Advanced Techniques in Machine Learning & Generative AI Course Overview

Last Updated : 18 Jun 2024

Advanced Techniques in Machine Learning & Generative AI Course Overview

Unlock the potential of Machine Learning and Generative AI with Koenig Solutions' comprehensive 10-day course. Dive deep into Advanced Techniques in Machine Learning, including Structured Data Analysis, Statistical Analysis, and Univariate/Multivariate Analysis. Master Natural Language Processing (NLP) with hands-on text classification and sentiment analysis. Explore the power of Computer Vision using PyTorch, focusing on object detection and image segmentation. Delve into Generative AI and Large Language Models (LLMs), learning about model architectures and fine-tuning. Finally, grasp the comprehensive Machine Learning Model Lifecycle, from Model Retraining to Performance Tuning and Deployment. Equip yourself with practical skills through engaging hands-on exercises and a detailed Classification Problem Project. Enhance your career today!

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Course Fee	1,700
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Flexi Video	16,449
Official E-coursebook
Exam Voucher (optional)
Hands-On-Labs²	4,159
+ GST 18%	4,259
Total Fees (without exam & Labs)	22,359 (INR)
Total Fees (with Labs)	28,359 (INR)

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Join a free session to assess your readiness for the course. This session will help you understand the course structure and evaluate your current knowledge level to start with confidence.

Assessments (Qubits)

Take assessments to measure your progress clearly. Koenig's Qubits assessments identify your strengths and areas for improvement, helping you focus effectively on your learning goals.

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Receive comprehensive post-training reports summarizing your performance. These reports offer clear feedback and recommendations to help you confidently take the next steps in your learning journey.

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Extend your lab time at no extra cost. With free lab extensions, you get additional practice to sharpen your skills, ensuring thorough understanding and mastery of practical tasks.

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1-on-1 Training

Schedule personalized sessions based upon your availability.

Learn More

Customized Training

Learning without limits. Create custom courses that fit your exact needs, from blended topics to brand-new content.

Learn More

Happiness Guaranteed

Experience exceptional training with the confidence of our Happiness Guarantee, ensuring your satisfaction or a full refund.

Learn More

Destination Training

Immerse yourself in a focused learning environment, free from distractions, where you can sharpen your skills in popular global destinations.

Learn More

Fly-Me-A-Trainer (FMAT)

Flexible on-site learning for larger groups. Fly an expert to your location anywhere in the world.

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Course Prerequisites

Prerequisites for the Advanced Techniques in Machine Learning & Generative AI Course

To successfully undertake the "Advanced Techniques in Machine Learning & Generative AI" course, it is recommended that students have the following foundational knowledge:

Basic Programming Skills: Familiarity with Python is essential as the course includes hands-on exercises using Python and PyTorch.
Understanding of Basic Machine Learning Concepts: Basic knowledge of machine learning algorithms and workflows will be beneficial.
Familiarity with Statistics: Basic understanding of statistical concepts such as mean, median, standard deviation, and probability.
Basic Knowledge of Data Analysis: Understanding of data preparation, including data cleaning and transformation techniques.
Introduction to Linear Algebra and Calculus: Basic understanding of linear algebra (e.g., vectors, matrices) and calculus (e.g., derivatives, integrals) will help in grasping machine learning concepts.

These prerequisites are intended to ensure that students are well-prepared to fully benefit from the advanced topics covered in this comprehensive course.

Target Audience for Advanced Techniques in Machine Learning & Generative AI

Introduction:
The Advanced Techniques in Machine Learning & Generative AI course is designed for professionals seeking to enhance their expertise in cutting-edge AI and ML techniques.

Job Roles and Audience:

Data Scientists
Machine Learning Engineers
AI Researchers
Data Analysts
Software Engineers
NLP Engineers
Computer Vision Engineers
Statisticians
Research Scientists
IT Professionals transitioning to AI
Technology Consultants
Big Data Engineers
Data Architects
Business Intelligence Analysts
Software Developers interested in AI/ML
Academic Researchers in AI/ML
Professionals working with Python and PyTorch
Technical Project Managers overseeing AI/ML projects
Students pursuing advanced studies in AI/ML
Product Managers in AI-driven products

Learning Objectives - What you will Learn in this Advanced Techniques in Machine Learning & Generative AI?

Introduction The Advanced Techniques in Machine Learning & Generative AI course by Koenig Solutions is a 10-day, intensive program designed to cover sophisticated methodologies and practical applications in data analysis, NLP, computer vision, generative AI, and the machine learning model lifecycle.

Learning Objectives and Outcomes

Data Preparation and Validation
- Master data wrangling techniques to clean and prepare structured datasets.
- Validate datasets to ensure data quality and consistency.
Feature Engineering and Selection
- Learn advanced feature selection and engineering methods to enhance model performance.
Statistical Analysis
- Conduct hypothesis testing using Z-tests, t-tests, and Chi-square tests.
- Build confidence intervals and perform ANOVA.
Univariate and Multivariate Analysis
- Analyze data using descriptive statistics, correlation, covariance, and regression analysis.
Natural Language Processing (NLP)
- Implement text classification, document clustering, sentiment analysis, and advanced learning methods like one-shot and few-shot learning.
Computer Vision using PyTorch
- Develop models for object detection, recognition, image segmentation, and OCR using PyTorch.
Generative AI and Large Language Models (LLMs)
- Understand model architectures, perform prompt engineering