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>Learn about the XGBoost data matrix</li>
  <li>Train a <code>Booster</code> object in XGBoost</li>
</ul>

<h2>A. Basic data structures</h2>
<p>The basic data structure for XGBoost is the <code><a href="https://xgboost.readthedocs.io/en/latest/python/python_api.html#xgboost.DMatrix" target="_blank">DMatrix</a></code>, which represents a data matrix. The <code>DMatrix</code> can be constructed from NumPy arrays.</p>
<p>The code below creates <code>DMatrix</code> objects with and without labels.</p>

data = np.array([
  [1.2, 3.3, 1.4],
  [5.1, 2.2, 6.6]])

import xgboost as xgb
dmat1 = xgb.DMatrix(data)

labels = np.array([0, 1])
dmat2 = xgb.DMatrix(data, label=labels)

<p>The <code>DMatrix</code> object can be used for training and using a <code><a href="https://xgboost.readthedocs.io/en/latest/python/python_api.html#xgboost.Booster" target="_blank">Booster</a></code> object, which represents the gradient boosted decision tree. The <code><a href="https://xgboost.readthedocs.io/en/latest/python/python_api.html#xgboost.train" target="_blank">train</a></code> function in XGBoost lets us train a <code>Booster</code> with a specified set of parameters.</p>
<p>The code below trains a <code>Booster</code> object using a predefined labeled dataset.</p>

<h2>Chapter Goals:</h2>
<ul>
  <li>Learn about the XGBoost data matrix</li>
  <li>Train a <code>Booster</code> object in XGBoost</li>
</ul>

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

XGBoost Basics

Chapter Goals:

A. Basic data structures