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>Create a basic linear regression model based on input data and labels</li>
</ul>

<h2>A. What is linear regression?</h2>
<p>One of the main objectives in both machine learning and data science is finding an equation or distribution that best fits a given dataset. This is known as data modeling, where we create a model that uses the dataset's features as independent variables to predict output values for some dependent variable (with minimal error). However, it is incredibly difficult to find an optimal model for most datasets, given the amount of noise (i.e. random errors/fluctuations) in real world data.</p>
<p>Since finding an optimal model for a dataset is difficult, we instead try to find a good approximating distribution. In many cases, a linear model (a <a href="http://mathworld.wolfram.com/LinearCombination.html" target="_blank">linear combination</a> of the dataset's features) can approximate the data well. The term <i>linear regression</i> refers to using a linear model to represent the relationship between a set of independent variables and a dependent variable.</p>

<h2>Chapter Goals:</h2>
<ul>
  <li>Create a basic linear regression model based on input data and labels</li>
</ul>

Learn about basic linear regression and how it's used.

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

Linear Regression

Chapter Goals:

A. What is linear regression?