Requirements
- Basic computer literacy: Familiarity with using a computer and navigating the internet.
- Willingness to learn: A curious mind and a desire to explore the fascinating world of AI and Machine Learning.
- Optional (but helpful) Prior programming experience: While not mandatory, some experience with programming concepts can be beneficial. The course will cover essential Python programming for those new to coding.
- Optional (but helpful) Mathematical background: A basic understanding of algebra will be helpful for grasping some machine learning concepts.
Features
- Expert-led instruction: Learn from experienced instructors passionate about AI and Machine Learning.
- Interactive learning: Engage with interactive lectures, discussions, and hands-on coding exercises.
- Real-world applications: Explore how AI and Machine Learning are transforming various industries.
- Python for Machine Learning: Master the fundamentals of Python programming for practical machine learning implementation.
- Project-based learning: Apply your acquired knowledge by building and evaluating a machine learning model for a real-world problem (final project).
This comprehensive course equips you with a solid foundation in Artificial Intelligence (AI) and its core application, Machine Learning (ML). We’ll explore the fundamental concepts, techniques, and applications of AI, with a deep dive into popular machine learning algorithms like linear regression and logistic regression using Python.
Learning Objectives:
- Gain a comprehensive understanding of Artificial Intelligence and its various branches.
- Explore the philosophy, goals, and history of AI research.
- Identify key contributors to the field of AI and understand different AI techniques.
- Grasp the concepts of intelligent systems, agents, and environments.
- Demystify Fuzzy Logic Systems and their applications.
- Master the fundamentals of Natural Language Processing (NLP) and its components.
- Delve into the world of Robotics and Computer Vision.
- Uncover the power of Artificial Neural Networks (ANNs) and their working principles.
- Become proficient in core Machine Learning concepts like supervised and unsupervised learning.
- Implement practical machine learning algorithms using Python (linear regression, logistic regression).
- Understand the importance of model evaluation and address overfitting with regularization techniques.
The field of AI and Machine Learning is rapidly growing, creating a high demand for skilled professionals. This course equips you with the skills to pursue careers like data scientist, machine learning engineer, AI researcher, and more!
The course offers in-depth exploration of 2-3 specific AI areas chosen based on the target audience's interest. These may include Fuzzy Logic Systems, Natural Language Processing, Robotics, or Artificial Neural Networks.
You'll gain hands-on experience with popular machine learning algorithms like linear regression and logistic regression using Python libraries. The course also introduces you to concepts like supervised learning, unsupervised learning, and regularization techniques.
While this course focuses on foundational concepts, we'll provide a brief introduction to Artificial Neural Networks, which are a core building block of Deep Learning. If you're interested in a deeper dive into Deep Learning, we offer a separate advanced course.