Learn To Predict Breast Cancer Using Machine Learning
In this course, you'll master the creation of three essential models: the logistic regression, Decision Tree, and Random Forest Classifier models, leveraging Scikit-learn to classify breast cancer as either Malignant or Benign. Utilizing the Breast Cancer Wisconsin (Diagnostic) Data Set from Kaggle, you'll delve into the intricacies of data analysis and machine learning.
By the end of this project, you will adeptly construct three classifiers for distinguishing cancerous and non-cancerous patients. Moreover, you will gain proficiency in setting up and operating within the Google lab environment and adeptly cleansing and preparing data for analysis.
What you'll learn:
- Utilize Python for Machine Learning to classify breast cancer into Malignant or Benign categories.
- Implement essential Machine Learning Algorithms.
- Conduct Exploratory Data Analysis.
- Harness the power of Pandas for Data Analysis.
- Leverage NumPy for handling Numerical Data.
- Employ Matplotlib for Python Plotting.
- Utilize Plotly for interactive dynamic visualizations.
- Master Seaborn for Python Graphical Representation.
- Gain insights into Logistic Regression, Random Forest, and Decision Trees.
Requirements:
- Familiarity with the Python Programming language.
- Theoretical understanding of the logistic regression, Decision Tree, and Random Forest Classifier models.
Who should take this course:
- Individuals seeking to harness Python's data analysis and visualization capabilities alongside powerful machine learning algorithms.
- Those eager for a hands-on exploration of machine learning, unraveling real-life challenges through predictive analytics.
- Aspiring learners aiming to cultivate new skills, deepen their understanding, and bolster their confidence in machine learning.
Course Components:
- Learn To Predict Breast Cancer Using Machine Learning Course
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$300.00Price
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