Artificial Intelligence for Biotechnology and Healthcare

Price 3000 | USD $90

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About Course

The field of biological sciences is becoming increasingly information-intensive and data-rich. For example, the growing availability of omics data or clinical measurements from humans promises a better understanding of the important questions in biology. However, the complexity and high-dimensionality of these biological data make it difficult to pull out mechanisms from the data. Machine Learning techniques promise to be useful tools for resolving such questions in biology because they provide a mathematical framework to analyse complex and vast biological data. In turn, the unique computational and mathematical challenges posed by biological data may ultimately advance the field of machine learning as well. This course will cover basics of the Python programming language for biological data wrangling and use of machine learning to that. This course is intended to give learner a conceptual overview of machine learning algorithms and hands on experience of the same.

Course highlights

Syllabus

  1. Installation and running python in Anaconda Spyder, variables and expressions, python functions
  2. Data structures and conditional statement
  3. Loops, classes and Python for biotechnology
  4. Introduction to machine learning for biological data
  5. Gradient and search-based optimisation for machine learning
  1. Handling Data in Pandas library
  2. Basic Pandas Data Cleaning
  3. Exploratory Data Analysis in Pandas
  4. Data Visualization in Python
  5. Use of NumPy and sci-kit learn libraries
  1. Introduction to sci-kit learn workflow
  2. Train/test split, and label encoding, one-hot encoding, parameters of model evaluation
  3. Linear regression and Logistic regression
  4. Decision Trees and Random Forest
  5. KNN and Support Vector Machines
  6. - Project 1: Machine learning model building for protein dataset. Use the given training dataset of proteins to make ML models for classification and then use the models to predict drug targets from the prediction dataset. Make a summary of approach comparing the accuracies of at least two different ML algorithms. Submit the result file in .csv format and the code file.
  1. Clustering Methods (K Means Clustering) and feature extraction
  2. Advanced Clustering Methods Hierarchical Clustering, DBSCAN
  3. Dimensionality Reduction: Principal component analysis
  4. Introduction to Artificial Neural Networks- theory and applications
  5. Introduction to Convolutional neural networks- theory and applications
  6. - Project 2: Feature extraction using unsupervised machine learning. Use the given dataset to identify most important and best possible features for the identification of the said disease. Make a summary of approach listing the important features identified and submit the word document along with the code file.

Features available in our live classes

“who knows, does it live”

Certificate of completion

Life time study material access

Doubt solving at any time

Job opportunities

Instructor

Top instructor

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What students say about us

ReadMyCourse is a one stop destination for understanding courses related to research and new technology With great qualified instructors and 24X7 doubts support, difficult topics are presented in a simplified manner.

Dr. Rimanpreet Kaur | Bioanalyst
Machine learning course

I had a great learning experience with ReadMyCourse. The concepts were clearly explained by the teachers, especially the way the course was simplified and presented, makes it more fun and easy. I got a job offer as Bioanalyst.

Anup Singh | IISC
NGS course

It is quite understandable for me. I have some background in programming language it is a plus point, apart from it also its quite easy to understand with live classes. Course was more practical and with live coding examples.

Sakina Vakhariya | AstraZeneca
Python for Bioinformatics course

Our values lie in our instructors

Anand Kumar
Python for Bioinformatics, Vaccine designing

Anand is the Co-Founder and CTO of ReadMyCourse. He has 5 years of experience in working with computation biology, Machine Learning, and Vaccine designing. He has co-authored various research papers in reputed journals and has advanced the career of thousands of students.

Dr. Dibyabhaba Pradhan
NGS data analysis, Vaccine designing, Bioanalyst

Dr. Dibyabhaba Pradhan is a Post-doctoral Research Scientist. He is a PhD in Bioinformatics and has more than 12 years of Research and teaching experience in High throughput NGS data analysis, Computer-aided vaccine design, Rational Drug Design and Medical Informatics. He co-authored more than 53 research papers in International and National Journals of repute.

Nirupma Singh
Python, R, Data Analysis, Machine Learning

Nirupma has 5 years of experience with R and Python programming languages for handling and analysing biological data and implement machine learning. She has a post-graduate in Microbiology therefore, She can understand and interpret the biological data quite well and could provide valuable insights. She enjoys interactive teaching with the learners and give her best to it.

Sharon Priya Alexander
Drug designing, Bioinformatics tools

Sharon Priya Alexander has Masters Degree in Bioinformatics bagged late Dr. P. Subramanyam IAS Gold Medal for being a University topper. He has co-authored various research papers in reputed journals. Currently Pursuing PhD in drug designing.

FAQs

If you miss the class you can attent in any other session. You can view recorded sessions also.

Yes you can attend demo session for free. Before the final class will be started you can attend demo live classes.

Yes you are eligible for refund in the period of three classes.

You will get the certificate after completion of all the live sessions and study materials.

Artificial Intelligence for Biotechnology and Healthcare