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Machine Learning Tutorial for Beginners | Applied Machine Learning Algorithms
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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.
Share your thoughts about this video in the comment section and if you have any doubts post it in comment section.
Thank You...!
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Where There is a Will There is a Way 💘
//Chapters and time splits
00:00:00-00:02:12 The power of algorithms in machine learning
00:02:13-00:03:35 What you should know
00:03:36-00:04:19 What tools you need
00:04:20-00:06:54 Defining model vs aldorithm
00:06:55-00:10:26 Process overview
00:10:27-00:17:58 Clean continous variables
00:17:59-00:24:46 Clean categorical variables
00:24:47-00:29:04 Split into train, validation and test set
00:29:05-00:32:15 What is logistic regression?
00:32:16-00:35:44 When should you consider using logistic regression?
00:35:45-00:41:21 What are the key hyperparameters to consider?
00:41:22-00:50:41 Fit a basic logistic regression model
00:50:42-00:55:28 What is Support Vector Machine?
00:55:29-00:58:23 When should you consider using SVM?
00:58:23-01:02:58 What are the key hyperparameters to consider?
01:02:59-01:09:43 Fit a basic SVM model
01:09:44-01:13:16 What is a multi-layer perceptron?
01:13:17-01:16:24 When should you consider using a multi-layer perceptron?
01:16:25-01:21:55 What are the key hyperparameters to consider?
01:21:56-01:30:00 Fit a basic multi-layer perceptron model
01:30:01-01:34:13 What is Random Forest?
01:34:14-01:36:20 When should you consider using Random Forest?
01:36:21-01:39:01 What are the key hyperparameters to consider?
01:39:02-01:43:26 Fit a basic Random Forest model
01:43:27-01:48:49 What is boosting?
01:48:50-01:51:35 When should you consider using boosting?
01:51:36-01:55:28 What are the key hyperparameters to consider?
01:55:29-02:00:51 Fit a basic boosting model
02:00:52-02:05:00 Why do you need to consider so many different model?
02:05:01-02:09:06 Conceptual comparison of algorithms
02:09:07-02:20:40 Final model selection and evaluation
02:20:41-02:22:18 Next steps
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.
Share your thoughts about this video in the comment section and if you have any doubts post it in comment section.
Thank You...!
→→→→→Visit Our Channel For More Videos←←←←←
🏹LIKE
🏹SHARE
🏹SUBSCRIBE
Where There is a Will There is a Way 💘
//Chapters and time splits
00:00:00-00:02:12 The power of algorithms in machine learning
00:02:13-00:03:35 What you should know
00:03:36-00:04:19 What tools you need
00:04:20-00:06:54 Defining model vs aldorithm
00:06:55-00:10:26 Process overview
00:10:27-00:17:58 Clean continous variables
00:17:59-00:24:46 Clean categorical variables
00:24:47-00:29:04 Split into train, validation and test set
00:29:05-00:32:15 What is logistic regression?
00:32:16-00:35:44 When should you consider using logistic regression?
00:35:45-00:41:21 What are the key hyperparameters to consider?
00:41:22-00:50:41 Fit a basic logistic regression model
00:50:42-00:55:28 What is Support Vector Machine?
00:55:29-00:58:23 When should you consider using SVM?
00:58:23-01:02:58 What are the key hyperparameters to consider?
01:02:59-01:09:43 Fit a basic SVM model
01:09:44-01:13:16 What is a multi-layer perceptron?
01:13:17-01:16:24 When should you consider using a multi-layer perceptron?
01:16:25-01:21:55 What are the key hyperparameters to consider?
01:21:56-01:30:00 Fit a basic multi-layer perceptron model
01:30:01-01:34:13 What is Random Forest?
01:34:14-01:36:20 When should you consider using Random Forest?
01:36:21-01:39:01 What are the key hyperparameters to consider?
01:39:02-01:43:26 Fit a basic Random Forest model
01:43:27-01:48:49 What is boosting?
01:48:50-01:51:35 When should you consider using boosting?
01:51:36-01:55:28 What are the key hyperparameters to consider?
01:55:29-02:00:51 Fit a basic boosting model
02:00:52-02:05:00 Why do you need to consider so many different model?
02:05:01-02:09:06 Conceptual comparison of algorithms
02:09:07-02:20:40 Final model selection and evaluation
02:20:41-02:22:18 Next steps
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