Artificial Intelligence (AI) with Machine Learning & Deep Learning

Gain a comprehensive understanding of artificial intelligence, machine learning, and deep learning. This course covers AI fundamentals, model development, deployment, governance, and responsible AI practices for today's data-driven organizations.

$29.99
  • 2.5 hours
  • English
  • Certificate
  • Online

Course Overview

Artificial Intelligence (AI) with Machine Learning & Deep Learning is a professional training course designed for learners, professionals, and organizations that need a structured understanding of modern AI systems, intelligent automation, and data-driven technologies. As artificial intelligence continues to reshape industries, business operations, digital products, public services, and workforce capabilities, professionals need to understand not only how AI works, but also how it is developed, deployed, governed, and used responsibly.

This course covers the foundations of artificial intelligence, machine learning, deep learning, neural networks, data preparation, algorithm selection, model development, AI deployment, MLOps, LLMOps, generative AI, AI agents, model monitoring, security, ethics, governance, and regulatory considerations. Participants will also explore advanced AI strategy, industry-specific implementation, artificial general intelligence, frontier models, alignment, and long-term AI safety.

The curriculum is relevant for professionals involved in technology, data, analytics, compliance, governance, risk management, business transformation, innovation, and organizational leadership. It supports both technical and non-technical learners seeking professional knowledge of AI, machine learning, and deep learning.

Course Includes

Participants will receive structured learning aligned with the course curriculum and focused on modern AI, machine learning, and deep learning concepts, including:

  • Curriculum-based professional training

  • Artificial intelligence foundation knowledge

  • Machine learning and deep learning concepts

  • Data, algorithms, and model development coverage

  • AI deployment, MLOps, and LLMOps understanding

  • Responsible AI, governance, and compliance knowledge

  • Certificate upon successful completion

What You'll Learn

  • Understand the evolution of artificial intelligence, machine learning, deep learning, and the modern AI ecosystem.
  • Identify core principles of data-driven intelligence, neural networks, representation learning, and global AI applications.
  • Analyze data collection, preparation, quality management, feature engineering, and model development requirements.
  • Assess supervised, unsupervised, semi-supervised, and reinforcement learning methods.
  • Evaluate deep learning architectures, including CNNs, RNNs, LSTMs, transformers, and foundation models.
  • Apply knowledge of AI development lifecycles, MLOps, LLMOps, cloud AI infrastructure, and production deployment.
  • Monitor AI security, adversarial threats, model performance, operational resilience, and emerging risks.
  • Evaluate responsible AI, governance frameworks, privacy, explainability, transparency, human oversight, and AI assurance.

Requirements

No specific prior experience or qualifications are required to participate in this course. A general interest in artificial intelligence, machine learning, deep learning, data, analytics, technology strategy, digital transformation, AI governance, or responsible innovation may be helpful, but it is not mandatory.

Why Choose Us

This Artificial Intelligence with Machine Learning & Deep Learning course is designed to provide structured, relevant, and professionally useful knowledge for modern AI-enabled workplaces.

  • Curriculum aligned with AI foundations, machine learning, deep learning, deployment, governance, and future technologies
  • Clear coverage of data preparation, algorithms, model training, validation, optimization, and performance measurement
  • Professional training suitable for individual learners, business teams, and organizational decision-makers
  • Balanced focus on technical AI concepts, responsible governance, regulatory awareness, and business transformation
  • Workplace-relevant knowledge for AI adoption, data-driven decision-making, operational resilience, and leadership
  • Learner-focused structure that supports understanding across technical and non-technical professional audiences
  • Professional development support through structured learning and successful-completion certification

Career path

This course supports professional development across roles and responsibilities that require knowledge of AI systems, machine learning models, deep learning methods, responsible AI, and organizational transformation.

Relevant career areas and responsibilities include:

  • Artificial Intelligence Strategy
  • Machine Learning Project Support
  • Data Science and Analytics
  • AI Product and Innovation Support
  • AI Governance and Risk Management
  • Digital Transformation Leadership
  • AI Security and Operational Resilience
  • Responsible AI and Compliance Support

Certification

Certification

A certificate is issued upon successful completion of the course. This certificate demonstrates that the participant has completed structured professional training in Artificial Intelligence (AI) with Machine Learning & Deep Learning and has developed knowledge of the main topics covered in the curriculum.

Course Curriculum

5 sections20 lectures2.5 hours
▶ 1. The Evolution of Artificial Intelligence and the Modern AI Ecosystem
▶ 2. Core Principles of Machine Learning and Data-Driven Intelligence
▶ 3. Deep Learning, Neural Networks, and Representation Learning
▶ 4. Global AI Applications, Opportunities, Limitations, and Societal Impact
▶ 1. Data Collection, Preparation, Quality Management, and Feature Engineering
▶ 2. Supervised, Unsupervised, Semi-Supervised, and Reinforcement Learning Methods
▶ 3. Deep Learning Architectures: CNNs, RNNs, LSTMs, Transformers, and Foundation Models
▶ 4. Model Training, Validation, Evaluation, Optimization, and Performance Measurement
▶ 1. AI Development Lifecycle and End-to-End Project Methodology
▶ 2. MLOps, LLMOps, Cloud AI Infrastructure, and Production Deployment
▶ 3. Generative AI, Large Language Models, Retrieval-Augmented Generation, and AI Agents
▶ 4. AI Security, Adversarial Threats, Model Monitoring, and Operational Resilience
▶ 1. AI Ethics, Human Rights, Fairness, Bias, and Accountability
▶ 2. Global AI Governance Frameworks, OECD Principles, UNESCO Guidance, and NIST AI RMF
▶ 3. AI Laws, Privacy Regulations, Intellectual Property, and Data Protection Requirements
▶ 4. Risk Management, Explainability, Transparency, Human Oversight, and AI Assurance
▶ 1. AI Strategy, Business Transformation, and Industry-Specific Implementation
▶ 2. AI in Healthcare, Finance, Manufacturing, Education, Government, and Emerging Sectors
▶ 3. Artificial General Intelligence (AGI), Frontier Models, Alignment, and Long-Term AI Safety
▶ 4. The Future of Human-AI Collaboration, Workforce Evolution, and Global AI Leadership