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Advanced implementation of regression modelling techniques like lasso regression in TensorFlow
What you'll learn:- TensorFlow 2.0
- Gradient Descent Algorithm
- Create Pipeline regression model in TensorFlow
- Lasso Regression
- Feature Selection with lasso
- Programming in TensorFlow 2.0
- Selection of Penalty factor lambda
- Visualizing graph in TensorBoard
- Neuron or Perceptron Model Architecture
- Loss or Cost Function
- TensorFlow Keras API
- Linear Regression
- Create customized model in TensorFlow
- Exploratory Data Analysis
- Data Preprocessing
- Multiple Linear Regression in TensorFlow
Welcome to the Course Introduction to Deep Learning with TensorFlow 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
· TensorFlow 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 TensorFlow. 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 Analysis
2. Multi-collinearity
3. Factor Analysis
2. Feature Engineering:
1. Lasso Regression
2. Identify the optimal penalty factor.
3. Feature Selection
3. Pipeline Model
4. Evaluation
We will start with the basics of TensorFlow 2.x to advanced techniques in it. Then we drive into intuition behind linear regression and optimization function like gradient descent.
Overview
