Machine Learning Tutorial 2 - Logistic Regression Python Part 1 | Machine Learning Basics
In this video, we will learn about logistic regression with python in machine learning. Logistic Regression is a Machine Learning algorithm which is used for classification problems, it is a predictive analysis algorithm, and based on the concept of probability. Logistic regression is basically a supervised classification algorithm. In a classification problem, the target variable(or output),y, can take only discrete values for a given set of features(or inputs), X. Logistic regression becomes a classification technique only when a decision threshold is brought into the picture. The setting of the threshold value is a very important aspect of Logistic regression and is dependent on the classification problem itself.
#LogisticRegression #MachineLearning #TitanicDataset
🔊 Watch till last for a detailed description
01:59 What is logistic regression?
06:05 Decision boundary
08:22 Building the model
13:12 Data understanding
38:00 analyzing embarked
50:19 Convert categorical data into numerical data
58:08 Build a logistic regression model
👇👇👇👇👇👇👇👇👇👇👇👇👇👇
✍️🏆🏅🎁🎊🎉✌️👌⭐⭐⭐⭐⭐
ENROLL in My Highest Rated Udemy Courses
to 🔑 Unlock Data Science Interviews 🔎 and Tests
📚 📗 NLP: Natural Language Processing ML Model Deployment at AWS
Build & Deploy ML NLP Models with Real-world use Cases.
Multi-Label & Multi-Class Text Classification using BERT.
Course Link: https://bit.ly/bert_nlp
📊 📈 Data Visualization in Python Masterclass: Beginners to Pro
Visualization in matplotlib, Seaborn, Plotly & Cufflinks,
EDA on Boston Housing, Titanic, IPL, FIFA, Covid-19 Data.
Course Link: https://bit.ly/udemy95off_kgptalkie
📘 📙 Natural Language Processing (NLP) in Python for Beginners
NLP: Complete Text Processing with Spacy, NLTK, Scikit-Learn,
Deep Learning, word2vec, GloVe, BERT, RoBERTa, DistilBERT
Course Link: https://bit.ly/intro_nlp .
📈 📘 2021 Python for Linear Regression in Machine Learning
Linear & Non-Linear Regression, Lasso & Ridge Regression, SHAP, LIME, Yellowbrick, Feature Selection & Outliers Removal. You will learn how to build a Linear Regression model from scratch.
Course Link: https://bit.ly/regression-python
📙📊 2021 R 4.0 Programming for Data Science || Beginners to Pro
Learn Latest R 4.x Programming. You Will Learn List, DataFrame, Vectors, Matrix, DateTime, DataFrames in R, GGPlot2, Tidyverse, Machine Learning, Deep Learning, NLP, and much more.
Course Link: http://bit.ly/r4-ml
---------------------------------------------------------------
💯 Read Full Blog with Code
https://kgptalkie.com/logistic-regres...
💬 Leave your comments and doubts in the comment section
📌 Save this channel and video for watch later
👍 Like this video to show your support and love ❤️
~~~~~~~~
🆓 Watch My Top Free Data Science Videos
👉🏻 Python for Data Scientist
https://bit.ly/3dETtFb
👉🏻 Machine Learning for Beginners
https://bit.ly/2WOVh7N
👉🏻 Feature Selection in Machine Learning
https://bit.ly/2YW6ZQH
👉🏻 Text Preprocessing and Mining for NLP
https://bit.ly/31sYMUN
👉🏻 Natural Language Processing (NLP)
Tutorials https://bit.ly/3dF1cTL
👉🏻 Deep Learning with TensorFlow 2.0
and Keras https://bit.ly/3dFl09G
👉🏻 COVID 19 Data Analysis and Visualization
Masterclass https://bit.ly/31vNC1U
👉🏻 Machine Learning Model Deployment Using
Flask at AWS https://bit.ly/3b1svaD
👉🏻 Make Your Own Automated Email Marketing
Software in Python https://bit.ly/2QqLaDy
***********
🤝 BE MY FRIEND
🌍 Check Out ML Blogs: https://kgptalkie.com
🐦Add me on Twitter: / laxmimerit
📄 Follow me on GitHub: https://github.com/laxmimerit
📕 Add me on Facebook: / kgptalkie
💼 Add me on LinkedIn: / laxmimerit
👉🏻 Complete Udemy Courses: https://bit.ly/32taBK2
⚡ Check out my Recent Videos: https://bit.ly/3ldnbWm
🔔 Subscribe me for Free Videos: https://bit.ly/34wN6T6
🤑 Get in touch for Promotion: [email protected]
Watch video Machine Learning Tutorial 2 - Logistic Regression Python Part 1 | Machine Learning Basics online without registration, duration hours minute second in high quality. This video was added by user KGP Talkie 24 July 2019, don't forget to share it with your friends and acquaintances, it has been viewed on our site 5,460 once and liked it 121 people.