Machine Learning with Python
Objective
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.
Duration of Course
8 weeks
Credit Weight
2 Credits
Certificate
The participants will be provided with a certificate upon successful completion of the course.
Career Advancement
Many top companies and startups, including Google, Netflix, Amazon, and Tesla, rely on Python for developing machine learning and AI models. This makes learning machine learning with Python highly valuable for career growth. Python’s versatility and ability to scale from simple models to complex production systems make it a go-to language for real-world applications. Starting as a Machine Learning Engineer or Data Scientist, professionals can progress to senior roles such as Lead AI Engineer, and Data Science Manager. Industries Hiring Machine Learning with Python Professionals: Finance, Healthcare, Retail and E-commerce, Technology, Telecommunications, Manufacturing etc.
Module - 1 Introduction to Machine Learning
- Introduction of Machine Learning
- Introduction of Artificial Intelligence
- Application of AI
- AI Problems
- Supervised Learning
- Unsupervised Learning
- Reinforcement Learning
- Algorithms, Models, Features, Labels
- Applications of ML and real-world examples
Module - 2 Basics of Python for Machine Learning
- Introduction to Python for ML
- Benefits of Python for ML
- Overview of NumPy and its core functions
- Overview of Pandas for data manipulation
Module - 3 Data Preprocessing in Machine Learning
- Handling missing values
- Noisy data
- Outliers
Module - 4 Dimensionality Reduction in ML
- Feature Selection
- Filters Methods
- Wrappers Methods
- Embedded Methods
-
Feature Extraction
- PCA (Principal Component Analysis)
Module - 5 Supervised Learning – Classification and Regression
- Training data
- Test data
- Introduction of Matplotlib
- Matplotlib in Machine Learning
- Logistic Regression
- k-Nearest Neighbors (k-NN)
- Decision Trees
- Accuracy
- Precision
- Confusion Matrix
- Linear Regression
- Random Forest
Module - 6 Model Tuning and evaluation
- Hyper parameter Tuning
- Bias and Variance Trade off
- Cross Validation
- Over fitting and Under fitting
Module - 7 Unsupervised Learning in ML
-
Clustering Algorithms
- K-Means
- Agglomerative
- Applications of Clustering
Module - 8 Project with Machine Learning
- Apply learned skill to a practical project using real world business data.
Learning Management System (LMS) Panel:
- Lifetime Access: Students can log in securely at any time.
Course Features
- Live Online Classes: Engage in real-time sessions with instructors
- Interactive Sessions: Participate in discussions and Q&A to enhance learning.
- Practical Sessions: Apply concepts through hands-on activities.
- Class Recordings: Access recordings of live classes for review at your convenience
- Self-Learning Videos: Benefit from pre-recorded videos to reinforce learning.
- Digital Course Materials: Receive a soft copy of all course content.
- Assignments: Complete practical assignments to apply your knowledge.
- Practice Assessments: Test your understanding with practice quizzes.
- Final Assessment: Evaluate your overall progress with a comprehensive exam.