Data Science and Machine Learning with Python
This course is designed to provide students with the knowledge and skills needed to perform data science using Python programming language. Students will learn to apply machine learning libraries such as Pandas, Seaborn and scikit-learn. The course will cover topics such as data cleaning, exploratory data analysis, machine learning algorithms, model evaluation, and selecting the appropriate model for business use.
Description
Course Content
- Introduction to Data Science in Python
- Overview of data science
- Introduction to Python for data science
- Exploratory Data Analysis and Feature Selection
- Data visualization techniques
- Data exploration with Pandas and Matplotlib
- What are features and how are they important
- Introduction to Machine Learning in Python
- Overview of machine learning
- Introduction to Python for machine learning
- Introduction to Google Colab Notebook
- Machine Learning Basics
- Introduction of Overfitting and Underfitting
- Class Imbalance and its solution
- Download the dataset from Kaggle
- Data splitting and data preparation for the Machine learning model
- Machine learning Basics and Types
- Supervised, Unsupervised, Reinforcement learning with example
- Supervised Learning Algorithms
- Linear regression
- Logistic regression
- Decision trees and random forests
- Project: Wine Quality Predictions
- Model Evaluation and Selection
- Model evaluation metrics
- Cross-validation
- Model selection techniques
- Ensemble Model
- Model save and load
- Real-world Examples
- Some real-life business applications
- Q&A and course feedback