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>Convert logits to probabilities</li>
  <li>Obtain model predictions from probabilities</li>
  <li>Calculate prediction accuracy</li>
</ul>

<h2>A. Sigmoid</h2>
<p>As discussed in the previous chapter, our model outputs logits based on the input data. These logits represent real number mappings from probabilities. Therefore, if we had the inverse mapping we could obtain the original probabilities. Luckily, we have exactly that, in the form of the <a href="https://en.wikipedia.org/wiki/Sigmoid_function" target="_blank">sigmoid function</a>.</p>

<p>The above plot shows a sigmoid function graph. The <i>x</i>-axis represents logits, while the <i>y</i>-axis represents probabilities. </p>

> **Note** the horizontal asymptotes at <i>y = 0</i> and <i>y = 1</i>.

<p>Using the sigmoid function (<code>tf.math.sigmoid</code> in TensorFlow), we can extract probabilities from the logits. In binary classification, i.e. <code>output_size = 1</code>, this represents the probability the model gives to the input data being labeled 1. A probability closer to 0 or 1 means the model is pretty sure in its prediction, while a probability closer to 0.5 means the model is unsure (0.5 is equivalent to a random guess of the label's value).</p>

<h2>Chapter Goals:</h2>
<ul>
  <li>Convert logits to probabilities</li>
  <li>Obtain model predictions from probabilities</li>
  <li>Calculate prediction accuracy</li>
</ul>

Discover the most commonly used metrics for evaluating a neural network.

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

Metrics

Chapter Goals:

A. Sigmoid