Learn To Predict Breast Cancer Using Machine Learning

Learn To Predict Breast Cancer Using Machine Learning, available at Free, has an average rating of 4.46, with 16 lectures, based on 169 reviews, and has 5287 subscribers.

You will learn about Use Python for Machine Learning to classify breast cancer as either Malignant or Benign. Implement Machine Learning Algorithms Exploratory Data Analysis Learn to use Pandas for Data Analysis Learn to use NumPy for Numerical Data Learn to use Matplotlib for Python Plotting Use Plotly for interactive dynamic visualizations Learn to use Seaborn for Python Graphical Representation Logistic Regression Random Forest and Decision Trees This course is ideal for individuals who are Interested in the field of Machine Learning? Then this course is for you! or This course had been designed to be your guide to learning how to use the power of Python to analyze data, create some good beautiful visualization for better understanding and use some powerful machine learning algorithms. or This course will also give you a hands-on walk through step-by-step into the world of machine learning and how amazing it is to make prediction on some serious real-life problems. This course will not only help you develop new skills and improve your understanding but also grow confidence in you. It is particularly useful for Interested in the field of Machine Learning? Then this course is for you! or This course had been designed to be your guide to learning how to use the power of Python to analyze data, create some good beautiful visualization for better understanding and use some powerful machine learning algorithms. or This course will also give you a hands-on walk through step-by-step into the world of machine learning and how amazing it is to make prediction on some serious real-life problems. This course will not only help you develop new skills and improve your understanding but also grow confidence in you.

Enroll now: Learn To Predict Breast Cancer Using Machine Learning

Summary

Title: Learn To Predict Breast Cancer Using Machine Learning

Price: Free

Average Rating: 4.46

Number of Lectures: 16

Number of Published Lectures: 16

Number of Curriculum Items: 16

Number of Published Curriculum Objects: 16

Original Price: Free

Quality Status: approved

Status: Live

What You Will Learn

Use Python for Machine Learning to classify breast cancer as either Malignant or Benign.

Implement Machine Learning Algorithms

Exploratory Data Analysis

Learn to use Pandas for Data Analysis

Learn to use NumPy for Numerical Data

Learn to use Matplotlib for Python Plotting

Use Plotly for interactive dynamic visualizations

Learn to use Seaborn for Python Graphical Representation

Logistic Regression

Random Forest and Decision Trees

Who Should Attend

Interested in the field of Machine Learning? Then this course is for you!

This course had been designed to be your guide to learning how to use the power of Python to analyze data, create some good beautiful visualization for better understanding and use some powerful machine learning algorithms.

This course will also give you a hands-on walk through step-by-step into the world of machine learning and how amazing it is to make prediction on some serious real-life problems. This course will not only help you develop new skills and improve your understanding but also grow confidence in you.

Target Audiences

Interested in the field of Machine Learning? Then this course is for you!

This course had been designed to be your guide to learning how to use the power of Python to analyze data, create some good beautiful visualization for better understanding and use some powerful machine learning algorithms.

This course will also give you a hands-on walk through step-by-step into the world of machine learning and how amazing it is to make prediction on some serious real-life problems. This course will not only help you develop new skills and improve your understanding but also grow confidence in you.

Here you will learn to build three models that are Logistic regression model, the Decision Tree model, and Random Forest Classifier model using Scikit-learn to classify breast cancer as either Malignant or Benign.

We will use the Breast Cancer Wisconsin (Diagnostic) Data Set from Kaggle.

Prerequisite

You should be familiar with the Python Programming language and you should have a theoretical understanding of the three algorithms that is Logistic regression model, Decision Tree model, and Random Forest Classifier model.

Learn Step-By-Step

In this course you will be taught through these steps:

Section 1: Loading Dataset

Introduction and Import Libraries

Download Dataset directly from Kaggle

2nd Way To Load Data To Colab

Section 2: EDA – Exploratory Data Analysis

Checking The Total Number Of Rows And Columns

Checking The Columns And Their Corresponding Data Types (Along With Finding Whether They Contain Null Values Or Not)

2nd Way To Check For Null Values

Dropping The Column With All Missing Values

Checking Datatypes

Section 3: Visualization

Display A Count Of Malignant (M) Or Benign (B) Cells

Visualizing The Counts Of Both Cells

Perform LabelEncoding – Encode The ‘diagnosis’ Column Or Categorical Data Values

Pair Plot – Plot Pairwise Relationships In A Dataset

Get The Correlation Of The Columns -> How One Column Can Influence The Other Visualizing The Correlation

Section 4: Dataset Manipulation on ML Algorithms

Split the data into Independent and Dependent sets to perform Feature Scaling

Scaling The Dataset – Feature Scaling

Section 5: Create Function For Three Different Models

Building Logistic Regression Classifier

Building Decision Tree Classifier

Building Random Forest Classifier

Section 6: Evaluate the performance of the model

Printing Accuracy Of Each Model On The Training Dataset

Model Accuracy On Confusion Matrix

2nd Way To Get Metrics

Prediction

Conclusion

By the end of this project, you will be able to build three classifiers to classify cancerous and noncancerous patients. You will also be able to set up and work with the Google colab environment. Additionally, you will also be able to clean and prepare data for analysis.

Course Curriculum

Chapter 1: Introduction – Loading Dataset

Lecture 1: Setting up Colab Environment

Lecture 2: Importing and downloading python libraries

Lecture 3: Downloading Dataset from Kaggle [Part 1]

Lecture 4: Downloading Dataset from Kaggle [Part 2]

Chapter 2: EDA – Exploratory Data Analysis

Lecture 1: Data Analysis [Part 1] – Summary Statistics

Lecture 2: Data Analysis [Part 2] – Dropping The Column With All Missing Values

Chapter 3: Data Visualization

Lecture 1: Display A Count Of Malignant (M) Or Benign (B) Cells

Lecture 2: Pair Plot – Plot Pairwise Relationships In A Dataset

Lecture 3: HeatMap – Get The Correlation Of The Columns

Chapter 4: Dataset Manipulation on ML Algorithms

Lecture 1: Scaling The Dataset – Feature Scaling

Chapter 5: Create Function For Three Different Models

Lecture 1: Building Logistic Regression Classifier

Lecture 2: Building Decision Tree Classifier

Lecture 3: Building Random Forest Classifier

Chapter 6: Evaluate the performance of the model

Lecture 1: Evaluate the performance of the model

Lecture 2: Model Accuracy On Confusion Matrix

Lecture 3: Model Prediction Vs. Actual Prediction

Instructors

Megha Ghosh
WordPress Engineer at Aeron7 Inc

Rating Distribution

1 stars: 2 votes

2 stars: 9 votes

3 stars: 31 votes

4 stars: 63 votes

5 stars: 64 votes

Frequently Asked Questions

How long do I have access to the course materials?

You can view and review the lecture materials indefinitely, like an on-demand channel.

Can I take my courses with me wherever I go?

Definitely! If you have an internet connection, courses on Udemy are available on any device at any time. If you don’t have an internet connection, some instructors also let their students download course lectures. That’s up to the instructor though, so make sure you get on their good side!