Machine Learning A-Z™: Hands-On Python & R In Data Science

Course Description

Step into the world of AI with Machine Learning A-Z™, a comprehensive, hands-on course designed to teach you how to build powerful machine learning models using both Python and R. Created for beginners and intermediate learners alike, this course blends theory and real-world practice to give you a complete understanding of machine learning algorithms and their practical applications.

Whether you’re analyzing customer behavior, forecasting stock prices, or detecting fraud, this course equips you with the tools and skills to solve real problems using data.

Certification

Get certified upon course completion and showcase your machine learning skills on your resume, LinkedIn profile, or in job interviews. The certificate validates your ability to use Python and R for predictive analytics and data science.

Who This Course is for

• Beginners in data science, AI, or machine learning

• Business analysts or developers looking to enhance their analytics skills

• Python or R programmers wanting to break into AI

• Students preparing for machine learning or data science interviews

• Anyone eager to solve real-world problems using ML techniques

What You’ll Learn

• Mastering machine learning fundamentals: supervised and unsupervised learning

• Implementing algorithms in Python and R side-by-side

• Regression techniques: linear, polynomial, and logistic regression

• Classification algorithms: decision trees, K-NN, SVMs, Naive Bayes

• Clustering: K-Means and Hierarchical Clustering

• Model selection, evaluation, and performance metrics

• Feature scaling, encoding, and preprocessing techniques

• Applying dimensionality reduction (PCA, LDA)

• Building powerful predictive models and automating workflows

• Real-life case studies: marketing optimization, credit scoring, churn prediction, and more

Tools & Libraries Covered

Python:

• NumPy

• Pandas

• Matplotlib & Seaborn

• Scikit-learn

• TensorFlow (optional bonus modules)

R:

• caret

• ggplot2

• dplyr

• e1071

• randomForest

Real-World Projects Include

• Predicting house prices

• Customer segmentation for marketing

• Detecting fraudulent transactions

• Sales forecasting and optimization

• Credit risk scoring