Deep Learning with TensorFlow 2.0 Course Syllabus

This course provides a hands-on introduction to deep learning with TensorFlow 2.0, tailored for beginners interested in applying machine learning to business intelligence. Over approximately 6 hours of structured content, you’ll progress through foundational concepts, practical modeling, and a final capstone project. Each module blends theory with real-world applications, ensuring you can derive actionable insights from data using AI tools.

Module 1: Introduction to Machine Learning in Business

Estimated time: 0.5 hours

• Overview of machine learning applications in business intelligence
• Role of TensorFlow in data-driven decision-making
• Introduction to Keras and deep learning for business outcomes

Module 2: Preparing Business Data for ML Models

Estimated time: 0.75 hours

• Data preprocessing and cleaning techniques
• Feature engineering for business datasets
• Exploratory Data Analysis (EDA) using Python tools

Module 3: Regression Analysis for Business Forecasting

Estimated time: 1 hour

• Linear regression models with Keras
• Logistic regression for business trend prediction
• Forecasting sales, revenue, and customer behavior

Module 4: Classification Models for Decision-Making

Estimated time: 1 hour

• Building classification models in TensorFlow
• Evaluating model performance
• Customer segmentation and churn prediction use cases

Module 5: Clustering and Unsupervised Learning

Estimated time: 0.75 hours

• Introduction to K-Means clustering
• Hierarchical clustering for business data
• Identifying hidden patterns in customers and products

Module 6: Final Project

Estimated time: 1.25 hours

• End-to-end machine learning project in business intelligence
• Deploying models to solve a real-world business challenge
• Presenting results and measuring ROI of ML applications

Prerequisites

• Basic understanding of Python programming
• Familiarity with fundamental data science concepts
• No prior deep learning experience required

What You'll Be Able to Do After

• Understand how machine learning enhances business intelligence
• Build and train ML models using TensorFlow and Keras
• Apply regression, classification, and clustering to business problems
• Analyze business datasets using AI-driven techniques
• Deploy machine learning solutions to improve KPIs and performance