PRDSML-B-7 Course Contents

This course contains the course contents of Hands-on Virtual Workshop on Python and R for Data Science and Machine Learning, Batch-7.

Course Outline

Introduction: An introduction to machine learning, supervised and unsupervised learning, the future of machine learning, and how to download secondary data from the Internet.

Python for Data Science & Machine Learning 

Environment Setup for Python: 1. Google Colab setup, 2. Google Colab interface overview, 3. Jupyter Notebook setup, 4. Jupyter Notebook interface overview.

Python Basics for Data Science & Machine Learning: 1. lists and tuples, 2. Flow control statements, 3. Functions.

Data Preprocessing in Python: 1. Importing dataset, 2. Data cleaning, 3. Uses of Pandas, Numpy, Matplotlib, and Scikit-learn libraries, 4. Encoding categorical data, 5. Splitting dataset, 6. Feature scaling.

Data Visualization Using Python: 1. Scatter plot, 2. Boxplot, 3. Histogram, 4. Line plot.

Building Some Useful Machine Learning Models Using Python: 1. Simple linear regression, 2. Multiple linear regression, 3. Logistic regression, 4. K-means clustering, 5. Principal component analysis.

R for Data Science & Machine Learning 

Environment Setup for R: 1. R Setup, 2. RStudio Setup, 3. RStudio interface overview.

Data Preprocessing in R: 1. Importing dataset, 2. Encoding categorical data, 3. Splitting dataset, 4. Feature scaling

Data Visualization Using R: 1. Scatter plot, 2. Boxplot, 3. Histogram, 4. Line plot.

Building Useful Machine Learning Models Using R: 1. Simple linear regression, 2. Multiple linear regression, 3. Decision tree regression, 4. Random Forest regression, 5. Naive Bayes, 6. Decision tree classification, 7. Random Forest classification, 8. Natural language processing, 9. Principal component analysis, 10. XGBoost