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

## Who is this module for?
This module is designed for anyone who wants to become familiar with the basics of machine learning with data analysis and algorithm selection through job-focused lessons and hands-on practice.

## Module structure
This module contains *eight* main chapters, namely:

1. __Data Manipulation with NumPy__ teaches us the basics of _NumPy_ and how it's used for data manipulation.
2. __Data Analysis with pandas__ teaches us the basics of _pandas_ and its use in data analysis.
3. __Data Preprocessing with scikit-learn__ teaches us the techniques of data preprocessing using _scikit-learn_.
4. __Data Modeling with scikit-learn__ teaches us the regressions and decision trees data modeling techniques using _scikit-learn_.
5. __Clustering with scikit-learn__ teaches us to use clustering algorithms in _scikit-learn_.
6. __Gradient Boosting with XGBoost__ teaches us the basics of _XGBoost_ and their application in gradient boosting.
7. __Deep Learning with TensorFlow__ teaches us an end-to-end deep-learning model application using _TensorFlow_.
8. __Deep Learning with Keras__ teaches us to use an end-to-end deep-learning model application using _Keras_.

# Who is this module for?
This module is designed for anyone who wants to become familiar with the basics of machine learning with data analysis and algorithm selection through job-focused lessons and hands-on practice.

# Module structure
This module contains *eight* main chapters, namely:

1. __Data Manipulation with NumPy__ teaches us the basics of _NumPy_ and how it's used for data manipulation.
2. __Data Analysis with pandas__ teaches us the basics of _pandas_ and its use in data analysis.
3. __Data Preprocessing with scikit-learn__ teaches us the techniques of data preprocessing using _scikit-learn_.
4. __Data Modeling with scikit-learn__ teaches us the regressions and decision trees data modeling techniques using _scikit-learn_.
5. __Clustering with scikit-learn__ teaches us to use clustering algorithms in _scikit-learn_.
6. __Gradient Boosting with XGBoost__ teaches us the basics of _XGBoost_ and their application in gradient boosting.
7. __Deep Learning with TensorFlow__ teaches us an end-to-end deep-learning model application using _TensorFlow_.
8. __Deep Learning with Keras__ teaches us to use an end-to-end deep-learning model application using _Keras_.

This lesson discusses the intended audience and the necessary prerequisites of the module.

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

About this Module

Who is this module for?

Module structure