Intro to Deep Learning project in TensorFlow 2.x and Python

Course Title: Intro to Deep Learning Project in TensorFlow 2.x and Python

Course Headline: Advanced Implementation of Regression Modelling Techniques Like Lasso Regression in TensorFlow

Welcome to the Course Introduction to Deep Learning with TensorFlow 2.0:

What you will Learn

• 🎯 TensorFlow 2.x: Gain expertise in the latest version of TensorFlow, with its user-friendly and flexible features.
• 🚀 Google Colab: Utilize the powerful Google Colab environment for your deep learning projects, enabling you to work seamlessly without setting up complex local environments.
• 📊 Linear Regression: Understand the foundational linear regression technique and its importance in predictive analytics.
• 📈 Gradient Descent Algorithm: Dive into the heart of neural network optimization with the gradient descent algorithm, learning how it finds minima for loss functions.
• 💻 Data Analysis: Learn to analyze data to extract meaningful insights and inform your predictive modelling decisions.
• 🔢 Regression Techniques: Explore various regression techniques, focusing on Lasso Regression for feature selection and its role in model simplification.
• 🧠 Model Evaluation: Master the process of evaluating models to ensure they are performing as expected and can be trusted to make predictions on new data.

Course Project: Customer Revenue (Lifetime Value) Prediction using Gradient Descent Algorithm

Module Breakdown:

1. Data Analysis & Pre-processing: Analyze customer data to gain insights into revenue drivers. You'll learn how to handle issues like multi-collinearity and apply factor analysis to clean and prepare your dataset for modelling.

2. Feature Engineering: Implement Lasso Regression to identify the optimal penalty factor, perform feature selection, and understand the importance of each feature in predicting customer lifetime value.

3. Pipeline Model: Construct a robust pipeline model using TensorFlow 2.x, ensuring that your data flows through the model without any hiccups or unnecessary complexities.

4. Evaluation: Evaluate your model's performance rigorously, understanding various metrics and validation techniques to ensure your predictive model is reliable and ready for real-world deployment.

Join us on this insightful learning adventure and transform your data science skills with TensorFlow 2.x and deep learning! 🌟

Enroll Now and Turn Your Data into Predictive Power with Confidence! 🚀🎉