Machine Learning Tutorial for Beginners | Applied Machine Learning Foundations

Welcome to my Channel...! In this video we are going to see the basics of Applied Machine Learning . These are the fundamentals of Applied Machine Learning and essential trainings. we will see more and more in upcoming videos. For any queries drop a mail at contact.missgoo@gmail.com Share your thoughts about this video in the comment section and if you have any doubts post it in comment section. BluePrism Playlist:- https://youtube.com/playlist?list=PLWMB5IYAuU6fwXy7lFh627J9buCMnPgRU Thank You...! →→→→→Visit Our Channel For More Videos←←←←← 🏹LIKE 🏹SHARE 🏹SUBSCRIBE Where There is a Will There is a Way 💘 //Chapters and time splits 00:00:00-00:01:46 Leveraging machine learning 00:01:47-00:02:52 What you should know 00:02:53-00:03:36 What tools you need 00:03:37-00:07:37 What is machine learning? 00:07:38-00:12:39 What kind of problems can this help you solve? 00:12:40-00:18:28 Why Python? 00:18:29-00:22:17 Machine learning vs Deep learning vs Artificial intelligence 00:22:18-00:25:16 Demos of machine learning in real life 00:25:17-00:31:20 Common challenges 00:31:21-00:34:49 Why do we need to explore and clean our data? 00:34:50-00:43:35 Exploring continuous features 00:43:36-00:51:10 Plotting continuous features 00:51:11-00:56:54 Continuous data cleaning 00:56:55-01:02:58 Exploring categorical features 01:02:59-01:09:18 Plotting categorical features 01:09:19-01:13:51 Categorical data cleaning 01:13:52-01:19:45 Why do we split up our data? 01:19:46-01:24:52 Split data for train/validation/test set 01:24:53-01:30:55 What is cross-validation? 01:30:56-01:35:28 Establish an evaluation framework 01:35:29-01:40:28 Bias/Variance tradeoff 01:40:29-01:42:54 What is underfitting? 01:42:55-01:45:41 What is overfitting? 01:45:42-01:48:57 Finding the optimal tradeoff 01:48:58-01:55:19 Hyperparameter tuning 01:55:20-01:57:50 Regularization 01:57:51-01:59:38 Overview of the process 01:59:39-02:04:42 Clean continuous features 02:04:43-02:09:00 Clean categorical features 02:09:01-02:12:48 Split data into train/validation/test set 02:12:49-02:18:09 Fit a basic model using cross-validation 02:18:10-02:24:43 Tune hyperparameters 02:24:44-02:31:26 Evaluate results on validation set 02:31:27-02:35:56 Final model selection and evaluation on test set 02:35:57-02:37:23 Next steps

Machine Learning Tutorial for Beginners | Applied Machine Learning Foundations

Welcome to my Channel...! In this video we are going to see the basics of Applied Machine Learning . These are the fundamentals of Applied Machine Learning and essential trainings. we will see more and more in upcoming videos. For any queries drop a mail at contact.missgoo@gmail.com Share your thoughts about this video in the comment section and if you have any doubts post it in comment section. BluePrism Playlist:- https://youtube.com/playlist?list=PLWMB5IYAuU6fwXy7lFh627J9buCMnPgRU Thank You...! →→→→→Visit Our Channel For More Videos←←←←← 🏹LIKE 🏹SHARE 🏹SUBSCRIBE Where There is a Will There is a Way 💘 //Chapters and time splits 00:00:00-00:01:46 Leveraging machine learning 00:01:47-00:02:52 What you should know 00:02:53-00:03:36 What tools you need 00:03:37-00:07:37 What is machine learning? 00:07:38-00:12:39 What kind of problems can this help you solve? 00:12:40-00:18:28 Why Python? 00:18:29-00:22:17 Machine learning vs Deep learning vs Artificial intelligence 00:22:18-00:25:16 Demos of machine learning in real life 00:25:17-00:31:20 Common challenges 00:31:21-00:34:49 Why do we need to explore and clean our data? 00:34:50-00:43:35 Exploring continuous features 00:43:36-00:51:10 Plotting continuous features 00:51:11-00:56:54 Continuous data cleaning 00:56:55-01:02:58 Exploring categorical features 01:02:59-01:09:18 Plotting categorical features 01:09:19-01:13:51 Categorical data cleaning 01:13:52-01:19:45 Why do we split up our data? 01:19:46-01:24:52 Split data for train/validation/test set 01:24:53-01:30:55 What is cross-validation? 01:30:56-01:35:28 Establish an evaluation framework 01:35:29-01:40:28 Bias/Variance tradeoff 01:40:29-01:42:54 What is underfitting? 01:42:55-01:45:41 What is overfitting? 01:45:42-01:48:57 Finding the optimal tradeoff 01:48:58-01:55:19 Hyperparameter tuning 01:55:20-01:57:50 Regularization 01:57:51-01:59:38 Overview of the process 01:59:39-02:04:42 Clean continuous features 02:04:43-02:09:00 Clean categorical features 02:09:01-02:12:48 Split data into train/validation/test set 02:12:49-02:18:09 Fit a basic model using cross-validation 02:18:10-02:24:43 Tune hyperparameters 02:24:44-02:31:26 Evaluate results on validation set 02:31:27-02:35:56 Final model selection and evaluation on test set 02:35:57-02:37:23 Next steps