Learning Machine Learning with Decision Trees

🚀 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

Recently completed a presentation on Jupyter Notebook for Machine Learning. In this, I covered: Basics and key features of Jupyter Notebook How it helps in building ML models step by step A simple Linear Regression example Data visualization using Python It is a powerful tool for learning, experimenting, and understanding machine learning concepts in a practical way. Looking forward to exploring more in Data Science and AI. #MachineLearning #DataScience #JupyterNotebook #Python #AI #Learning

🤖 Machine Learning is shaping the future. From data to decisions, from code to intelligence. The world is moving towards automation and smart systems. Learning technologies like Python and Machine Learning is no longer optional — it’s the future. 🚀 Start today, stay ahead tomorrow. #MachineLearning #AI #Python #Technology #Future #Learning

🚀 Day 48/100 – Python, Data Analytics & Machine Learning Journey 🤖 Module 3: Machine Learning 📚 Today’s Learning: Supervised Learning – Classification Algorithm 5: Random Forest Today I explored Random Forest, a powerful ensemble learning algorithm used for classification and regression tasks. Random Forest works by building multiple decision trees during training and combining their predictions to produce a more accurate and stable result. One of the key advantages of Random Forest is its ability to reduce overfitting and handle large datasets with higher accuracy. It also works well with both numerical and categorical data. Random Forest is widely used in real-world applications such as fraud detection, recommendation systems, medical diagnosis, and customer behavior analysis. The journey continues as I explore more algorithms and their real-world applications. 📌 Code & Notes: https://lnkd.in/dmFHqCrK #100DaysOfPython #MachineLearning #AIML #Python #LearningInPublic #DataScience

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

Stop using Python without the right libraries. Raw Python slows you down. Libraries unlock real data science. NumPy for numerical computing. Pandas for cleaning and analyzing data. Matplotlib / Seaborn for visualization. Scikit-learn for machine learning. TensorFlow / PyTorch for deep learning. Tools don’t replace thinking. But the right stack makes thinking scalable. #Python #DataScience #MachineLearning #DeepLearning #PythonLibraries #NumPy #Pandas #ScikitLearn #TensorFlow #PyTorch #DataAnalytics #AI #LearnDataScience #TechSkills #InsightSeeker

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

Recently completed a presentation on Jupyter Notebook for Machine Learning. In this, I covered: Basics and key features of Jupyter Notebook How it helps in building ML models step by step A simple Linear Regression example Data visualization using Python It is a powerful tool for learning, experimenting, and understanding machine learning concepts in a practical way. Looking forward to exploring more in Machine Learning and AI. #MachineLearning #JupyterNotebook #Python #AI #Learning

𝗦𝗶𝗺𝗽𝗹𝗶𝗳𝘆 𝗧𝗶𝗺𝗲 𝗦𝗲𝗿𝗶𝗲𝘀 𝗙𝗼𝗿𝗲𝗰𝗮𝘀𝘁𝗶𝗻𝗴 𝘄𝗶𝘁𝗵 𝗗𝗮𝗿𝘁𝘀! The Darts library has simplified time series analysis and forecasting with Python. Darts supports various forecasting approaches, ranging from statistical models like ARIMA, to novel methods based on deep learning. Darts also supports advanced techniques like explainable forecasting, conformal prediction and anomaly detection. Therefore, Darts has been established as one of the best libraries for time series tasks, making it extremely useful to data scientists and researchers. Visit the link below for more information and follow me for regular data science content! 𝗗𝗮𝗿𝘁𝘀 𝗹𝗶𝗯𝗿𝗮𝗿𝘆 𝘄𝗲𝗯𝘀𝗶𝘁𝗲: https://lnkd.in/dEQepm3D 𝗟𝗲𝗮𝗿𝗻 𝗠𝗟 𝗮𝗻𝗱 𝗙𝗼𝗿𝗲𝗰𝗮𝘀𝘁𝗶𝗻𝗴: https://lnkd.in/dyByK4F #datascience #python #machinelearning #deeplearning

Why is Python the most popular language in data science and AI? Because of its incredible ecosystem. From data analysis to machine learning, deep learning, APIs, and dashboards, Python libraries make complex tasks simpler and more powerful. #Python #DataScience #MachineLearning #AI #Programming #Analytics