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Intro to Deep Learning with Project in TensorFlow 2, Python

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

On this course, you’ll be taught superior linear regression approach course of and with this, you may be ready to construct any regression downside. Utilizing this you possibly can resolve real-world issues like buyer lifetime worth, predictive analytics, and so on.

What you’ll Be taught

· TensorFlow 2.x

· Google Colab

· Linear Regression

· Gradient Descent Algorithm

· Information Evaluation

· Regression

· Characteristic Engineering and Choice with Lasso Regression.

· Mannequin Analysis

All of the above-mentioned methods are defined in TensorFlow. On this course, you’ll work on the Project Buyer Income (Lifetime worth) Prediction utilizing Gradient Descent Algorithm

Drawback Assertion: A big baby training toy firm that sells instructional tablets and gaming techniques each on-line and in retail shops wished to analyze the shopper information. The purpose of the issue is to decide the next goal as proven under.

1. Information Evaluation & Pre-processing: Analyse buyer information and draw the insights w.r.t income and based mostly on the insights we’ll do information pre-processing. On this module, you’ll be taught the next.

1. Vital Information Evaluation

2. Multi-collinearity

3. Issue Evaluation

2. Characteristic Engineering:

1. Lasso Regression

2. Establish the optimum penalty issue.

3. Characteristic Choice

3. Pipeline Mannequin

4. Analysis

We’ll begin with the fundamentals of TensorFlow 2.x to superior methods in it. Then we drive into instinct behind linear regression and optimization perform like gradient descent.

Who this course is for:

  • Anybody who need to construct and practice their very own community
  • Curious of knowledge science
  • Who need to studying Deep Learning

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