Logistic Regression SKLearn In Python | Tutorial for Beginners ☞ http://bit.ly/36ZRuIr #python #SKLearn
seen from China
seen from China

seen from United States
seen from Taiwan

seen from United States

seen from United States

seen from United States
seen from China

seen from Canada
seen from United States
seen from China
seen from United States

seen from United States

seen from Germany
seen from France

seen from Germany

seen from Canada

seen from United States

seen from Germany
seen from Kazakhstan
Logistic Regression SKLearn In Python | Tutorial for Beginners ☞ http://bit.ly/36ZRuIr #python #SKLearn

Anya is live and ready to show you everything. Watch her strip, dance, and perform exclusive shows just for you. Interact in real-time and make your fantasies come true.
Free to watch • No registration required • HD streaming
My 1st Machine Learning program
Can you spot the error?
scaledData = StandardScaler().fit_transform(inputData.flatten().reshape((-1,1))) scaledData = MinMaxScaler().fit_transform(inputData.flatten().reshape((-1,1)) scaledData = reshape(scaledData,(targets,length))
Sklearn Regression: The Ultimate Power Guide to Mastering Predictive Modeling
Sklearn Regression: The Ultimate Power Guide to Mastering Predictive Modeling” explores how regression techniques in Scikit-learn empower data scientists to build accurate predictive models. It covers key algorithms like linear, ridge, and lasso regression, along with practical tips for implementation and evaluation. A must-read for anyone looking to strengthen their machine learning Read More..
Article
Just published an article on Using Bagging And XGBoost To Train Large Datasets. The link to the article is shown below.
Contribute to subair99/ML_Zoomcamp_2024_Modules development by creating an account on GitHub.

Anya is live and ready to show you everything. Watch her strip, dance, and perform exclusive shows just for you. Interact in real-time and make your fantasies come true.
Free to watch • No registration required • HD streaming
ML Zoomcamp Project Capstone 2 – 2: Exploratory Data Analysis
The dataset used in this project is the Brain Tumor MRI Dataset https://www.kaggle.com/datasets/masoudnickparvar/brain-tumor-mri-dataset from kaggle.
It consists of 7,023 magnetic resonance imaging scans, annotated in a folder structure of 5,712 test and 1,311 train. The dataset consists of images of no tumour and brain tumor types: pituitary, meningioma, and glioma, as shown in the attached diagram.
ML Zoomcamp Project Capstone 2 – 1: Problem Statement
A brain tumor is a mass of abnormal cells that grows in the brain, it can be benign (non-cancerous) or malignant (cancerous). Its symptoms are:
Headaches, which can be severe, persistent, or come and go
Seizures, which can be mild or severe
Weakness or paralysis in part of the body
Loss of balance
Changes in mood or personality
Changes in vision, hearing, smell, or taste
Nausea and vomiting
Difficulty speaking
Difficulty swallowing
The growth of brain tumors can cause the pressure inside the skull to increase leading to brain damage, and loss of life if not discovered early and properly treated.
This project is aimed at developing a robust brain tumor detection model using Convolutional Neural Networks (CNNs) to automate the analysis of magnetic resonance imaging (MRI) scan by accurately identifying and classifying the tumors at an early stage which can reduce the load on doctors, help in selecting the most convenient treatment method and hence increase the rate of survival.
ML Zoomcamp
Just completed the tenth week of Machine Learning Zoomcamp.
The lessons covered include:
TensorFlow Serving
Creating a pre-processing service
Running everything locally with Docker-compose
Introduction to Kubernetes
Deploying a simple service to Kubernetes
Deploying TensorFlow models to Kubernetes
Deploying to EKS
The link to the course is below: https://github.com/DataTalksClub/machine-learning-zoomcamp