This course equips learners with practical data analysis and machine learning with pandas and other frameworks such as NumPy and TensorFlow.

ml.tar.gz

mnist-fashion

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>Learn about multiclass classification</li>
  <li>Understand the purpose of multiple hidden layers</li>
  <li>Learn the pros and cons of adding hidden layers</li>
</ul>

<h2>Chapter Goals:</h2>
<ul>
  <li>Learn about multiclass classification</li>
  <li>Understand the purpose of multiple hidden layers</li>
  <li>Learn the pros and cons of adding hidden layers</li>
</ul>

Understand the differences between binary and multiclass classification.

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

Multiclass

Chapter Goals: