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 feed data values into a neural network
- Understand how to run a neural network using input values
- Train the neural network on batched input data and labels

# A. Running the model
In this chapter and the next, you will be running your model on input data, using a <code><a href="https://www.tensorflow.org/api_docs/python/tf/compat/v1/Session" target="_blank">tf.compat.v1.Session</a></code> object and the <code>tf.compat.v1.placeholder</code> variables from the previous chapters.
The <code>tf.compat.v1.Session</code> object has an extremely important function called <code><a href="https://www.tensorflow.org/api_docs/python/tf/Session#run" target="_blank">run</a></code>. All the code written in the previous chapters was to build the computation graph of the neural network, i.e. its layers and operations. However, we can only train or evaluate the model on real input data using <code>run</code>. The function takes in a single required argument and a few keyword arguments.

# B. Using <code>run</code>
The required argument is normally either a single tensor/operation or a list/tuple of tensors and operations. Calling <code>run</code> on a tensor returns the value of that tensor after executing our computation graph. The output of <code>run</code> with a tensor input is a NumPy array.
The code below shows usages of <code>run</code> on <code><a href="https://www.tensorflow.org/api_docs/python/tf/constant" target="_blank">tf.constant</a></code> tensors.

Initialize and train a TensorFlow neural network using actual training data.

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

Training

Chapter Goals:

A. Running the model

B. Using `run`

Training

Chapter Goals:

A. Running the model

B. Using run

B. Using `run`