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

<h2 style="font-size:1.1em">Chapter Goals:</h2>
<ul>
  <li>Add a hidden layer to the model's layers</li>
  <li>Understand the purpose of non-linear activations</li>
  <li>Learn about the ReLU activation function</li>
</ul>

<h2>A. Why a single layer is limited</h2>
<p>In the previous chapter we saw that the single layer perceptron was unable to classify points as being inside or outside a circle centered around the origin. This was because the output of the model (the logits) only had connections directly from the input features.</p>
<p>Why exactly is this a limitation? Think about how the connections work in a single layer neural network. The neurons in the output layer have a connection coming in from each of the neurons in the input layer, but the connection weights are all just real numbers.</p>

<p>Based on the diagram, the logits can be calculated as a linear combination of the input layer and weights:</p>

$$logits = w_1 \cdot x + w_2 \cdot y + w_3$$

<h2>Chapter Goals:</h2>
<ul>
  <li>Add a hidden layer to the model's layers</li>
  <li>Understand the purpose of non-linear activations</li>
  <li>Learn about the ReLU activation function</li>
</ul>

An overview of the limitations of a single layer perceptron model.

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

Hidden Layer

Chapter Goals:

A. Why a single layer is limited