[40% OFF]Machine Learning for Software Engineers Coupon-Educative.io

Machine Learning for Software Engineers Coupon-Educative.io

With Machine Learning for Software Engineers you will get a 40% discount on yearly plans and a 20% monthly discount oneducative.io. It is one of the popular courses from educative.io

This course will teach you to write useful code and create impactful Machine Learning applications immediately. From the start, you’ll be given all the tools that you need to create industry-level machine learning projects.

Machine Learning for Software Engineers– Developer Discount

With the exclusive Holiday discount, you can get a 20% discount on two years of access to educative.io which includes all the existing and future courses. Two-year access is just $199 after the discount. Lockin this price before it expires.

Get an additional 20 discount on Handling Financial Services with Square API course

Coupon: Use code devops at checkout

Also, you can get a 10% discount on all educative courses using the exclusive discount.

Coupon: Use Code Educative10 at checkout

Here is what you will Learn from Machine Learning for Software Engineers

1. What you’ll learn from this course

  1. Overview

2. Data Manipulation with NumPy

  1. Introduction
  2. NumPy Arrays
  3. NumPy Basics
  4. Math
  5. Random

3. Data Analysis with pandas

  1. Introduction
  2. Series
  3. DataFrame
  4. Combining
  5. Indexing

4. Data Preprocessing with scikit-learn

  1. Introduction
  2. Standardizing Data
  3. Data Range
  4. Robust Scaling
  5. Normalizing Data

5. Data Modeling with scikit-learn

  1. Introduction
  2. Linear Regression
  3. Ridge Regression
  4. LASSO Regression
  5. Bayesian Regression

6. Clustering with scikit-learn

  1. Introduction
  2. Cosine Similarity
  3. Nearest Neighbors
  4. K-Means Clustering
  5. Hierarchical Clustering

7. Gradient Boosting with XGBoost

  1. Introduction
  2. XGBoost Basics
  3. Cross-Validation
  4. Storing Boosters
  5. XGBoost Classifier

8. Deep Learning with TensorFlow

  1. Introduction
  2. Model Initialization
  3. Logits
  4. Metrics
  5. Optimization

9. Deep Learning with Keras

  1. Introduction
  2. Sequential Model
  3. Model Output
  4. Model Configuration
  5. Model Execution
  6. Course Conclusion
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