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>Learn about labeled datasets</li>
  <li>Separate principle component data by class label</li>
</ul>

<h2>A. Class labels</h2>
<p>A big part of data science is classifying observations in a dataset into separate categories, or <i>classes</i>. A popular use case of data classification is in separating a dataset into "good" and "bad" categories. For example, we can classify a dataset of breast tumors as either malignant or benign.</p>
<p>The code below separates a breast cancer dataset into malignant and benign categories. The <code><a href="https://scikit-learn.org/stable/modules/generated/sklearn.datasets.load_breast_cancer.html#sklearn.datasets.load_breast_cancer" target="_blank">load_breast_cancer</a></code> function is part of the scikit-learn library, and its data comes from the <a href="https://archive.ics.uci.edu/ml/datasets/Breast+Cancer+Wisconsin+(Diagnostic)" target="_blank">Breast Cancer Wisconsin</a> dataset.

Separate the PCA components of a dataset by class.

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

Labeled Data

Chapter Goals:

A. Class labels