This learning path provides a comprehensive introduction to Business Analytics using Python. It covers fundamental machine learning concepts such as supervised and unsupervised learning, regression, and classification. You will explore various algorithms including Linear Regression, Logistic Regression, Decision Trees, Random Forests, K-Means, and Density-Based Clustering. The path also delves into essential techniques like cross-validation, bootstrapping, ensemble learning (boosting and bagging), and statistical concepts such as hypothesis testing, p-value, and confidence intervals. Practical application with scikit-learn is integrated throughout the modules.

By the end of this learning path, you will be able to:

• Understand and differentiate between supervised and unsupervised learning.

• Apply regression and classification techniques using Python.

• Implement various machine learning algorithms such as Linear Regression, Logistic Regression, Decision Trees, Random Forests, K-Means, and Density-Based Clustering.

• Utilize essential techniques like cross-validation, bootstrapping, and ensemble learning.

• Interpret statistical concepts including hypothesis testing, p-value, and confidence intervals.

• Practically apply these concepts using the scikit-learn library in Python for business analytics.