Professional Certificate in Applied Artificial Intelligence

<h1 style=”font-size: 25px; font-family: HLB; color: #d2af13;”><strong>Programme Overview:</strong></h1>

The Professional Certificate in Applied Artificial Intelligence (AI) is designed to enhance
participants’ expertise in implementing and managing AI and Machine Learning (ML)
systems. The course covers fundamental definitions, applications, AI/ML approaches, tools, and techniques. It begins with an introduction to AI and ML, highlighting key concepts and
historical developments that have shaped current practices. Participants will gain a deep understanding of AI/ML principles, including algorithmic
approaches, constraints, and the use of various tools such as Rapidminer, Python, and Octave. The course also addresses compliance with industry standards and best practices, ensuring
participants are well-versed in legal and ethical requirements. Practical skills covered include implementing machine learning modules, developing simple
ML applications using Python, applying deep learning techniques, and utilizing Natural
Language Processing (NLP). The course also focuses on problem-solving techniques such as
logic rules, fuzzy expert systems, and evolutionary algorithms. By the end of the course, participants will be equipped to develop and maintain effective
AI/ML systems, conduct data analytics, ensure ethical compliance, and apply AI solutions to
real-world business challenges. Successful completion leads to certification, recognizing the
individual’s competence in applied artificial intelligence and machine learning.

CoursesProgramme Outcome

<hr />

<h3 style=”text-transform: capitalize;”>Programme Structure</h3>
Programme Duration:
Total Duration: 10 weeks
Schedule: 3 hours per day, 6 days a week

<table>
<tbody>
<tr>
<th>Weeks</th>
<th>Days</th>
<th>Subject</th>
</tr>
<tr>
<td rowspan=”2″>Week 1: Introduction to AI and Machine Learning</td>
<td>Day 1-3</td>
<td>
<ul>
<li>Introduction to Artificial Intelligence
<ul>
<li>Definitions and Applications</li>
<li>Historical Development and Key Figures</li>
</ul>
</li>
</ul>
<ul>
<li>Basic Concepts in Machine Learning
<ul>
<li>Supervised vs. Unsupervised Learning</li>
</ul>
</li>
</ul>
</td>
</tr>
<tr>
<td>Day 4-6</td>
<td>
<ul>
<li>Overview of AI/ML Approaches
<ul>
<li>Algorithms and Constraints</li>
<li>Ethical and Legal Considerations</li>
</ul>
</li>
</ul>
</td>
</tr>
<tr>
<td rowspan=”2″>Week 2: Rapidminer Essentials</td>
<td>Day 1-3</td>
<td>
<ul>
<li>Introduction to Rapidminer
<ul>
<li>Environment and Interface</li>
<li>Basic Features and Functions</li>
</ul>
</li>
</ul>
</td>
</tr>
<tr>
<td>Day 4-6</td>
<td>
<ul>
<li>Practical Exercises with Rapidminer
<ul>
<li>Data Import and Preprocessing</li>
<li>Basic Data Analytics</li>
</ul>
</li>
</ul>
</td>
</tr>
<tr>
<td rowspan=”2″>Week 3: Machine Learning with Rapidminer</td>
<td>Day 1-3</td>
<td>
<ul>
<li>Designing ML Systems in Rapidminer
<ul>
<li>Building and Training Models</li>
<li>Supervised Learning Algorithms</li>
</ul>
</li>
</ul>
</td>
</tr>
<tr>
<td>Day 4-6</td>
<td>
<ul>
<li>Implementing ML Systems
<ul>
<li>Evaluation and Testing</li>
<li>Model Optimization</li>
</ul>
</li>
</ul>
</td>
</tr>
<tr>
<td rowspan=”2″>Week 4: Python for Machine Learning</td>
<td>Day 1-3</td>
<td>
<ul>
<li>Introduction to Python Programming
<ul>
<li>Basics of Python Syntax</li>
<li>Data Structures and Libraries</li>
</ul>
</li>
</ul>
</td>
</tr>
<tr>
<td>Day 4-6</td>
<td>
<ul>
<li>Python for ML
<ul>
<li>Developing Simple ML Modules</li>
<li>Practical Exercises and Projects</li>
</ul>
</li>
</ul>
</td>
</tr>
<tr>
<td rowspan=”2″>Week 5: Deep Learning Basics</td>
<td>Day 1-3</td>
<td>
<ul>
<li>Introduction to Deep Learning
<ul>
<li>Basic Concepts and Applications</li>
<li>Neural Networks</li>
</ul>
</li>
</ul>
</td>
</tr>
<tr>
<td>Day 4-6</td>
<td>
<ul>
<li>Implementing Deep Learning Models
<ul>
<li>Using TensorFlow and Keras</li>
<li>Practical Exercises</li>
</ul>
</li>
</ul>
</td>
</tr>
<tr>
<td rowspan=”2″>Week 1: Introduction to AI and Machine Learning</td>
<td>Day 1-3</td>
<td>
<ul>
<li>Introduction to Artificial Intelligence
<ul>
<li>Definitions and Applications</li>
<li>Historical Development and Key Figures</li>
</ul>
</li>
</ul>
<ul>
<li>Basic Concepts in Machine Learning
<ul>
<li>Supervised vs. Unsupervised Learning</li>
</ul>
</li>
</ul>
</td>
</tr>
<tr>
<td>Day 4-6</td>
<td>
<ul>
<li>Overview of AI/ML Approaches
<ul>
<li>Algorithms and Constraints</li>
<li>Ethical and Legal Considerations</li>
</ul>
</li>
</ul>
</td>
</tr>
<tr>
<td rowspan=”2″>Week 2: Rapidminer Essentials</td>
<td>Day 1-3</td>
<td>
<ul>
<li>Introduction to Rapidminer
<ul>
<li>Environment and Interface</li>
<li>Basic Features and Functions</li>
</ul>
</li>
</ul>
</td>
</tr>
<tr>
<td>Day 4-6</td>
<td>
<ul>
<li>Practical Exercises with Rapidminer
<ul>
<li>Data Import and Preprocessing</li>
<li>Basic Data Analytics</li>
</ul>
</li>
</ul>
</td>
</tr>
<tr>
<td rowspan=”2″>Week 3: Machine Learning with Rapidminer</td>
<td>Day 1-3</td>
<td>
<ul>
<li>Designing ML Systems in Rapidminer
<ul>
<li>Building and Training Models</li>
<li>Supervised Learning Algorithms</li>
</ul>
</li>
</ul>
</td>
</tr>
<tr>
<td>Day 4-6</td>
<td>
<ul>
<li>Implementing ML Systems
<ul>
<li>Evaluation and Testing</li>
<li>Model Optimization</li>
</ul>
</li>
</ul>
</td>
</tr>
<tr>
<td rowspan=”2″>Week 4: Python for Machine Learning</td>
<td>Day 1-3</td>
<td>
<ul>
<li>Introduction to Python Programming
<ul>
<li>Basics of Python Syntax</li>
<li>Data Structures and Libraries</li>
</ul>
</li>
</ul>
</td>
</tr>
<tr>
<td>Day 4-6</td>
<td>
<ul>
<li>Python for ML
<ul>
<li>Developing Simple ML Modules</li>
<li>Practical Exercises and Projects</li>
</ul>
</li>
</ul>
</td>
</tr>
<tr>
<td rowspan=”2″>Week 5: Deep Learning Basics</td>
<td>Day 1-3</td>
<td>
<ul>
<li>Introduction to Deep Learning
<ul>
<li>Basic Concepts and Applications</li>
<li>Neural Networks</li>
</ul>
</li>
</ul>
</td>
</tr>
<tr>
<td>Day 4-6</td>
<td>
<ul>
<li>Implementing Deep Learning Models
<ul>
<li>Using TensorFlow and Keras</li>
<li>Practical Exercises</li>
</ul>
</li>
</ul>
</td>
</tr>
</tbody>
</table>

