Machine Learning With Python

The objective of this course is to work on industry-specific case studies and projects that reflect real-world challenges and solutions. Master advanced Python techniques and libraries for data manipulation, analysis, and visualisation tailored to industry needs.

Gain proficiency in using libraries and frameworks like Scikit-learn, PyTorch etc. for scalable and efficient data processing. Develop, train, and fine-tune machine learning models focusing on practical performance metrics relevant to industry scenarios.

Gain experience deploying models into production environments and integrating them with existing systems or applications. It provides the skills necessary to apply machine learning solutions effectively within a business context, address real-world problems, and drive value through data-driven decision-making.

Target Audience:

Students who have done Bachelor of Technology (CS/IT), Bachelor of Computer Applications (BCA), Bachelor of Science in Information and Technology (B.Sc IT), Master of Computer Applications (MCA), Master of Science in Computer Science (M.Sc CS), Master of Science in Information and Technology (M.Sc IT) and professionals seeking a future in IT Industry.

Syllabus:

• Module - 1   Introduction to Machine Learning
• Unit 1 : Overview of ML and AI
• Introduction of Machine Learning and Artificial Intelligence
• Application of AI and AI Problems
• Supervised Learning
• Unsupervised Learning
• Reinforcement Learning
• Unit 2 : Terminologies of ML
• Algorithms, Models, Features, Labels
• Applications of ML and real-world examples
• Module - 2   Basics of Python for Machine Learning
• Unit 1 : Basics of Machine Learning
• Benefits of Python for ML
• Unit 2 : Numpy and Pandas
• Overview of NumPy and its core functions
• Overview of Pandas for data manipulation
• Module - 3   Data Preprocessing in Machine Learning
• Unit 1 : Data Cleaning
• Handling Missing Values
• Handling Noisy Data
• Module - 4   Dimensionality Reduction in ML
• Unit 1 : Approaches to Dimensionality Reduction
• Feature Selection
• Filters Methods
• Wrappers Methods
• Embedded Methods
• Feature Extraction
• PCA (Principal Component Analysis)
• Module - 5   Supervised Learning – Classification and Regression
• Unit 1 : Data Splitting and Basic data visualization Tool/Library
• Training and Testing Data
• Matplotlib in Machine Learning
• Unit 2 : Evaluation Metrics
• Confusion Matrix, Accuracy, Precision
• Unit 3 : Introduction to Classification
• Logistic Regression
• k-Nearest Neighbors (k-NN)
• Decision Trees
• Unit 4 : Introduction to Regression
• Linear Regression
• Random Forest
• Module - 6   Model Tuning and evaluation
• Unit 1 : Model Selection and Tuning
• Hyper parameter Tuning
• Bias and Variance
• Unit 2 : Model evaluation
• Cross Validation
• Over fitting and Under fitting
• Module - 7   Unsupervised Learning in ML
• Unit 1 : Introduction to Unsupervised Learning
• Clustering Algorithms
• K-Means
• Agglomerative
• Applications of Clustering
• Module - 8   Project with Machine Learning
• Unit 1 : Application
• Apply learned skill to a practical project using real world business data.