Machine Learning with Python

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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

Unit 1 : Overview of ML and AI
  • Introduction of Machine Learning
  • Introduction of Artificial Intelligence
  • Application of AI
  • 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
  • Introduction to Python for ML
  • 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
  • Noisy data
  • Outliers

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 data
  • Test data
  • Introduction of Matplotlib
  • Matplotlib in Machine Learning
Unit 2 : Introduction to Classification
  • Logistic Regression
  • k-Nearest Neighbors (k-NN)
  • Decision Trees
Unit 3 : Evaluation Metrics
  • Accuracy
  • Precision
  • Confusion Matrix
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 Trade off
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.

Learning Management System (LMS) Panel:

Course Features

Course Features

  • Duration 8 Weeks
  • Credit Weight 2 Credits
  • Certificate After Completion Yes
  • Course Fee with GST Rs. 4749/-
  • Lifetime Access Yes
  • Language English, Hindi
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