Artificial Intelligence: Machine Learning Essentials

Artificial Intelligence and Machine Learning Essentials is a foundational training program designed for individuals eager to understand the principles of AI and ML and apply them to solve real-world challenges. This course serves as a starting point for professionals, students, and enthusiasts who aim to build practical skills and foundational knowledge in this rapidly growing field.

What You’ll Learn

By the end of this course, participants will:

• Understand key AI and ML concepts, such as supervised and unsupervised learning, neural networks, and model evaluation.
• Gain hands-on experience with essential tools like Python, TensorFlow, and Scikit-learn.
• Build and train machine learning models to solve classification, regression, and clustering problems.
• Learn best practices for data preparation, feature engineering, and algorithm selection.
• Explore ethical considerations in AI, including bias, fairness, and responsible deployment of intelligent systems.
• Develop a roadmap for integrating AI and ML into workflows, projects, or organizational strategies.

This course is designed for:

1. Beginners looking to enter the field of AI and ML without prior experience.
2. Data Analysts and Developers seeking to add AI and ML capabilities to their skillset.
3. Business Decision-Makers who want to understand the potential of AI and ML for driving innovation and efficiency.
4. Students interested in learning about intelligent systems to prepare for advanced studies.

Course Highlights

1. Practical Hands-On Experience: Participants will work on real-world projects and exercises, ensuring they gain practical skills.
2. Expert Guidance: Learn from experienced instructors with industry knowledge in AI and ML applications.
3. Tool Proficiency: Master widely-used tools and frameworks, including Python libraries, TensorFlow, and Scikit-learn.
4. Ethics in AI: Address critical issues of fairness, bias, and transparency in AI solutions.
5. Career Advancement: Acquire the skills needed to enter AI and ML roles or apply advanced technologies in current work.

Course Modules

1. Introduction to Artificial Intelligence: Understanding the scope, definitions, and real-world applications of AI.
2. Machine Learning Fundamentals: Covering supervised and unsupervised learning, including key algorithms.
3. Deep Learning Basics: Exploring neural networks and the foundational ideas behind deep learning.
4. Practical Tools for AI and ML: Using Python, TensorFlow, and Scikit-learn to build and evaluate models.
5. Case Studies and Applications: Solving real-world problems in domains like healthcare, finance, and marketing.
6. Ethics and Responsible AI: Understanding the implications of deploying AI solutions.

Learning Outcomes

Participants completing this course will be able to:

• Analyze and prepare data for machine learning models.
• Select appropriate algorithms for various AI and ML tasks.
• Build, train, and fine-tune models to solve practical problems.
• Understand the ethical implications of AI and apply responsible practices in projects.
• Effectively communicate AI-driven insights to stakeholders and team members.
• Plan and execute the adoption of AI solutions in their organizations

Why This Course?

This course stands out because it bridges the gap between theoretical knowledge and practical application. It is structured to cater to different learning needs, whether you are exploring AI for the first time or looking to enhance your professional skillset. By the end of this program, participants will not only have the confidence to work with AI and ML but also the expertise to make a meaningful impact in their respective fields.

• By the end of the course, the students should be comfortable applying what they learned (building their own datasets and applying ML algorithms on them) and extending their knowledge in this domain confidently.

1. Introduction. Defining terms (AI, ML, Deep Learning, etc.). Mapping the domain.

Getting to know the participants: 30 minutes / Theory: 30 minutes

2. Machine learning basics. Supervised vs Unsupervised learning. Algorithms overview (Decision Trees, Regression, Clustering etc.). Code examples with the typical ML workflow.

Theory: 1h / Code examples 1h

3. Neural Networks basics. Simple NN, Convolutional NN (image processing), Recurrent NN (text processing). Adjusting parameters. Small code example that can be extended to other tasks.

Theory: 1h / Code examples 1h

4. Data Mining. Data preprocessing. How to make your own dataset.

Theory: 30 minutes / Code examples: 1h 30 minutes