Advanced Techniques in Machine Learning & Generative AI Course Overview

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