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:
* Learn how to aggregate data in NumPy
* Write code to obtain sums and concatenations of NumPy arrays

# A. Summation
As we know how to calculate the sum of individual values between multiple arrays. To sum the values within a single array, we use the <a href="https://docs.scipy.org/doc/numpy/reference/generated/numpy.sum.html" target="_blank">np.sum</a> function.

The function takes in a NumPy array as its required argument, and uses the `axis` keyword argument. If the `axis` keyword argument is not specified, `np.sum` returns the overall sum of the array.
The code below shows how to use `np.sum`

arr = np.array([[0, 72, 3],
                [1, 3, -60],
                [-3, -2, 4]])
print(np.sum(arr))
print(repr(np.sum(arr, axis=0)))
print(repr(np.sum(arr, axis=1)))

In addition to regular sums, NumPy can perform cumulative sums using <a href="https://docs.scipy.org/doc/numpy/reference/generated/numpy.cumsum.html" target="_blank">np.cumsum</a>. Like `np.sum`, `np.cumsum` also takes in a NumPy array as a required argument and uses the `axis` argument. If the `axis` keyword argument is not specified, `np.cumsum` will return the cumulative sums for the flattened array.
The code below shows how to use `np.cumsum`. For a 2-D NumPy array, setting `axis=0` returns an array with cumulative sums across each column, while `axis=1` returns the array with cumulative sums across each row. Not setting `axis` returns a cumulative sum across all the values of the flattened array.

Use aggregation techniques to combine NumPy data and arrays.

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

Aggregation

Chapter Goals:

A. Summation