Artificial Intelligence(AI) & Machine Learning(ML)

Artificial Intelligence (AI) and Machine Learning (ML) are transforming bioinformatics by enabling the analysis of complex biological data at scale. From genomics to drug discovery, AI/ML models help uncover hidden patterns, predict outcomes, and automate research workflows.
This course is ideal for life science students and researchers who want to gain practical knowledge of AI/ML techniques applied to biological data, with a strong focus on real-world bioinformatics use cases.

Key learning outcomes include:
- Fundamentals of AI and Machine Learning
- Data preprocessing and feature engineering for biological datasets
- Supervised and unsupervised learning algorithms
- Applying ML to gene expression, protein classification, and sequence data
- Model evaluation, validation, and deployment in bioinformatics workflows
Upon completing the AI and Machine Learning training, learners will gain a solid understanding of core AI concepts, including supervised, unsupervised, and reinforcement learning techniques. They will be able to apply statistical and mathematical principles to real-world data, preprocess and visualize datasets, and build predictive models using Python and popular libraries like Scikit-learn, TensorFlow, and PyTorch.

👉 Enroll now and take the first step toward a future in data-driven biology and beyond.

## 10% Discount if you register before 15th October, 2025. Hurry up!!

Course Information

Course

Artificial Intelligence(AI) & Machine Learning

Duration

Online - 15 Days Training [ 2 Hours Daily [ Monday To Friday ] ]

Slots

Our working Time is 9:00 AM to 6:00 PM Indian Time Available slots - 9:00 AM to 11:00 AM / 11:00 AM to 1:00 PM / 2:00 PM to 4:00 PM / 4:00 PM to 6:00 PM
For training slots after 6 PM or before 9 am as well as weekends training kindly mention during registration accordingly it will be scheduled.

Mode

👉 For online training candidate have to install ZOOM (with remote control on candidate system which makes 100% interactive)
👉 Run time video recording candidate can make as well as pdf manual will be provided for future reference.
👉 All our training is 100% practical and 100% industrial and 100% interactive which provides same as offline learning.
👉 For doubt clear there will be extra support will be provided based on the requirement.
👉 Certificate will be provided
Training Fees

Support

Candidate can also discuss and possible implementation with their own genomic data.

Module - 1

Basics & Advanced python programming for AI

Topics

    📘 Linux Basics
    - Linux installation and configuration
    - Essential Linux commands for bioinformatics
    - Installing necessary bioinformatics tools and software

    📘 Introduction to Python
    - Why Python? What makes Python special?
    - Setting up the Python environment
    - Writing your first Python program

    📘 Python Data Types and Variables
    - Understanding basic data types
    - Variable declaration and usage

    📘 Python Collections
    - Lists: ordered, mutable sequences
    - Tuples: ordered, immutable sequences
    - Dictionaries: key-value pairs for data mapping

    📘 Control Flow Statements
    - Conditional statements: if, elif, else
    - Looping constructs: for loops, while loops

    📘 Functions in Python
    - Built-in functions
    - Defining and using user-defined functions

    📘 File Handling
    - Reading from files
    - Writing to files
    - Algorithm development with file data

    📘 Module Handling
    - Using built-in modules
    - Creating and importing user-defined modules

    📘 Object-Oriented Programming in Python
    - Understanding classes and objects
    - Concepts of inheritance, encapsulation, and polymorphism

Module - 2

Artificial Intelligence(AI) & Machine Learning

Topics

    📘 Introduction to Artificial Intelligence
    - Overview of AI concepts and goals
    - Different approaches to achieving AI

    📘 Data Analysis for AI and ML
    - Importance of data in AI/ML
    - Preparing data for modeling

    📘 Introduction to Machine Learning
    - Definition and scope of ML
    - Types of machine learning:
      - Supervised learning
      - Unsupervised learning

    📘 Essential AI and ML Packages
    - Installing and using NumPy, Pandas, SciPy
    - Visualization tools: matplotlib, scikit-image, PIL, OpenCV
    - Machine learning frameworks: Scikit-learn, TensorFlow, Keras, PyTorch

    📘 Deep Learning Overview
    - Introduction to neural networks
    - Limitations of deep learning
    - Types of deep learning architectures:
      - Feedforward Neural Networks (FFN)
      - Convolutional Neural Networks (CNN)
      - Recurrent Neural Networks (RNN)

    📘 TensorFlow and Keras
    - Basics of TensorFlow framework
    - Building models with Keras API

    📘 Model Development and Deployment
    - Designing and training ML models
    - Evaluating and optimizing model performance

    📘 Real-World AI & ML Applications in Bioinformatics(Any One)
    - AMP (Antimicrobial Peptides) sequence classification
    - Pneumonia detection from chest X-ray images
    - Feature extraction from TCGA cancer database images
    - Gene expression pattern recognition
    - Variant calling and prioritization
    - Biomarker discovery

Preparation

- Python installation: https://www.python.org/downloads/
- Visual Studio code: https://code.visualstudio.com/

Instructor

Industry Experienced

Target Audiance

This course is designed for graduate students, postdoctoral researchers, and professionals working in the fields of conservation biology, evolutionary genomics, and population genetics or any life sciences who are interested in applying genomic tools to real-world conservation challenges.

Contact

Please write us at info@arraygen.com or call or whatsapp us on mobile +91-9673625446 if you need any clarification or for any custom training based on candidate reference paper or candidate own content/tools.