IMDigisol

Machine Learning using Python

In this course, we will utilize Anaconda Python for a comprehensive programming environment. We will leverage GPU capabilities to accelerate computationally intensive algorithms, ensuring efficient processing. Additionally, we will employ GitHub for version control and collaboration, and Kaggle for accessing datasets and participating in data science competitions.

Week 3: Introduction to Machine Learning

• Theoretical background in Machine learning
• Feature Engineering and Selection
• Supervised/unsupervised learning
• Classifications
• Regression
• Clustering
• Visualization
• ML Project Setup
• Train/Validation/Test
• ETL (Extract Transform Load)
• Assignment

Week 4: Classification (Supervised)

• Supervised Machine learning
• Introduction to classification
• binary and multi-class classification
• one-vs-one and one-vs-all oversampling and undersampling
• Applications of classification
• Implementation of classification algorithms
• Assignment

Week 5: Regression

• Difference between regression and classification
• Linear regression
• Multi-linear regression
• Test/train (in the context of regression)
• Implementation
• Evaluating model
• Assignment

Week 7: Data Visualization

• Basic graphs
• Exploring Matplotlib and Seaborn
• EDA (Exploratory data analysis)
• Creating graphs in Python using Plotting libraries
• Real data visualization
• Assignment

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Machine Learning using Python

Week 1: Introduction to Python

Week 2: Basic Python Libraries w.r.t to Machine Learning

Week 3: Introduction to Machine Learning

Week 4: Classification (Supervised)

Week 5: Regression

Week 6: Clustering

Week 7: Data Visualization

Week 8: Student project Demonstration

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