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 configure a Keras model for training.

## A. Configuring for training</h2>

When it comes to configuring the model for training, Keras shines with its simplicity. A single call to the [compile](https://keras.io/models/sequential) function allows to set up all the training requirements for the model.

The function takes in a single required argument, which is the optimizer to use, e.g. [ADAM](https://arxiv.org/abs/1412.6980). A full list of optimizers can be found [here](https://keras.io/optimizers/). A shorthand way to specify the optimizer is to use a (lowercase) string of the optimizer's name (e.g. `adam`, `sgd`, `adagrad`.

The two main keyword arguments to know are `loss` and `metrics`. The `loss` keyword argument specifies the loss function to use. For binary classification, we set the value to `binary_crossentropy`, which is the binary cross-entropy function. For multiclass classification, we set the value to `categorical_crossentropy`, which is the multiclass cross-entropy function.

The `metrics` keyword argument takes in a list of strings, representing metrics we want to track during training and evaluation. For classification, we only need to track the model loss (which is tracked by default) and the classification accuracy.

# Chapter Goals:
- Learn how to configure a Keras model for training.

# A. Configuring for training</h2>

When it comes to configuring the model for training, Keras shines with its simplicity. A single call to the [compile](https://keras.io/models/sequential) function allows to set up all the training requirements for the model.

The function takes in a single required argument, which is the optimizer to use, e.g. [ADAM](https://arxiv.org/abs/1412.6980). A full list of optimizers can be found [here](https://keras.io/optimizers/). A shorthand way to specify the optimizer is to use a (lowercase) string of the optimizer's name (e.g. `adam`, `sgd`, `adagrad`.

The two main keyword arguments to know are `loss` and `metrics`. The `loss` keyword argument specifies the loss function to use. For binary classification, we set the value to `binary_crossentropy`, which is the binary cross-entropy function. For multiclass classification, we set the value to `categorical_crossentropy`, which is the multiclass cross-entropy function.

The `metrics` keyword argument takes in a list of strings, representing metrics we want to track during training and evaluation. For classification, we only need to track the model loss (which is tracked by default) and the classification accuracy.

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

Model Configuration

Chapter Goals:

A. Configuring for training