Building Naive Bayes from scratch with Python and NumPy

Python Libraries – Difficulty to Learn When learning Python, choosing the right libraries can make a huge difference in your journey. Some are beginner-friendly, while others require deeper understanding of systems, distributed computing, or machine learning. 🟢 Easy: Requests, Pandas, NumPy, Matplotlib, BeautifulSoup 🟡 Easy–Medium: FastAPI, Pydantic, Pytest 🟠 Medium: SQLAlchemy, Scikit-Learn, PyTorch, TensorFlow, Statsmodels 🔴 Hard: Dask, Ray 🟣 Very Hard: LangChain, LangGraph ☠️ Extreme: Building your own Python framework The key is not to learn everything at once. Start with the fundamentals, build projects, and gradually move to more advanced tools. Great developers aren’t the ones who know every library — they’re the ones who know when and why to use them. Which Python library are you currently learning? 👇 #Python #Programming #DataScience #MachineLearning #AI #SoftwareDevelopment #Developers #Coding #TechLearning #PythonLibraries

Starting my NumPy journey with a simple observation: Python List vs NumPy Array While learning Python, I mostly worked with lists to store data. They are simple and flexible. But after starting NumPy, I noticed that the same data can also be stored in something called a NumPy array. At first glance, both look very similar. But internally they are built for different purposes. Python List • Flexible and easy to use • Can store different data types • Mostly used for general programming tasks NumPy Array • Stores elements of the same type • Optimized for numerical and mathematical operations • Much faster when working with large datasets So, Output should be: <class 'list'> <class 'numpy.ndarray'> This is one of the main reasons why NumPy is widely used in Data Science, Machine Learning, and AI applications. Right now I’ve started exploring NumPy step by step as part of my Python → Data → ML learning journey. Next, I’ll explore multi-dimensional arrays in NumPy. #Python #NumPy #MachineLearning #DataScience #LearningInPublic

🚀 Day 1 of My Artificial Intelligence Learning Journey Today I started strengthening my Python fundamentals, which are essential for learning Artificial Intelligence and Machine Learning. Here are some concepts I learned today: 🔹 Python Variables – used to store and manipulate data 🔹 Variable Naming Rules – proper naming conventions in Python 🔹 Python Data Types – int, float, string, list, tuple, dictionary, set, boolean 🔹 Strings in Python – text data using single or double quotes 🔹 Variable Scope – local vs global variables 🔹 Python Operators – arithmetic, assignment, comparison, logical, membership, and bitwise operators 📌 Key Takeaway: A strong understanding of Python fundamentals is important before diving deeper into AI and Machine Learning. This is Day 1, and I’m excited to continue learning and sharing my journey. #Python #ArtificialIntelligence #MachineLearning #AIJourney #LearningInPublic

𝗖𝗿𝗲𝗮𝘁𝗲 𝗘𝘅𝗽𝗹𝗮𝗶𝗻𝗮𝗯𝗹𝗲 𝗠𝗮𝗰𝗵𝗶𝗻𝗲 𝗟𝗲𝗮𝗿𝗻𝗶𝗻𝗴 𝗠𝗼𝗱𝗲𝗹𝘀! Machine learning models usually have low explainability, hence making it difficult to understand their predictions. This is a serious obstacle in many cases, including industries where black box models are unacceptable. Shapash is a Python library that lets you understand machine learning models by providing an interactive web dashboard. Furthermore, shapash can also be used to generate reports, hence being a significantly useful tool for data scientists and analysts! Visit the link below for more information, and make sure to follow me for regular data science content. 𝗦𝗵𝗮𝗽𝗮𝘀𝗵 𝗹𝗶𝗯𝗿𝗮𝗿𝘆 𝘄𝗲𝗯𝘀𝗶𝘁𝗲: https://lnkd.in/dDiid5Vj 𝗟𝗲𝗮𝗿𝗻 𝗠𝗟 𝗮𝗻𝗱 𝗙𝗼𝗿𝗲𝗰𝗮𝘀𝘁𝗶𝗻𝗴: https://lnkd.in/dyByK4F #datascience #python #deeplearning #machinelearning

🚀 Day 1 of #100DaysOfDataScience Today, I focused on building a strong foundation in Python by learning: 🔹 Data Types (int, float, string, boolean) 🔹 Lists and their methods 🔹 Tuples and their properties 🔹 Dictionaries and their methods 💡 Key Learning: Understanding data types is essential because all data science workflows depend on how data is stored, accessed, and manipulated. 📌 I also practiced writing small code snippets to explore different methods and operations. This is just the beginning — consistency is the goal 💪 if you also learning data science , feel free to check this out. I'd also appreciate any suggestions or feedback to improve my journey . #Python #DataScience #Beginner #LearningInPublic #100DaysChallenge

