Day 31 - Decision Trees Regularization & Regression

Day 31 - Decision Trees Regularization & Regression *Useful Links* GitHub link: https://github.com/Puneeth1996/machin... Reference Materials: Welcome to the *Machine Learning Mastery* playlist! If you've ever wanted to learn *Machine Learning from scratch* and build real-world AI applications, this *Machine Learning tutorial* series is for you. Whether you're looking for a complete **Machine Learning course**, **Machine Learning for beginners**, or want to master **Python Machine Learning**, this playlist will guide you step by step. This series is designed for: ✅ Beginners with little or no ML knowledge ✅ College Students ✅ Software Developers ✅ Data Analysts ✅ Test Engineers ✅ Working Professionals looking to switch into AI/ML In this playlist, we won't just learn theory—we'll build real-world projects, visualize concepts, write clean code, and understand how Machine Learning works behind the scenes. What You'll Learn ✔ Python for Machine Learning ✔ NumPy, Pandas & Matplotlib ✔ Data Cleaning & Data Preprocessing ✔ Exploratory Data Analysis (EDA) ✔ Feature Engineering ✔ Statistics & Probability for Machine Learning ✔ Linear Algebra Essentials ✔ Supervised Learning Algorithms ✔ Unsupervised Learning Algorithms ✔ Linear Regression & Logistic Regression ✔ Classification Algorithms ✔ Decision Trees & Random Forests ✔ Support Vector Machines (SVM) ✔ K-Nearest Neighbors (KNN) ✔ Naive Bayes ✔ Clustering Algorithms ✔ Principal Component Analysis (PCA) ✔ Model Evaluation Metrics ✔ Hyperparameter Tuning ✔ Cross Validation ✔ Ensemble Learning ✔ Recommendation Systems ✔ Time Series Basics ✔ Introduction to Deep Learning ✔ Neural Networks using TensorFlow & Keras ✔ End-to-End Machine Learning Projects ✔ Model Deployment using Flask & FastAPI ✔ Git, GitHub & MLOps Basics ✔ Interview Questions & Coding Exercises Throughout this playlist, you'll build practical *Python projects* and complete *Machine Learning projects* that strengthen your portfolio and prepare you for interviews and real-world development. By the end of this playlist, you'll have a strong understanding of **Machine Learning**, **Data Science**, **Artificial Intelligence**, **Deep Learning**, **Regression**, **Classification**, **Clustering**, **Feature Engineering**, and modern ML workflows using **Python**, **NumPy**, **Pandas**, **Scikit-learn**, **TensorFlow**, and **Keras**. If you're searching for a complete *Machine Learning Python* tutorial, a hands-on *Machine Learning course**, or want to **learn Machine Learning* through practical examples and projects, this playlist is for you. 🎓 *Looking for structured live training and complete courses?* Visit my training portal: *https://puneeth1996.github.io/puneeth... There you'll find comprehensive training programs, project-based learning, mentorship, interview preparation, and industry-focused courses in: • Machine Learning • Data Science • Artificial Intelligence • Python Programming • MERN Stack Development • React & TypeScript • Data Structures & Algorithms (DSA) • Automation with Python 👍 If you find this playlist helpful, don't forget to **Like**, **Share**, **Subscribe**, and turn on the notification bell so you never miss a new video from **Learn Tech With Me**. *Keywords:* machine learning, machine learning tutorial, machine learning python, python machine learning, learn machine learning, machine learning course, machine learning for beginners, data science, artificial intelligence, tensorflow, keras, scikit learn, numpy, pandas, python projects, machine learning projects, deep learning, classification, regression, clustering, feature engineering.