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

Welcome to the "**Become a Machine Learning Engineer**" Path. This Path will help you learn data-manipulation and pre-processing using **pandas** and **Numpy**, and model creation and deployment using **Keras** and **TensorFlow**. It will also teach you to create end-to-end industry-standard solutions using these techniques. If you are looking to start your career in the highly sought-after machine learning industry, then this is the right place for you, as this path will also help you prepare for job interviews.

## Prerequisites
In order to learn from this Path, you should be familiar with the following:
- Python programming
- Probability basics

> If you’re not familiar with any or all of these prerequisites, you can take a walk through the [Python for Programmers](https://www.educative.io/path/python-for-programmers) Path, which will help you learn them.

## What will we cover?
The Path is divided into *six* modules:


Welcome to the "**Become a Machine Learning Engineer**" Path. This Path will help you learn data-manipulation and pre-processing using **pandas** and **Numpy**, and model creation and deployment using **Keras** and **TensorFlow**. It will also teach you to create end-to-end industry-standard solutions using these techniques. If you are looking to start your career in the highly sought-after machine learning industry, then this is the right place for you, as this path will also help you prepare for job interviews.

# Prerequisites
In order to learn from this Path, you should be familiar with the following:
- Python programming
- Probability basics

> If you’re not familiar with any or all of these prerequisites, you can take a walk through the [Python for Programmers](https://www.educative.io/path/python-for-programmers) Path, which will help you learn them.

# What will we cover?
The Path is divided into *six* modules:


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 Path

Prerequisites