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Overview
This course offers a deep dive into the essential principles and algorithms of machine learning. It covers supervised and unsupervised learning, model evaluation, feature engineering, and regularization techniques. Students engage with practical coding exercises using Python and key libraries to implement algorithms such as SVMs, random forests, and neural networks. The course balances theoretical understanding with applied skills, preparing learners to tackle real-world machine learning problems effectively. It is suitable for beginners and intermediate learners looking to build a strong...
Syllabus
- Day 1: Introduction
- Day 2: Python Basics
- Day 3: Python Numpy and Pandas
- Day 4: Understanding the AI ML Environment
- Day 5: Math & Stats Behind ML (Basics)
- Day 6: Math & Stats Behind ML (Advanced)
- Day 7: ML Programming | With Concepts & Exploratory Data Analysis
- Day 8: ML Programming (Classification)
- Day 9: ML Programming (Naive Bayes & Decision Trees)
- Day 10: ML Programming (Unsupervised Learning)
- Day 11: Natural Language Processing
- Day 12: Deep Learning in NLP Part 1
- Day 13: Deep Learning in NLP Part 2
- Day 14: Build and Host ML Applications on the web
- Day 15: Building & Deploying an NLP Wordcloud as a web application
- Bonus Lecture
Instructor
Sai Acuity Institute of Learning Pvt Ltd Enabling Learning Through Insight!
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