Programme Parameters
Foundation
3 Weeks (30 Hrs)
Online (e-LMS)
• No prior experience required — basic computer literacy is sufficient.
• A laptop or desktop with a stable internet connection.
• Willingness to complete assignments and the capstone project.
About This Course
Machine Learning & AI Fundamentals is a clear, broad foundation in how modern AI works. You learn the core ideas — what machine learning is, supervised, unsupervised and reinforcement learning, how models learn from data, and how they are evaluated — along with a grounded sense of what AI can and cannot do. The course keeps things conceptual and accessible, building the vocabulary and mental model you need before going deeper or applying AI. You finish with a solid foundation in ML and AI fundamentals. A verified e-Certificate of competency and e-Marksheet from the Deep Science & Technology Consortium.
🎯 Program Aim
This foundational course covers machine learning and AI fundamentals — the core concepts, main types of learning and how AI systems work, for a solid conceptual start.
📋 Course Objectives
1. Explain what machine learning and AI are.
2. Distinguish supervised, unsupervised and reinforcement learning.
3. Understand how models learn from data.
4. Grasp how models are evaluated.
5. Judge what AI can and cannot do.
👥 Who Should Enroll?
• Complete beginners to AI
• Professionals starting their AI journey
• Students across disciplines
• Anyone wanting the fundamentals
🚀 Key Learning Outcomes
• A solid foundation in ML and AI.
• The vocabulary to go deeper.
• A springboard to applied AI.
• A verified e-Certificate of competency and e-Marksheet from the Deep Science & Technology Consortium.
💎 What You'll Gain
Live & Recorded Sessions
Lifetime access to class recordingse-Certificate on Completion
Cryptographically verified credentialPost-Programme Support
Direct access to mentors & councilHands-On Experience
Notebooks, real-world code & datasetsCurriculum Outline
AI Fundamentals, Mathematics, and Machine Learning Foundations
Develop a comprehensive understanding of the mathematical prerequisites for machine learning, including linear algebra and calculus • Analyze the fundamental concepts of artificial intelligence, including machine learning, deep learning, and neural networks • Design a basic machine learning model using a supervised learning approach, including data preprocessing and feature selection
Data Engineering, Preprocessing, and Feature Pipelines
Configure a data pipeline using Apache Beam, including data ingestion, processing, and storage • Implement data preprocessing techniques, including handling missing values, data normalization, and feature scaling • Evaluate the effectiveness of different feature engineering techniques, including feature extraction and selection
Model Architecture, Algorithm Design, and Machine Learning Methods
Design a convolutional neural network (CNN) architecture for image classification, including convolutional and pooling layers • Develop a recurrent neural network (RNN) model for natural language processing, including long short-term memory (LSTM) and gated recurrent units (GRU) • Analyze the performance of different machine learning algorithms, including support vector machines (SVM) and k-nearest neighbors (KNN)
Training, Hyperparameter Optimization, and Evaluation
Implement a grid search algorithm for hyperparameter tuning, including learning rate and batch size optimization • Evaluate the performance of a machine learning model using metrics, including accuracy, precision, and recall • Develop a strategy for handling overfitting and underfitting, including regularization and early stopping
Deployment, MLOps, and Production Workflows
Configure a machine learning model for deployment using Docker, including containerization and orchestration • Implement a continuous integration and continuous deployment (CI/CD) pipeline using Jenkins, including automated testing and deployment • Develop a monitoring and logging strategy for machine learning models in production, including metrics and alerts
Ethics, Bias Mitigation, and Responsible AI Practices
Analyze the ethical implications of machine learning, including bias, fairness, and transparency • Develop a strategy for mitigating bias in machine learning models, including data preprocessing and feature selection • Evaluate the effectiveness of different techniques for ensuring fairness and accountability in AI systems
Industry Integration, Business Applications, and Case Studies
Develop a machine learning solution for a real-world business problem, including data collection and preprocessing • Implement a machine learning model for predictive maintenance, including sensor data analysis and anomaly detection • Evaluate the effectiveness of different machine learning algorithms for recommender systems, including collaborative filtering and content-based filtering
Technical Specifications
| Parameter | Requirement |
|---|---|
| Covered Tool / Platform | Python |
| Covered Tool / Platform | TensorFlow |
| Covered Tool / Platform | Keras |
| Covered Tool / Platform | scikit-learn |
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