In this python machine learning tutorial for beginners we will look into, 1) how to hyper tune machine learning model paramers 2) choose best model for given machine learning problem We will start by comparing traditional train_test_split approach with k fold cross validation. Then we will see how GridSearchCV helps run K Fold cross validation with its convenient api. GridSearchCV helps find best parameters that gives maximum performance. RandomizedSearchCV is another class in sklearn library that does same thing as GridSearchCV but without running exhaustive search, this helps with computation time and resources. We will also see how to find best model among all the classification algorithm using GridSearchCV. In the end we have interesting exercise for you to solve. #MachineLearning #PythonMachineLearning #MachineLearningTutorial #Python #PythonTutorial #PythonTraining #MachineLearningCource #HyperParameter #GridSearchCV #sklearntutorials #scikitlearntutorials Exercise: Code in this tutorial: Do you want to learn technology from me? Check for my affordable video courses. Exercise solution: Topics that are covered in this Video: 00:00 Introduction 00:45 train_test_split to find model performance 01:37 K fold cross validation 04:44 GridSearchCV for hyperparameter tuning 10:18 RandomizedSearchCV 12:35 Choosing best model 15:25 Exercise Next Video: Deep Learning Tutorial Python, Tensorflow And Keras: Introduction and Installation: Populor Playlist: Data Science Full Course: Data Science Project: Machine learning tutorials: Pandas: matplotlib: Python: Jupyter Notebook: Tools and Libraries: Scikit learn tutorials Sklearn tutorials Machine learning with scikit learn tutorials Machine learning with sklearn tutorials 🌎 My Website For Video Courses: Need help building software or data analytics and AI solutions? My company can help. Click on the Contact button on that website. #️⃣ Social Media #️⃣ 🔗 Discord: 📸 Dhaval's Personal Instagram: 📸 Codebasics Instagram: 🔊 Facebook: 📱 Twitter: 📝 Linkedin (Personal): 📝 Linkedin (Codebasics): 🔗 Patreon: