Artificial Intelligence & Machine Learning Certificate Program
- Mr. Mukesh Pandey
- (5.0/ 2 Ratings)
Course Description
Acadmiac’s Artificial Intelligence & Machine Learning Skill-Building Course is designed to introduce learners to the core concepts, techniques, and real-world applications of AI and machine learning through a structured, practical approach. The course covers programming foundations, data handling, machine learning algorithms, model evaluation, deep learning basics, AI applications, and responsible AI practices. Delivered through progressive modules and hands-on projects, the program helps learners understand how intelligent systems are built, trained, and evaluated. By the end of the course, learners gain conceptual clarity, analytical thinking, and practical exposure required to begin their journey in artificial intelligence and machine learning.
The Acadmiac Advantage
- Build a strong conceptual foundation in artificial intelligence and machine learning principles.
- Understand how data, algorithms, and models work together to create intelligent systems.
- Learn practical workflows and tools used in AI and ML development environments.
- Apply what you learn through projects that reflect real-world AI and machine learning use cases.
At Acadmiac, we believe future-ready careers are built on strong fundamentals and responsible innovation. This course is designed to help learners move beyond buzzwords and develop a clear understanding of how artificial intelligence and machine learning work in practice. With every module, students gain confidence in analyzing problems, working with data, and applying AI concepts thoughtfully. By the end of the program, learners are prepared to explore entry-level AI and ML roles or advance toward specialized learning pathways.
Module 1: Introduction to Artificial Intelligence & Machine Learning
Understand what AI and ML are and how they are used in real-world systems.
- What is artificial intelligence
- What is machine learning
- AI vs ML vs data science
- Real-world applications of AI
➤ Practical Tasks
Outcome: Understand the scope and impact of AI and machine learning.
Module 2: Programming Foundations for AI & ML
Build a basic understanding of programming required for AI and ML concepts.
- Programming fundamentals
- Variables, data types, and logic
- Control flow and functions
- Introduction to Python for AI
➤ Practical Tasks
Outcome: Write basic programs used in AI and ML workflows.
Module 3: Mathematics & Statistics for Machine Learning
Learn essential math and statistics concepts used in ML models.
- Linear algebra basics
- Probability concepts
- Descriptive statistics
- Understanding data distributions
➤ Practical Tasks
Outcome: Apply basic math and statistics concepts in ML contexts.
Module 4: Data Handling & Preprocessing
Prepare and clean data before applying machine learning techniques.
- Types of data
- Data collection methods
- Data cleaning techniques
- Feature understanding
➤ Practical Tasks
Outcome: Prepare datasets for machine learning tasks.
Module 5: Machine Learning Concepts & Algorithms
Understand how machine learning models work.
- Supervised vs unsupervised learning
- Regression and classification basics
- Clustering overview
- Model training concepts
➤ Practical Tasks
Outcome: Understand how basic machine learning algorithms function.
Module 6: Model Evaluation & Performance Metrics
Learn how machine learning models are evaluated and improved.
- Training vs testing data
- Model accuracy and metrics
- Overfitting and underfitting
- Model improvement basics
➤ Practical Tasks
Outcome: Evaluate machine learning models effectively.
Module 7: Introduction to Deep Learning
Explore neural networks and deep learning fundamentals.
- What is deep learning
- Neural network basics
- Layers and activation functions
- Use cases of deep learning
➤ Practical Tasks
Outcome: Understand foundational deep learning concepts.
Module 8: AI Applications & Use Cases
See how AI is applied across industries.
- AI in healthcare, finance, and marketing
- Recommendation systems
- Chatbots and virtual assistants
- Image and speech recognition
➤ Practical Tasks
Outcome: Identify real-world applications of AI and ML.
Module 9: AI Tools, Platforms & Workflows
Learn tools and environments used in AI development.
- AI development tools overview
- ML libraries introduction
- Model development workflow
- Documentation and reporting
➤ Practical Tasks
Outcome: Understand professional AI and ML workflows.
Module 10: Ethics, Bias & Responsible AI
Understand ethical considerations in AI systems.
- AI ethics fundamentals
- Bias in AI models
- Data privacy concerns
- Responsible AI practices
➤ Practical Tasks
Outcome: Understand ethical responsibilities in AI development.
Module 11: AI & ML Mini Project
Apply learning through guided implementation.
- Problem identification
- Data preparation
- Model selection
- Result interpretation
➤ Practical Tasks
Outcome: Apply AI and ML concepts to a practical problem.
Module 12: Final Project & Assessment
Demonstrate learning through a comprehensive project.
- End-to-end AI/ML project
- Model evaluation
- Project presentation
- Final assessment
➤ Practical Tasks
Outcome: Build and present an AI and machine learning project.
Mr. Mukesh Pandey
Artificial Intelligence & Machine Learning Educator
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Course Includes:
- Price:
35,000 INR31,500 INR - Instructor: Mr. Mukesh Pandey
- Duration: 16 Weeks
- Lessons: 12 Modules
- Mode: On-Campus
- Language: English
- Certifications: Yes
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