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
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.
Who this course is for:
- 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.
Can I download Learn To Predict Breast Cancer Using Machine Learning course?
You can download videos for offline viewing in the Android/iOS app. When course instructors enable the downloading feature for lectures of the course, then it can be downloaded for offline viewing on a desktop.Can I get a certificate after completing the course?
Yes, upon successful completion of the course, learners will get the course e-Certification from the course provider. The Learn To Predict Breast Cancer Using Machine Learning course certification is a proof that you completed and passed the course. You can download it, attach it to your resume, share it through social media.Are there any other coupons available for this course?
You can check out for more Udemy coupons @ www.coursecouponclub.com
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