Artificial Intelligence (AI) & Machine Learning Industrial Training Program
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Published date: 2026/06/15
- Location: University Of California, Berkeley, Berkeley, California, United States
Name: Arraygen Technologies
Phone: 967362xxxx
Course Overview
Duration: 15 Days (Online Training)
Schedule: 2 Hours Daily (Monday–Friday)
Mode: Live Online Training via Zoom
Training Schedule (IST)
Available Slots:
9:00 AM – 11:00 AM
11:00 AM – 1:00 PM
2:00 PM – 4:00 PM
4:00 PM – 6:00 PM
Flexible Timing Available
Before 9:00 AM
After 6:00 PM
Weekends
(Special timings must be requested during registration.)
Key Features
✅ 100% Practical Training
✅ 100% Industrial-Oriented Learning
✅ 100% Interactive Sessions
✅ Live Zoom Training with Remote-Control Support
✅ Session Recordings Can Be Saved by Participants
✅ PDF Training Manuals Provided
✅ Additional Doubt-Clearing Support
✅ Industry-Experienced Instructor
✅ Course Completion Certificate
✅ Discussion and Possible Implementation Using Candidate's Own Data
Course Curriculum
Module 1: Advanced Python Programming for AI
Linux Fundamentals
Linux installation and configuration
Essential Linux commands
Software and tool installation
Python Programming
Python introduction and setup
Variables and data types
Lists, Tuples, Dictionaries
Conditional statements
Loops (for, while)
Functions and modules
File handling
Algorithm development
Object-Oriented Programming (OOP)
Classes and objects
Inheritance
Encapsulation
Polymorphism
Module 2: Artificial Intelligence & Machine Learning
Introduction to AI
AI concepts and applications
AI approaches and methodologies
Data Analysis
Data preparation
Data preprocessing techniques
Machine Learning Fundamentals
Supervised Learning
Unsupervised Learning
AI & ML Libraries
NumPy
Pandas
SciPy
Matplotlib
OpenCV
PIL
Scikit-learn
TensorFlow
Keras
PyTorch
Deep Learning
Neural Networks
Feedforward Neural Networks (FFN)
Convolutional Neural Networks (CNN)
Recurrent Neural Networks (RNN)
Deep Learning limitations and applications
TensorFlow & Keras
Model building
Training neural networks
Performance evaluation
Model Development & Deployment
Model training
Validation
Optimization techniques
Deployment concepts
Hands-On Project (Choose Any One)
Participants will complete a real-world AI/ML project:
🔹 Antimicrobial Peptide (AMP) Classification
🔹 Pneumonia Detection Using Chest X-Ray Images
🔹 TCGA Cancer Image Feature Extraction
🔹 Gene Expression Pattern Recognition
🔹 Variant Calling & Prioritization
🔹 Biomarker Discovery
Software Preparation
Participants should install:
Python:
Visual Studio Code:
Who Can Join?
Suitable for:
Graduate Students
PhD Scholars
Postdoctoral Researchers
Life Science Professionals
Bioinformatics Researchers
Conservation Biology Researchers
Evolutionary Genomics Researchers
Population Genetics Researchers
Anyone Interested in AI & Machine Learning Applications in Life Sciences
Learning Outcomes
After completing this program, participants will be able to:
✔ Develop Python-based AI and ML applications
✔ Analyze biological and genomic datasets
✔ Build machine learning and deep learning models
✔ Work with TensorFlow, Keras, Scikit-learn, and PyTorch
✔ Perform real-world bioinformatics AI projects
✔ Apply AI techniques for healthcare and genomics research
✔ Independently design and implement AI/ML workflows
Refund Policy
No refunds after registration and payment confirmation.
Course start dates may be postponed.
Registration can be transferred to an alternative course.
Contact Information
Program Outcome
Become proficient in Artificial Intelligence, Machine Learning, Deep Learning, Python Programming, and AI Applications in Bioinformatics through industry-oriented practical training and project-based learning.
Duration: 15 Days (Online Training)
Schedule: 2 Hours Daily (Monday–Friday)
Mode: Live Online Training via Zoom
Training Schedule (IST)
Available Slots:
9:00 AM – 11:00 AM
11:00 AM – 1:00 PM
2:00 PM – 4:00 PM
4:00 PM – 6:00 PM
Flexible Timing Available
Before 9:00 AM
After 6:00 PM
Weekends
(Special timings must be requested during registration.)
Key Features
✅ 100% Practical Training
✅ 100% Industrial-Oriented Learning
✅ 100% Interactive Sessions
✅ Live Zoom Training with Remote-Control Support
✅ Session Recordings Can Be Saved by Participants
✅ PDF Training Manuals Provided
✅ Additional Doubt-Clearing Support
✅ Industry-Experienced Instructor
✅ Course Completion Certificate
✅ Discussion and Possible Implementation Using Candidate's Own Data
Course Curriculum
Module 1: Advanced Python Programming for AI
Linux Fundamentals
Linux installation and configuration
Essential Linux commands
Software and tool installation
Python Programming
Python introduction and setup
Variables and data types
Lists, Tuples, Dictionaries
Conditional statements
Loops (for, while)
Functions and modules
File handling
Algorithm development
Object-Oriented Programming (OOP)
Classes and objects
Inheritance
Encapsulation
Polymorphism
Module 2: Artificial Intelligence & Machine Learning
Introduction to AI
AI concepts and applications
AI approaches and methodologies
Data Analysis
Data preparation
Data preprocessing techniques
Machine Learning Fundamentals
Supervised Learning
Unsupervised Learning
AI & ML Libraries
NumPy
Pandas
SciPy
Matplotlib
OpenCV
PIL
Scikit-learn
TensorFlow
Keras
PyTorch
Deep Learning
Neural Networks
Feedforward Neural Networks (FFN)
Convolutional Neural Networks (CNN)
Recurrent Neural Networks (RNN)
Deep Learning limitations and applications
TensorFlow & Keras
Model building
Training neural networks
Performance evaluation
Model Development & Deployment
Model training
Validation
Optimization techniques
Deployment concepts
Hands-On Project (Choose Any One)
Participants will complete a real-world AI/ML project:
🔹 Antimicrobial Peptide (AMP) Classification
🔹 Pneumonia Detection Using Chest X-Ray Images
🔹 TCGA Cancer Image Feature Extraction
🔹 Gene Expression Pattern Recognition
🔹 Variant Calling & Prioritization
🔹 Biomarker Discovery
Software Preparation
Participants should install:
Python:
Visual Studio Code:
Who Can Join?
Suitable for:
Graduate Students
PhD Scholars
Postdoctoral Researchers
Life Science Professionals
Bioinformatics Researchers
Conservation Biology Researchers
Evolutionary Genomics Researchers
Population Genetics Researchers
Anyone Interested in AI & Machine Learning Applications in Life Sciences
Learning Outcomes
After completing this program, participants will be able to:
✔ Develop Python-based AI and ML applications
✔ Analyze biological and genomic datasets
✔ Build machine learning and deep learning models
✔ Work with TensorFlow, Keras, Scikit-learn, and PyTorch
✔ Perform real-world bioinformatics AI projects
✔ Apply AI techniques for healthcare and genomics research
✔ Independently design and implement AI/ML workflows
Refund Policy
No refunds after registration and payment confirmation.
Course start dates may be postponed.
Registration can be transferred to an alternative course.
Contact Information
Program Outcome
Become proficient in Artificial Intelligence, Machine Learning, Deep Learning, Python Programming, and AI Applications in Bioinformatics through industry-oriented practical training and project-based learning.
