What you'll learn
Students will learn how to use the Python programming language for data analysis and manipulation.
Students will learn how to create numpy arrays to better understand and communicate their data.
Machine learning algorithm: Students will learn how to use support vector machine learning model in this course.
Diabetes prediction model: Students will learn how to build model to predict the onset of diabetes using svm.
Model evaluation: Students will learn how to evaluate the performance of the models using test data accuracy score and training data accuracy score.
Data preparation: Students will learn how to prepare data for analysis, including fitting, transforming and standardizing data.
Early detection and prevention of diabetes: Students will learn about the early detection and prevention of diabetes using data science
Requirements
Basic Python knowledge
Interest to learn data science
Laptop or desktop computer with internet connection
Description
Welcome to the course on "Diabetes Prediction Project with Python" - In this course You will learn to build and evaluate a machine learning model using python.Introduction:In this course, you will learn how to use the Support Vector Machine (SVM) algorithm for diabetes prediction. You will work with real-world diabetes data, perform train and test split, and build a predictive model to identify new cases of diabetes.Data Collection and Preparation:You will learn how to download and prepare real-world diabetes data, including calculating mean values and counting the number of people affected by diabetes and those who are not.Train and Test Split:You will learn how to perform train and test split, which is a critical step in evaluating the performance of predictive models.Support Vector Machine (SVM) Algorithm:This section will cover the basics of SVM, including its mathematical foundations and how it can be used for diabetes prediction.Building the Predictive Model:You will use the SVM algorithm to build a predictive model that can be used to identify new cases of diabetes. You will also learn how to evaluate the accuracy of the models and understand the factors that contribute to diabetes risk.Evaluating the Model:You will learn how to evaluate the performance of their models, including accuracy, precision score. Conclusion:By the end of the course, you will have a complete understanding of how to use SVM for diabetes prediction and the skills necessary to build a predictive system that can be used to identify new cases of diabetes. This course covers all the necessary skills and concepts for students to succeed in the field of data science and machine learning, including data collection and preparation, machine learning algorithms, model building and evaluation, and more. With its practical, hands-on approach, this course is an excellent resource for anyone looking to advance their skills in data science and machine learning and apply them to real-world problems.Thank you for your interest in this course...I will see you in the course...
Overview
Section 1: Introduction
Lecture 1 Introduction
Section 2: Learn the machine learning algorithm and its basics
Lecture 2 Machine learning algorithm
Section 3: Project Steps
Lecture 3 Step by step process
Section 4: Dataset downloading and starting google colab
Lecture 4 Dataset downloading and starting google colab
Section 5: Importing required libraries
Lecture 5 Importing required libraries
Section 6: Import data set and get number of rows and columns in google colab
Lecture 6 Import data set and get number of rows and columns in google colab
Section 7: Analyse the dataset with mean values
Lecture 7 Analyse the dataset with mean values
Section 8: Splitting the dataset by data and labels
Lecture 8 Splitting the dataset by data and labels
Section 9: Standardize the dataset
Lecture 9 Standardize the dataset
Section 10: Train and test split the dataset
Lecture 10 Train and test split the dataset
Section 11: Training the model
Lecture 11 Training the model
Section 12: Create a diabetic predictive system
Lecture 12 Testing the predictive system with random data
Lecture 13 Create a diabetic predictive system
Section 13: Step - by - step explanation
Lecture 14 Step 1 : Importing libraries
Lecture 15 Step 2 : Import dataset, Analyse and Splitting the data, Creating SVM
Lecture 16 Step 3 : Training and testing the model, Testing with random data
Section 14: Download the source code
Lecture 17 Download the source code in .py file and .ipynb file format
Healthcare professionals: Doctors, nurses, and other healthcare professionals who want to learn how to use data science techniques for early detection and prevention of diabetes.,Data scientists: Data scientists and analysts who want to develop their skills in machine learning and Python programming.,Python developers: Python developers who want to learn how to use their skills for diabetes prediction and data analysis in the field of healthcare.,Individuals interested in diabetes: People who are interested in learning more about diabetes and how data science can be used for its prevention and management.,Students and recent graduates: Students and recent graduates in fields such as computer science, data science, and healthcare who want to gain hands-on experience in the application of data science to healthcare.,Anyone interested in personal and professional growth: This course is suitable for anyone who wants to learn about the data science approach to diabetes prediction and expand their knowledge in this area.


Homepage

https://www.udemy.com/course/diabetes-prediction-using-machine-learning-data-science-project/