<h1 style=”font-size: 25px; font-family: HLB; color: #d2af13;”><strong>Programme Outcome:</strong></h1>
Upon successful completion of this 10-week intensive course, participants will be able to:
<ol>
<li>Articulate AI and ML Fundamentals:
<ul>
<li>Define and explain the basic concepts, historical development, and applications of Artificial Intelligence and Machine Learning</li>
</ul>
</li>
<li>Implement AI/MLAlgorithms:
<ul>
<li>Understand various AI/ML approaches and constraints, and demonstrate how algorithms function in practical scenarios.</li>
</ul>
</li>
<li>Utilize Rapidminer for Machine Learning:
<ul>
<li>Navigate and effectively use the Rapidminer environment to design, implement, and evaluate machine learning systems.</li>
</ul>
<li>Develop ML Modules Using Python:
<ul>
<li>Write and execute Python code to develop simple machine learning modules, utilizing essential libraries and data structures.</li>
</ul>
</li>
<li>Comprehend Deep Learning Concepts:
<ul>
<li>Understand basic to advanced deep learning techniques, including neural networks, and apply these concepts in various business contexts.</li>
</ul>
</li>
<li>Apply Natural Language Processing Techniques:
<ul>
<li>Demonstrate an understanding of NLP basics and develop simple NLP systems using deep learning algorithms.</li>
</ul>
</li>
<li>Perform Data Analytics Using Octave:
<ul>
<li>Utilize Octave for matrix computation, data analytics, and machine learning, developing efficient programs and solutions.</li>
<li>Design and Implement AI Solutions:
<ul>
<li>Design rules-based fuzzy expert systems and evolutionary algorithms for solving optimization problems.</li>
</ul>
</li>
<li>Integrate AI into Business Solutions:
<ul>
<li>Identify and implement AI applications in real-world business scenarios, assessing their impact and optimizing business processes.</li>
</ul>
</li>
<li>Develop Comprehensive AI Projects:
<ul>
<li>Plan, develop, and present comprehensive AI solutions through capstone projects, demonstrating the integration of learned techniques and tools.</li>
</ul>
</li>
<li>Ensure Ethical and Legal Compliance:
<ul>
<li>Understand and apply ethical guidelines and legal requirements related to AI/ML implementation in business contexts.</li>
</ul>
</li>
<li>Promote a Culture of AI-Driven Innovation:
<ul>
<li>Foster a culture of innovation and productivity within organizations by leveraging AI and ML technologies to address business challenges.</li>
</ul>
</li>
</ol>

Participants will leave the course equipped with practical skills, hands-on experience, and a
recognized certification, positioning them as competent professionals in the field of applied
artificial intelligence.