Training on Data Science and Machine Learning

Module 1: Introduction to Data Science and Python for Data Analysis

• Overview of data science concepts and business applications
• Python fundamentals for data analysis
• Working with Jupyter Notebooks
• Data manipulation using Pandas
• Data visualization with Matplotlib and Seaborn
• Case Study: Using data science to uncover performance trends in a business dataset
• Practical Exercise: Load, clean, and visualize a sample dataset using Python

Module 2: Data Preprocessing and Exploration

• Data cleaning and handling missing values
• Feature engineering and selection
• Data normalization and scaling techniques
• Exploratory Data Analysis (EDA)
• Handling categorical and time-series data
• Case Study: Preparing raw operational data for predictive modeling
• Practical Exercise: Perform EDA and feature preparation on a real-world dataset

Module 3: Supervised Learning – Regression and Classification

• Fundamentals of supervised learning
• Linear and logistic regression models
• Decision trees and random forests
• Support Vector Machines (SVM)
• Model evaluation metrics and validation
• Case Study: Predicting customer behavior using classification models
• Practical Exercise: Build and evaluate a regression and classification model

Module 4: Unsupervised Learning – Clustering and Dimensionality Reduction

• Principles of unsupervised learning
• K-Means and hierarchical clustering
• Principal Component Analysis (PCA)
• Anomaly detection techniques
• Applications in segmentation and pattern discovery
• Case Study: Customer segmentation using clustering techniques
• Practical Exercise: Apply clustering and PCA to identify hidden data patterns

Module 5: Deep Learning and Model Deployment

• Introduction to neural networks and deep learning concepts
• Using TensorFlow and Keras
• Building and training simple neural networks
• Overfitting, regularization, and hyperparameter tuning
• Model deployment using Flask and Docker
• Case Study: Deploying a machine learning model into a production environment
• Practical Exercise: Train a neural network and deploy a basic ML model