Predictive Modeling with TensorFlow focuses on equipping participants with the necessary skills to develop robust predictive models using one of the leading frameworks in machine learning. This course is structured around hands-on projects that enable learners to apply theoretical concepts in practical scenarios, ultimately enhancing their proficiency in data analytics and model development. Participants will engage in collaborative exercises, culminating in a final project that showcases their ability to implement predictive modeling techniques effectively.
The curriculum delves into various aspects of predictive analytics, including data preprocessing, model selection, and evaluation metrics. By the end of the course, learners will have a comprehensive understanding of how to leverage TensorFlow for real-world applications. Participants are encouraged to publish their project results in Cademix Magazine, providing an opportunity for visibility and professional recognition. This course is designed for those who are keen on advancing their careers in data science and analytics.
Introduction to Predictive Modeling and its Applications
Overview of TensorFlow: Installation and Environment Setup
Data Preprocessing Techniques for Model Readiness
Exploratory Data Analysis (EDA) with Python Libraries
Understanding Linear Regression and its Implementation in TensorFlow
Advanced Regression Techniques: Decision Trees and Random Forests
Introduction to Neural Networks: Architecture and Functionality
Building and Training Deep Learning Models with TensorFlow
Model Evaluation: Metrics and Techniques for Performance Assessment
Final Project: Developing a Predictive Model for a Real-World Dataset