🚀 Day 20 – The 30-Day AI & Analytics Sprint In data processing, why is map faster than a for loop? 🔍Why? 1.The map() function in Python is implemented in C ,which is a lower-level language than Python. Because of this, many operations are executed faster at the system level, reducing the overhead that occurs when Python executes instructions line by line. 2.Less Python Interpreter Overhead In a for loop, Python must repeatedly: Fetch the next element Execute Python bytecode Run the loop body Append or store the result 3.Lazy Evaluation map() returns an iterator, meaning it computes values only when needed. This can reduce memory usage and sometimes improve performance when working with large datasets. 4.Functional Style map() applies a function directly to all elements, which can make the operation more concise and efficient compared to manually managing a loop. 💡Important Note In modern Python, list comprehensions are often preferred because they are both fast and more readable: 🙏Great Thanks for: Muhammed Al Reay ,Instant Software Solutions and Mariam Metawe'e #Python #Programming #AI #DataAnalytics #LearningInPublic #30DaysOfAI

🚀 Day 2 of My Artificial Intelligence Learning Journey Continuing my Python learning journey for AI and Machine Learning, today I explored some important data structures and concepts in Python. Here’s what I learned today: 🔹 Stacks and Queues – Understanding how data can be organized and processed using LIFO (Stack) and FIFO (Queue). 🔹 Queue Implementation – Practiced using Python’s queue module and collections.deque. 🔹 Lists – Learned how lists store collections of items and explored common methods like append(), insert(), remove(), and pop(). 🔹 Dictionaries – Key-value data structure used to store and access data efficiently. 🔹 Sets – Unordered collection of unique elements and useful methods like add(), remove(), and discard(). 📌 Key Takeaway: Understanding data structures in Python is essential because they help organize and process data efficiently—an important skill for building AI and machine learning models. Excited to continue learning and building a strong foundation in Python for AI. #Python #ArtificialIntelligence #MachineLearning #DataStructures #LearningInPublic #AIJourney

I'm committing to building popular ML algorithms from scratch daily without using anything but Python built-ins and NumPy. No sklearn. No shortcuts. Just pure code and first principles. Day 5: Support Vector Machine (SVM) ✅ SVM intuition is simple: imagine you have two groups of points on a 2D plane and you want to draw a line that separates them. But not just any line, the one with the biggest gap between the two groups. That gap is called the margin. And the data points sitting right on the edge of that margin are the Support Vectors, the only points that actually define where the line goes. Remove any other point, and the line stays the same. SVM finds the maximum margin boundary using the Hinge Loss and gradient descent, penalizing points that end up on the wrong side. This is fully open if you want to collaborate, add an algorithm, or drop a suggestion in the comments or issues tab. Feel free to do so. 🤝 👉 GitHub: https://lnkd.in/duTd7jie #MachineLearning #Python #NumPy #DataScience #OpenSource #LearnML #100DaysOfCode #SVM #SupportVectorMachine #Classification

🚀 Day 55/100 – Python, Data Analytics & Machine Learning Journey 🤖 Module 3: Machine Learning 📚 Today’s Learning: • Bias & Variance Today, I focused on understanding the Bias-Variance Tradeoff, one of the most important concepts for building effective machine learning models. I learned that Bias occurs when a model is too simple and fails to capture the underlying patterns in the data, leading to underfitting. On the other hand, Variance occurs when a model is too complex and learns noise from the data, leading to overfitting. I also understood that there is always a tradeoff between bias and variance, and the goal is to find the right balance so that the model performs well on both training and unseen data. Understanding this concept is essential for improving model performance and building models that generalize well in real-world scenarios. The learning journey continues as I explore more core concepts in machine learning 🚀 📌 Code & Notes: https://lnkd.in/dmFHqCrK #100DaysOfPython #MachineLearning #AIML #Python #LearningInPublic #DataScience

🚀 Day 58/100 – Python, Data Analytics & Machine Learning Journey 🤖 Module 3: Machine Learning 📚 Today’s Learning • Voting Classifier & Ensemble Learning Today, I explored ensemble learning techniques, focusing on how combining multiple models can significantly improve performance. I learned about Bagging (Bootstrap Aggregating), where multiple models are trained on different subsets of the data and their predictions are combined. This approach helps reduce variance and makes models more stable. I also studied Boosting, a sequential technique where each model learns from the mistakes of the previous one. This method reduces bias and builds a strong predictive model step by step. Additionally, I implemented the Voting Classifier, which combines predictions from different models (like Logistic Regression, Decision Tree, and KNN) to make a final decision. This improves overall accuracy and robustness compared to individual models. Understanding these ensemble techniques is crucial for building reliable and high-performance machine learning systems used in real-world applications. The journey continues as I keep strengthening my ML concepts and practical skills. 📌 Code & Notes: https://lnkd.in/dmFHqCrK #100DaysOfPython #MachineLearning #AIML #Python #LearningInPublic #DataScience 🚀