Personalized Cancer Diagnosis using Machine Learning
Personalized Cancer Diagnosis using Machine Learning
Srikanth Varma,
Lead DSML Instructor at Scaler
About this Free Personalized Cancer Diagnosis using Machine Learning Course
Our comprehensive course on Personalized Cancer Diagnosis provides an immersive journey into this vital field, offering learners a structured path to understanding and mastering the intricacies of cancer diagnosis customization. Whether you're a seasoned professional seeking to deepen your expertise or a novice eager to explore the frontier of healthcare innovation, this course is designed to cater to learners at every level.
5
Audio: English
Subtitles: English
Duration
3h 49m (1 Modules)Course Level
BeginnerCertificate
IncludedWhat you’ll learn
The skills that you would learn after taking up this Personalized Cancer Diagnosis using Machine Learning online course are:
- Business objectives and constraints related to real-world problems.
- Formulation of machine learning problems, including defining data, mapping real-world problems to ML problems, and constructing train, cross-validation, and test datasets.
- Exploratory data analysis techniques, such as reading and preprocessing data, analyzing the distribution of class labels, and evaluating a "random" model.
- Univariate analysis of different types of features, including gene features, variation features, and text features.
- Data preparation for machine learning models.
- Implementation and evaluation of various machine learning algorithms, including Naive Bayes, K-Nearest Neighbors etc.
Course Content
1 Modules | 21 Lessons | 3h 49m
Certificate for Free Personalized Cancer Diagnosis using Machine Learning
After successfully completing the course, you’ll be awarded with Certificate Of Excellence from Scaler
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Instructor of this course
Lead DSML Instructor at Scaler
2000+ Students on Scaler Platform
600+ Hours of Lectures Delivered
5 Star Instructor on Scaler
9 Courses
- Co-Founder & Principal Instructor, Applied AI & AppliedRoots
- Senior ML Scientist @ Amazon, Palo Alto and Bangalore
- Co-Founder, Matherix Labs
- Research Engineer, Yahoo! Labs
- Masters from IISc Bangalore, Gate 2007(AIR 2)
- 13 years of experience in AI and Machine Learning
Key Features of this Personalized Cancer Diagnosis Course
Embark on a transformative journey into the realm of personalized cancer diagnosis, equipped with the knowledge and skills to make a meaningful impact in the fight against cancer. By enrolling, you will:
- Gain insight into the significance of personalized cancer diagnosis and the hurdles in its execution.
- Navigate through a well-defined course structure covering data acquisition, preprocessing, model deployment, and beyond.
- Engage with real-world datasets and scenarios, bridging theory and practice in cancer diagnosis.
- Participate in hands-on activities and exercises to reinforce learning in data preprocessing and analysis.
- Learn from industry experts, gaining practical insights and best practices from their experiences.
Pre-requisites for Personalized Cancer Diagnosis Course
Prior to embarking on this course, familiarity with the following concepts is recommended:
- Basic understanding of machine learning fundamentals and concepts.
- Familiarity with Python programming language and its libraries, particularly for data manipulation and analysis (e.g., NumPy, pandas).
- Knowledge of data preprocessing techniques, such as handling missing values and feature scaling.
- Understanding of classification algorithms and their applications.
- Awareness of exploratory data analysis (EDA) methods for gaining insights from datasets.
- Some exposure to healthcare or biomedical data analysis is beneficial but not required.
Who should learn this Personalized Cancer Diagnosis Course for Beginners?
This course is ideal for beginners who are:
- Interested in understanding the significance of personalized cancer diagnosis.
- Seeking to grasp the fundamentals of machine learning in healthcare.
- Looking to explore practical applications of data science in oncology.
- Eager to contribute to advancements in personalized medicine.
- Committed to making a positive impact in the fight against cancer.