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Course overview
Module Duration: 4 Weeks
Week 1: Introduction to AI and ML
Lesson 1: Understanding Artificial Intelligence
- Definition and scope of Artificial Intelligence.
- Historical development and milestones.
- AI in daily life and industry.
Lesson 2: Basics of Machine Learning
- Differentiating between AI and ML.
- Types of machine learning: supervised, unsupervised, and reinforcement learning.
- Real-world examples of machine learning applications.
Week 2: Foundations of Mathematics for AI/ML
Lesson 1: Essential Mathematics
- Overview of linear algebra, calculus, and probability.
- Importance of mathematical concepts in AI/ML.
Lesson 2: Python Programming for ML
- Introduction to Python for data science and machine learning.
- Basic libraries: NumPy, Pandas, and Matplotlib.
Week 3: Key Algorithms and Practical Implementation
Lesson 1: Core Machine Learning Algorithms
- Linear regression, logistic regression, decision trees, and k-nearest neighbors.
- Hands-on coding exercises using Python and Jupyter Notebooks.
Lesson 2: Introduction to Deep Learning
- Understanding neural networks and deep learning.
- Overview of popular deep learning frameworks: TensorFlow and PyTorch.
Week 4: Applications and Future Trends
Lesson 1: Real-world Applications of AI/ML
- Explore how AI/ML is applied in industries such as healthcare, finance, and autonomous systems.
- Case studies and success stories.
Lesson 2: Future Trends in AI/ML
- Emerging technologies and advancements.
- Ethical considerations and responsible AI.
Assessment and Certification:
- Weekly quizzes and assignments to reinforce learning.
- Final project: Apply learned concepts to solve a real-world problem.
- Course completion certificate.
Note: After enrolment please contact here or email - mentor@ipreptoday.org
What will i learn?
- Acquire practical skills in programming and data manipulation using languages like Python and essential libraries such as NumPy and Pandas. The course may involve hands-on projects and exercises that allow you to apply your knowledge to real-world scenarios.
- Develop problem-solving abilities through exposure to various machine learning algorithms. You'll learn how to approach and solve problems using data-driven methods, enhancing your analytical and critical thinking skills
- Gain insights into the diverse applications of Artificial Intelligence and Machine Learning across industries. Understanding how these technologies are used in real-world scenarios will broaden your perspective and showcase the wide-reaching impact of AI and ML
- Lay the groundwork for further exploration in the field. An introductory course provides the necessary foundation for more advanced topics. It can serve as a stepping stone for pursuing specialized courses, engaging in research, or applying AI and ML concepts to specific domains
- Foundational Knowledge: Gain a solid understanding of the foundational concepts of Artificial Intelligence and Machine Learning. This includes learning about different types of machine learning, key algorithms, and the basic principles that underpin these technologies.
Requirements
- Foundational Mathematics Knowledge
- Programming Skills:
- Access to Online Learning Platforms
- Hands-On Practice with Datasets
- Approach AI and ML with curiosity and a willingness to explore
Curriculum for this course
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1: Introduction to AI and ML
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2: Foundations of Mathematics for AI/ML
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3: Key Algorithms and Practical Implementation
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4: Applications and Future Trends
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Assessment and Certification
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