Course Outcome

• Understand the mathematical and statistical foundations of machine learning.

• Work with data cleaning, preprocessing, and feature selection techniques.

• Build and evaluate machine learning models using real-world data.

• Implement algorithms such as regression, classification, clustering, and decision trees.

• Develop deep learning models using frameworks like TensorFlow and Keras.

• Analyze model performance using key metrics and fine-tune algorithms for better accuracy.

• Solve complex problems across domains using predictive modeling and pattern recognition.

• Create a professional portfolio of ML projects to showcase to employers or clients.

Course Duration

6 Months

Career Options

• Machine Learning Engineer

• Data Scientist

• AI Engineer

• Data Analyst

• Business Intelligence Developer

• Predictive Modeler

• Research Scientist

• Algorithm Engineer

• Quantitative Analyst

• Software Developer with ML focus