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 how to scale data without being affected by outliers</li>
</ul>

<h2>A. Data outliers</h2>
<p>An important aspect of data that we have to deal with is <i>outliers</i>. In general terms, an outlier is a data point that is significantly further away from the other data points. For example, if we had watermelons of weights 5, 4, 6, 7, and 20 pounds, the 20 pound watermelon is an outlier.</p>

<p>The data scaling methods from the previous two chapters are both affected by outliers. Data standardization uses each feature's mean and standard deviation, while ranged scaling uses the maximum and minimum feature values, meaning that they're both susceptible to being skewed by outlier values.</p>
<p>We can robustly scale the data, i.e. avoid being affected by outliers, by using use the data's median and <a href="https://en.wikipedia.org/wiki/Interquartile_range" target="_blank">Interquartile Range (IQR)</a>. Since the median and IQR are percentile measurements of the data (50% for median, 25% to 75% for the IQR), they are not affected by outliers. For the scaling method, we just subtract the median from each data value then scale to the IQR.</p>

<h2>Chapter Goals:</h2>
<ul>
  <li>Learn how to scale data without being affected by outliers</li>
</ul>

Understand how outliers can affect data and implement robust scaling.

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

Robust Scaling