Complete Artificial Intelligence for Beginners Course Overview

Prerequisites for the Complete Artificial Intelligence for Beginners Course:

• Basic Knowledge of Object-Oriented Programming: Familiarity with object-oriented programming concepts is essential. While Python is recommended, knowledge of any object-oriented programming language is sufficient.
• Basic Familiarity with Python: Understanding Python basics, such as syntax, data types, and control structures, will be advantageous.
• Willingness to Learn: An eagerness to dive into artificial intelligence and data science, along with dedication and commitment to the intensive 15-day course.

These prerequisites ensure you have the foundational knowledge required to successfully undertake this comprehensive training in artificial intelligence.

Target Audience for Complete Artificial Intelligence for Beginners

The "Complete Artificial Intelligence for Beginners" course provides a robust grounding in Python programming, machine learning, and deep learning, ideal for those looking to start a career in AI and data science.

• Aspiring Data Scientists
• Machine Learning Engineers
• Software Developers interested in AI
• IT Professionals looking to upskill
• Graduates with a background in Computer Science or Engineering
• Research Scientists
• Technical Project Managers
• Analytics Professionals
• Business Analysts
• AI Enthusiasts and Hobbyists

Learning Objectives - What you will Learn in this Complete Artificial Intelligence for Beginners?

Course Introduction and Learning Outcomes

The Complete Artificial Intelligence for Beginners course aims to provide a solid foundation in AI, covering key concepts and practical skills in Python programming, machine learning, and deep learning. Participants will gain hands-on experience to proficiently analyze, model, and solve real-world data science problems.

Learning Objectives and Outcomes

• Understanding AI Fundamentals

  • What AI is, its history, and its applications across various fields.
  • Key differences and roles of machine learning and deep learning.

• Mastering Python for Data Science

  • Python installation, setup, and basics, including syntax, data types, control structures, functions, and modules.
  • Proficiency in essential Python libraries: NumPy, Pandas, Matplotlib, and Seaborn.

• Data Analysis and Pre-processing

  • Techniques for data cleaning, handling missing/categorical data, and Exploratory Data Analysis (EDA) with visualizations.

• Data Preprocessing Techniques

  • Methods for data normalization, feature encoding, and splitting datasets into train, test, and validation sets.

• Supervised and Unsupervised Learning

  • Understanding regression (Simple and Multiple Linear Regression) and classification techniques (Logistic Regression, SVM, Decision Trees