This course equips learners with practical data analysis and machine learning with pandas and other frameworks such as NumPy and TensorFlow.

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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

Additional hidden layers allow the model to learn more complex decision boundaries

Using more hidden layers will speed up model training

A neural network with only a single hidden layer is more computationally expensive

Using more hidden layers helps regularize the neural network

What is a benefit to using more hidden layers in a neural network?

What is a benefit to using more hidden layers in a neural network?

The sigmoid function converts the model's output into a real number

The sigmoid function extracts a bounded absolute value from the model's output

The sigmoid function converts the model's output into a probability

The sigmoid function is faster to calculate than other functions

Why do we use the sigmoid function for binary classification?

Why do we use the sigmoid function for binary classification?

A model is trained until its weights converge, at which point training is halted

A model is trained until it reaches 99% accuracy on the training set, but performs poorly on the test set

Which of the following is an example of overfitting?

Which of the following is an example of overfitting?

A single layer (i.e. no hidden layers) neural network can learn non-linear decision boundaries

The weights of a neural network can be either positive or negative

Cross entropy loss is the only loss function used to train neural networks

Logits are the output of a neural network, with values strictly between 0 and 1

Which of the following statements is true?

Which of the following statements is true?

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

Quiz