🚀 Starting my journey in AI & Machine Learning Currently learning the fundamentals and practicing basic data operations using Pandas in Python. In this video, I worked with a sample hotel dataset to explore: • Reading CSV files • Understanding datasets using head(), tail(), info(), and describe() • Filtering data • Dropping rows and columns • Using loc[] and iloc[] for indexing Building strong foundations before moving into Machine Learning projects. #Python #Pandas #MachineLearning #ArtificialIntelligence #PythonDeveloper #FullStackDeveloper

🚀 Exploring Scikit-Learn – The Backbone of Machine Learning in Python! From data preprocessing to model evaluation, Scikit-Learn makes building ML models intuitive and efficient. Whether it's Supervised Learning (Linear Regression, KNN, Decision Trees) or Unsupervised Learning (K-Means, PCA), this library provides a clean API and powerful tools to turn data into insights. Understanding core modules like linear_model, tree, ensemble, cluster, and metrics is essential for every aspiring Data Scientist and ML Engineer. Consistent practice + the right tools = impactful machine learning solutions 💡 #ScikitLearn #MachineLearning #Python #DataScience #ArtificialIntelligence #MLAlgorithms #DataAnalytics #LearningJourney #TechSkills #WomenInTech

In today’s data-driven world, knowing Python isn’t enough; knowing how to use it for real-world problem solving is what sets professionals apart. Our Scientific Computing & Data Analysis module goes beyond theory. You’ll work with industry-standard tools like NumPy, Pandas, Matplotlib, and Seaborn to analyze data, build simulations, and extract meaningful insights. If you're serious about building a future in Data Science, AI, research, or analytics, this is the skillset that gives you leverage. Learn practical Python for data and science at https://fastlearner.ai/ #Python #DataScience #ScientificComputing #NumPy #Pandas #DataAnalytics #MachineLearning #FastLearner #Upskill #CareerGrowth

🚀 Day 44/100 – Python, Data Analytics & Machine Learning Journey 🤖 Started Module 3: Machine Learning 📚 Today’s Learning: Supervised Learning – Classification Algorithm 1: Decision Tree I began exploring classification algorithms in machine learning. Decision Trees help in making predictions by splitting data into branches based on conditions, making them easy to understand and interpret. Machine Learning is the core of modern AI systems, and I’m excited to continue learning more algorithms, models, and their real-world applications in the coming days. 📌 Code & Notes: https://lnkd.in/dmFHqCrK #100DaysOfPython #MachineLearning #AIML #Python #LearningInPublic #DataScience

🎲 random.seed() — Small line. Big impact. Every time we split data, initialize models, or run cross-validation, randomness is involved. Without setting a seed → results change every run. With a seed → experiments become reproducible. Python uses a deterministic algorithm (Mersenne Twister). Same seed = Same sequence. It doesn’t improve accuracy. It improves credibility. Reproducibility is not optional in production-grade data science. #DataScience #MachineLearning #Python #MLOps #AI

🚀 Day 45/100 – Python, Data Analytics & Machine Learning Journey 🤖 Module 3: Machine Learning 📚 Today’s Learning: Supervised Learning – Classification Algorithm 2: Logistic Regression Today I explored Logistic Regression, one of the fundamental algorithms used for classification problems in machine learning. It helps predict the probability of an outcome, such as whether a patient has a disease based on medical data. Understanding these core algorithms is helping me build a strong foundation in machine learning and prepare for solving real-world problems using data. Machine Learning continues to be an exciting field, and I’m looking forward to exploring more algorithms and practical implementations in the coming days. 📌 Code & Notes: https://lnkd.in/dmFHqCrK #100DaysOfPython #MachineLearning #LogisticRegression #AIML #Python #LearningInPublic #DataScience

We all use optimisers in Machine Learning — but how often do we actually see them working? I built Gradient Descent from scratch in Python, implementing: • Vanilla Gradient Descent • Momentum • Learning Rate Decay • RMSprop No ML libraries. Just NumPy, math, and curiosity. I visualised the entire training process — loss curves, weight & bias updates, parameter movement, and even full training animations. Watching the line slowly move toward the true parameters makes the theory feel real. Big takeaway? Optimisers aren’t magic. They’re disciplined update rules applied repeatedly. I did take GPT’s help in structuring parts of the code — AI speeds things up, but real understanding comes from building and experimenting yourself. Code here: https://lnkd.in/d4maNaR4 #MachineLearning #GradientDescent #Python #AI #LearningInPublic #DeepLearning #NeuralNetworks #ArtificialIntelligence #MLResearch #LearningDynamics #Optimization #GradientDescent

Machine Learning doesn't have to be complicated. With Scikit-learn, you can build powerful ML models with just a few lines of code. From classification to prediction and data analysis, it makes Machine Learning more accessible. Many beginners start their ML journey with Python + Scikit-learn. Small tools. Big possibilities. 🤖 #AI #MachineLearning #Python #DataScience #AfricaAgility #GIT20DayChallenge

Day 6/20 my AI/ML journey 🚀 One thing I’m starting to appreciate more is how much work happens before machine learning models are even involved. This week I spent time working on reading and exploring datasets using Python. Simple things like: Understanding the structure of the dataset Checking the data types of each column Looking for missing values Inspecting how different features are distributed At first it seemed basic, but the more I explore datasets the more I realize how important this stage is. If you don’t understand your dataset, you can’t build a reliable model. Data first. Models later. #africaagility #learninginpublic #AI #MachineLearning #DataScience #Python

This week in my AI learning journey As part of my MSc in Artificial Intelligence, I worked on analyzing a dataset and visualizing insights using Python. One thing that surprised me: Data preparation often takes more time than building the machine learning model itself. Tools I used: • Python • Pandas • Matplotlib Coming from an Oracle SQL background, it’s interesting to see how structured data can be transformed into meaningful insights using machine learning. Small steps every week toward AI #AI #MachineLearning #Python #DataAnalytics #LearningJourney