Fundamentals of Artificial Intelligence (AI) and Machine Learning (ML) Course Overview

To ensure the best learning experience and success in the Fundamentals of Artificial Intelligence (AI) and Machine Learning (ML) course, the following minimum prerequisites are recommended for students:

• Basic understanding of programming concepts: Familiarity with the fundamentals of programming, such as variables, loops, and functions, is beneficial as the course involves hands-on coding exercises.
• Knowledge of Python: Since the course uses Python for implementing AI and ML concepts, a foundational knowledge of Python programming is essential.
• Fundamental mathematics skills: A grasp of high school level mathematics, particularly algebra and a basic understanding of calculus and statistics, will help in understanding the algorithms and statistical concepts covered in the course.
• Logical and analytical thinking: The ability to think logically and analytically will aid in understanding and solving the complex problems presented during the training.
• Computer literacy: Comfort with using computers and navigating software installations is necessary, as the course requires setting up a Python data science environment and other tools.

Please note that while these prerequisites are aimed at ensuring a smooth learning curve, our course is designed with step-by-step guidance to help all learners, regardless of background, to grasp the fundamental concepts of AI and ML.

Target Audience for Fundamentals of Artificial Intelligence (AI) and Machine Learning (ML)

1. This course provides foundational knowledge in AI and ML, ideal for professionals seeking to upskill in data science.

   
   

2. Target audience for the Fundamentals of Artificial Intelligence (AI) and Machine Learning (ML) course:

   
   

• Aspiring Data Scientists
• Software Developers wanting to specialize in AI/ML
• Data Analysts transitioning to AI/ML roles
• Computer Science Graduates looking to enhance their AI/ML knowledge
• IT Professionals interested in understanding data-driven technologies
• Statisticians aiming to apply their expertise in new technologies
• Business Analysts seeking to leverage AI/ML for better insights
• Product Managers aiming to incorporate AI/ML in product development
• Technical Managers looking for a comprehensive understanding of AI/ML to lead teams
• Researchers and Academics in fields related to AI/ML
• Engineers who want to understand AI/ML applications in their domain
• AI/ML Hobbyists or Enthusiasts eager to formalize their knowledge

Learning Objectives - What you will Learn in this Fundamentals of Artificial Intelligence (AI) and Machine Learning (ML)?

Introduction to Learning Outcomes:

This AI and ML fundamentals course equips you with a comprehensive Python data science toolkit, statistical knowledge, and practical machine learning competencies to tackle real-world data challenges.

Learning Objectives and Outcomes:

• Install and manage a Python data science environment to perform data analysis and machine learning tasks.
• Utilize iPython (Jupyter) Notebooks effectively for interactive computing and visualization.
• Understand and apply Python basics to manipulate data and execute scripts for various AI and ML tasks.
• Refresh core statistics and probability concepts and apply them in Python to interpret data insights.
• Gain proficiency in Matplotlib for data visualization and explore advanced probability concepts such as Bayes' theorem.
• Comprehend and implement different machine learning algorithms including linear, non-linear, and ensemble methods.
• Develop predictive models like linear and polynomial regression and apply multivariate regression to real-life scenarios such as car price prediction.
• Apply practical machine learning techniques using Python to real-world datasets, including overfitting prevention and implementing a spam classifier.
• Create and improve recommender systems using collaborative filtering and understand the mechanisms behind movie recommendations.
• Explore more advanced machine learning techniques such as dimensionality reduction with PCA and reinforcement learning.
• Understand the challenges of real-world data, including the bias/variance trade-off, and implement strategies like k-fold cross-validation for model validation.
• Get hands-on experience with Spark for big data processing and machine learning, and learn how to build scalable models with MLlib.
• Design and interpret A/B tests with a strong grasp of experimental design principles, including calculating t-statistics and p-values using Python.
• Develop practical skills to build user interfaces and REST APIs for deploying machine learning models in production environments.