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

 Buy Now
  

Price: 19.99$

Welcome to the Course Introduction to Deep Learning with Tensor Flow 2.0: In this course, you will learn advanced linear regression technique process and with this, you can be able to build any regression problem. Using this you can solve real-world problems like customer lifetime value, predictive analytics, etc. What you will Learn· Tensor Flow 2. x· Google Colab· Linear Regression· Gradient Descent Algorithm· Data Analysis· Regression· Feature Engineering and Selection with Lasso Regression.· Model Evaluation All the above-mentioned techniques are explained in Tensor Flow. In this course, you will work on the Project Customer Revenue (Lifetime value) Prediction using Gradient Descent Algorithm Problem Statement: A large child education toy company that sells educational tablets and gaming systems both online and in retail stores wanted to analyze the customer data. The goal of the problem is to determine the following objective as shown below.1. Data Analysis & Pre-processing: Analyse customer data and draw the insights w. r. t revenue and based on the insights we will do data pre-processing. In this module, you will learn the following.1. Necessary Data Analysis2. Multi-collinearity3. Factor Analysis2. Feature Engineering:1. Lasso Regression2. Identify the optimal penalty factor.3. Feature Selection3. Pipeline Model4. Evaluation We will start with the basics of Tensor Flow 2. x to advanced techniques in it. Then we drive into intuition behind linear regression and optimization function like gradient descent.