Delve into practical machine learning with NumPy, pandas, scikit-learn, and more. Gain insights into data analysis, feature engineering, and deep learning using industry-standard frameworks. Basic Python required.

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If you're a software engineer looking to add machine learning to your skillset, this is the place to start. 

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. Rather than reading through dense theory, you’ll learn practical skills and gain actionable insights. Topics covered include data analysis/visualization, feature engineering, supervised learning, unsupervised learning, and deep learning. All of these topics are taught using industry-standard frameworks: NumPy, pandas, scikit-learn, XGBoost, TensorFlow, and Keras.

Basic knowledge of Python is a prerequisite to this course.

This course was created by AdaptiLab, a company specializing in evaluating, sourcing, and upskilling enterprise machine learning talent. It is built in collaboration with industry machine learning experts from Google, Microsoft, Amazon, and Apple.

Machine Learning with NumPy, pandas, scikit-learn, and More

## Chapter Goals:

<ul>
  <li>Initialize an MLP model in Keras</li>
</ul>

<h2>A. Building the MLP</h2>
<p>In Keras, every neural network model is an instance of the <code><a href="https://keras.io/models/sequential/" target="_blank">Sequential</a></code> object. This acts as the container of the neural network, allowing us to build the model by stacking multiple layers inside the <code>Sequential</code> object.</p>
<p>The most commonly used Keras neural network layer is the <code><a href="https://keras.io/layers/core/#dense" target="_blank">Dense</a></code> layer. This represents a fully-connected layer in the neural network, and it is the most important building block of an MLP model.</p>
<p>When building a model, we start off by initializing a <code>Sequential</code> object. We can initialize an empty <code>Sequential</code> object and add layers onto the model using the <code>add</code> function, or we can directly initialize the <code>Sequential</code> object with a list of layers.</p>

# Chapter Goals:

<ul>
  <li>Initialize an MLP model in Keras</li>
</ul>

Learn how a neural network is built in Keras.

What you'll learn from this course

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

Sequential Model

Chapter Goals:

A. Building the MLP