Explore the basics of machine learning with data analysis and algorithm selection through job-focused lessons and hands-on practice.

Become a Machine Learning Engineer

Start your journey toward becoming an ML engineer by learning practical ML skills such as data manipulation, data analysis, data preprocessing, data modeling, clustering, gradient boosting and deep learning. Basic knowledge about Python is a prerequisite. All topics are taught with industry-standard python frameworks: NumPy, pandas, scikit-learn, XGBoost, TensorFlow, and Keras.

Machine Learning for Software Engineers

What type of model does XGBoost use?

They're much faster than scikit-learn models

They're much more accurate than scikit-learn models

They're easier to use than scikit-learn models

There is no benefit, it's a matter of personal preference

What is the main benefit of using XGBoost models over scikit-learn models?

What is the main benefit of using XGBoost models over scikit-learn models?

Using the <code>dump</code> function of the <code>joblib</code> module

Using the <code>save_model</code> function for Booster objects

Both A and B are viable (Correct)

How do we save models in XGBoost?

The F1 score of each data feature in making model predictions

The magnitude of values for each data feature

The number of non-zero values for each data feature

The relative importance of each data feature in making model predictions

What does the <code>feature_importances_</code> property of an XGBoost model tell us?

What does the <code>feature_importances_</code> property of an XGBoost model tell us?

Before We Begin

Are You Ready to Become a Machine Learning Engineer?

Data Manipulation with NumPy

Data Analysis with pandas

Data Preprocessing with scikit-learn

Data Modeling with scikit-learn

Clustering with scikit-learn

Gradient Boosting with XGBoost

Deep Learning with TensorFlow

Deep Learning with Keras

Conclusion

Quiz