Before jumping into AI, master Python’s fundamentals. Focus on data structures, libraries, and syntax. Understand how Lists, Tuples, Dictionaries, and Loops differ from C#. Then move to libraries like NumPy, Pandas, and Matplotlib. 🔗 Learn Python Basics – W3Schools https://lnkd.in/ggnJcSp 💡 Tip: Unlike C#, Python is dynamically typed — freeing you to think more about logic than structure. #️⃣ #PythonBasics #DotNetToAI #LearnPython --- 🟩 2️⃣ Machine Learning Fundamentals Once you’re comfortable with Python, move to data-driven intelligence. Learn supervised vs unsupervised learning, regression, and classification. Experiment using Scikit-learn with real-world datasets (like student marks, sales, or performance logs). You’ll start seeing how algorithms find patterns you’d never hardcode in .NET. 🔗 Scikit-learn Tutorials – Official Docs https://lnkd.in/gMZQnP29 💡 Tip: Use train_test_split and RandomForestClassifier — they’re your first steps into predictive analytics. #️⃣ #MachineLearning #ScikitLearn #DotNetDevelopers --- 🟧 3️⃣ Deep Learning & Real Projects Now it’s time to go deeper. Explore neural networks using TensorFlow or PyTorch. Build small but practical AI projects like: Predicting weak students or product churn Chatbots for support Image recognition systems Then deploy them as APIs, and integrate them with your .NET applications. That’s where true synergy happens — logic meets learning. 🔗 TensorFlow Beginner’s Guide 🔗 PyTorch Official Tutorials 💡 Tip: Use .NET for deployment, Python for intelligence — best of both worlds. --- Why this works: You’re not skipping steps. You’re building foundation → intelligence → application. Spend 1–2 months mastering each layer. That’s how a .NET expert becomes an AI engineer #DotnetToAITransition #PythonForDotNet #CareerGrowth ---

Python Dictionaries & Sets! ⚡ Today I explored two fundamental Python data structures: dictionaries and sets. Both are incredibly powerful, but they behave very differently. 1. Dictionaries: Dictionaries store key-value pairs and preserve insertion order. They’re perfect for structured data like student info or inventory: info = {"name": "Sidraa", "age": 24} info["new_member"] = "Danny" print(info) 👉Output: {'name': 'Sidraa', 'age': 24, 'new_member': 'Danny'} 2. Sets: Sets are unordered collections of unique items. They’re great for membership tests, removing duplicates, and performing mathematical set operations: cluster = {1, 3, 5} cluster.add(100) print(cluster) 👉Output: {1, 3, 5, 100} ✅ Key takeaway: 👀Dictionaries = labeled, ordered, mutable 👀Sets = unique, unordered, mutable and have immutable elements -------------------------- 🤓 Check Out More About Python Dictionaries and Sets in my recent Jupyter Notebook! -------------------------- ☺️ Here are Python (Beginner to Intermediate) GitHub Repos for you: 📁Python Variables: https://lnkd.in/e9rjz-_D 📁Python Operators: https://lnkd.in/e6hzgHSn 📁Python Conditionals: https://lnkd.in/egQNGZBF 📁Python Loops: https://lnkd.in/eezUg_-y 📁Python Functions: https://lnkd.in/eKdU6nex 📁Python Lists & Tuples: https://lnkd.in/eZ8KiQNs 📁Python Dictionaries & Sets: https://lnkd.in/eDmgj7pc ------------------------- ⚡ Follow my learning journey: 📎 GitHub: https://lnkd.in/ehu8wX85 🔗GitLab: https://lnkd.in/eiiQP2gw 💬 Feedback: I’d love your thoughts and tips! 🤝 Collab: If you’re also exploring Python, DM me! Let’s grow together! -------------------------- 📞Book A Call With Me: https://lnkd.in/e23BtnR9 -------------------------- #pythondictionaries #pythonsets #pythonprogramming #pythonforbeginners #pythonfornewbies #pythonlanguage