Logistic Regression for Classification | Working with a real-world dataset from Kaggle
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Published 2021-06-26
In this lesson we will learn about using Logistic Regression for Classification. Logistic Regression is a commonly used technique for solving binary classification problems. You can experiment with the notebook used in the above video here 👉 jovian.ai/aakashns/python-sklearn-logistic-regress…
đź”— Check out this playlist for the complete lecture series on Gradient Boosting Machines:    • Machine Learning with Python: Zero to... Â
🎯 Topics Covered
• Downloading a real-world dataset from Kaggle
• Splitting a dataset into training, validation & test sets
• Imputing and scaling numeric features
• Encoding categorical columns as one-hot vectors
• Training a logistic regression model using Scikit-learn
• Evaluating a model using a validation set and test set
❓ Ask Questions here: jovian.ai/forum/t/lesson-2-logistic-regression-for…
classification/17915
⌚ Time Stamps:
00:00 Introduction
05:16 Problem Statement
25:43 Downloading a real-world dataset from Kaggle
35:35 Exploring data analysis and visualization
47:06 Splitting a dataset into training, validation & test sets
01:03:04 Filling/Imputing missing values in numeric columns
01:21:55 Scaling numeric features to a(0,1) range
01:28:10 Encoding categorical columns as one-hot vectors
01:39:02 Training a logistic regression model using Scikit-learn
01:53:41 Evaluating a model using a validation set and test set
02:19:38 Saving a model to disk and loading it back
02:36:28 Summary and Conclusion
⚡ Free Certification Course
"Machine Learning with Python: Zero to GBMs(Gradient Boosting Machine)" is a practical and beginner-friendly introduction to supervised machine learning, decision trees, and gradient boosting using Python. You will solve 3 coding assignments & build a course project where you'll train ML models using a large real-world dataset. Enroll now: zerotogbms.com/
🔗 Visit the logistic regression lecture page here: jovian.ai/learn/machine-learning-with-python-zero-…
🎤 About the speaker
Aakash N S is the co-founder and CEO of Jovian - a community learning platform for data science & ML. Previously, Aakash has worked as a software engineer (APIs & Data Platforms) at Twitter in Ireland & San Francisco and graduated from the Indian Institute of Technology, Bombay. He’s also an avid blogger, open-source contributor, and online educator.
#GBM #MachineLearning #Python #Certification #Course #Jovian
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All Comments (21)
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That was intense!!! This is probably the first time I have watched a tutorial this long without any break You are Awesome sir
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Thanks a lot Aakash for the fabulous explanations and infectious passion to empower others. These tutorials are simply unmatched! Bravo!
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This video is still one of the best. A literal game changer!
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Nicely explained Akash and Jovian Team..this was probably the most thorough and clearly explained tutorial I came across
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Great video! I learned a lot! Thank you!
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great explanation with reasonable depth for this topic, such a great video...
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Really, a lecture full of knowledge
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Thank you, this was very beginner friendly and it helped me understand a lot of practical topics.
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Salute Boss. This is wholesome đź’ťđź’ť
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Great content Aakash sir , that too free...really amazed and impressed by jovian !
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Thank you for such a detailed lecture. Very very helpful. Would love to know about more.
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Thank you very much.🙏
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excellent brother!
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Very good tutorial.elaborate and detailed .thanks
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Nice Video....Really appreciated. Can we also include the topic of setting up data pre processing pipelines in future sessions.
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Great content
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Nice lecture
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hey, also isn't it a common practice to scale the test data that is transform the test data or validation data by fitting it only on training datasets?
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Hello. I have a question. Should we scale the features after the imputation or before because here you imputed the raw_df dataframe which is not imputed? Thanks
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finished watching
