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Machine Learning for Software Engineers

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Before We Begin

About this Path About this Module

Are You Ready to Become a Machine Learning Engineer?

Data Manipulation with NumPy

Introduction NumPy Arrays NumPy Basics Math Random Indexing Filtering Statistics Aggregation Saving Data Quiz

Data Analysis with pandas

Data Preprocessing with scikit-learn

Introduction Standardizing Data Data Range Robust Scaling Normalizing Data Data Imputation PCA Labeled Data Quiz

Data Modeling with scikit-learn

Introduction Linear Regression Ridge Regression LASSO Regression Bayesian Regression Logistic Regression Decision Trees Training and Testing Cross-Validation Applying CV to Decision Trees Evaluating Models Exhaustive Tuning Quiz

Clustering with scikit-learn

Introduction Cosine Similarity Nearest Neighbors K-Means Clustering Hierarchical Clustering Mean Shift Clustering DBSCAN Evaluating Clusters Feature Clustering Quiz

Gradient Boosting with XGBoost

Introduction XGBoost Basics Cross-Validation Storing Boosters XGBoost Classifier XGBoost Regressor Feature Importance Hyperparameter Tuning Model Persistence Quiz

Deep Learning with TensorFlow

Introduction Model Initialization Logits Metrics Optimization Training Evaluation Linear Limitations Hidden Layer Multiclass Softmax Quiz

Deep Learning with Keras

Introduction Sequential Model Model Output Model Configuration Model Execution Quiz

Conclusion

Introduction

An overview of data analysis with pandas.

We'll cover the following

A. Data analysis
B. pandas
C. Matplotlib and pyplot

In this chapter, you'll use pandas to analyze Major League Baseball (MLB) data. The data comes courtesy of Sean Lahman and contains statistics for every player, manager, and team in MLB history. The full database can be found and downloaded here.