Basic programming knowledge
Understanding of basic statistics
Familiarity with business data concepts
Laptop with Python environment installed
Basic SQL knowledge
Course Deliverables
1. Understanding of ML fundamentals
2. Practical experience with Python ML libraries
3. Hands-on experience with real business cases
4. Best practices documentation
Day 1: Python Fundamentals and Data Preparation
Morning Session (8:00 AM - 12:00 PM)
Introduction to Python for Business Intelligence
o Python basics review
o Key libraries (NumPy, Pandas, Scikit-learn)
o Jupyter Notebook environment
o Best practices in code organization
Data Preparation and Analysis
o Loading and handling different data formats
o Data cleaning techniques
o Handling missing values
o Feature engineering basics
o Data transformation and normalization
o Hands-on Exercise: Preparing a business dataset
Afternoon Session (1:30 PM - 4:00 PM)
Exploratory Data Analysis
o Statistical analysis with Python
o Data visualization with Matplotlib and Seaborn
o Correlation analysis
o Outlier detection
o Data insights extraction
o Hands-on Exercise: Analyzing sales data
Day 2: Core Machine Learning Concepts
Morning Session (8:00 AM - 12:00 PM)
Introduction to Machine Learning
o Types of machine learning problems
o Supervised vs unsupervised learning
o Model selection principles
o Training and testing concepts
o Cross-validation techniques
Regression Models
o Linear regression
o Multiple regression
o Model evaluation metrics
o Hands-on Exercise: Sales forecasting model
Afternoon Session (1:30 PM - 4:00 PM)
Classification Models
o Logistic regression
o Decision trees
o Random forests
o Model evaluation (accuracy, precision, recall)
o Hands-on Exercise: Customer churn prediction
Day 3: Advanced Topics and Business Applications
Morning Session (8:00 AM - 12:00 PM)
Clustering and Segmentation
o K-means clustering
o Customer segmentation techniques
o Market basket analysis
o Hands-on Exercise: Customer segmentation project
Time Series Analysis
o Time series components
o Trend analysis
o Seasonality detection
o Basic forecasting methods
o Hands-on Exercise: Demand forecasting
Afternoon Session (1:30 PM - 4:00 PM)
Model Deployment and Integration
o Saving and loading models
o Basic API development
o Integration with BI tools
o Best practices for production
Final Project
o End-to-end ML project implementation
o Business case presentation
o Model evaluation and interpretation
o Implementation planning