Level 1
Introduction to Machine Learning
Registrations Open !!
Basic Computer Knowledge
Registrations Open !!
Basic Computer Knowledge
This course introduces the fundamental concepts of Machine Learning, including types of learning, mathematical foundations, neural networks, and deep learning. It also explores data visualization, dashboard development, and emerging trends in ML and DL.
| Day | Topic | Sub-Topic No. | Sub-Topic Title | Detailed Contents |
| Day 1 | Foundations of Machine Learning and Generative AI | 1.1 | Course Overview & Orientation | Introduction to course structure, learning goals, and expected outcomes. |
| 1.2 | Introduction to ML | Understanding the concept of machines learning from data. | ||
| 1.3 | Difference between ML and DL | Comparison of machine learning and deep learning approaches | ||
| 1.4 | Introduction to Generative Al | Overview of AI models capable of generating new content | ||
| 1.5 | Generative Al as a type of deep learning | Understanding generative models within deep learning frameworks. | ||
| 1.6 | Application to ML and DL | Exploring real-world uses of machine learning and deep learning | ||
| 1.7 | What brought a change during development of ML? | Discussion of technological advances such as big data, GPUs, and improved algorithms.. | ||
| Day 2 | Mathematical Foundations for Machine Learning | 2.1 | Basic concepts for understanding graphs? | Introduction to graph concepts used in ML data representation. |
| 2.2 | Probability and Statistics | Understanding probability distributions and statistical methods used in ML | ||
| 2.3 | Mathematical concepts behind the working of Computer Vision | Introduction to mathematical principles used in image processing. | ||
| 2.4 | Linear algebra to build ML models | Understanding vectors, matrices, and operations used to build ML models. | ||
| 2.5 | Dimensionality Reduction-Importance and Implementation | Techniques to reduce data complexity while preserving information. | ||
| Day 3 | Data Visualization and Dashboard Development | 3.1 | Introduction to Dashboards, their importance and applications | Understanding dashboards and their role in data analysis and decision making. |
| 3.2 | No-code data visualization tools and their features | Overview of tools that allow visualizations without programming. | ||
| 3.3 | Building a dashboard | Demonstration of creating a basic data dashboard | ||
| 3.4 | Different data visualization techniques used in a dashboard | Exploring charts and graphs used to present data effectively. | ||
| Day 4 | Supervised Learning in Machine Learning | 4.1 | Types of Learning | Overview of supervised, unsupervised, and reinforcement learning paradigms |
| 4.2 | Supervised Learning based ML models | Understanding models that learn from labeled data for prediction task | ||
| Day 5 | Unsupervised and Reinforcement Learning | 5.1 | Unsupervised Learning based ML models | Understanding algorithms that identify patterns in unlabeled data |
| 5.2 | Reinforcement Learning | Learning through interaction with an environment using rewards and penalties. | ||
| Day 6 | Neural Networks, Deep Learning, and Future Trends | 6.1 | Neural Networks | Understanding artificial neural networks inspired by biological neurons. |
| 6.2 | Deep Learning | Exploring multi-layer neural networks for complex learning tasks. | ||
| 6.3 | Future of ML and DL | Discussion of emerging technologies, research directions, and future possibilities. |
| Know your Mentor | Contact Number | Email Id | Teaching Experience (in Yrs.) |
| Ms. Ravinder Saini | 9478964586 | ravindersaini.cse@cumail.in | 9 Years |
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