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

It helps the model take into account each of the outlier points in the dataset

It lets the model use larger weight values, which improves the model's performance

It prevents the model from being too sensitive to random errors or outliers in the dataset

Why is regularization important in regression modeling?

Why is regularization important in regression modeling?

It provides an additional evaluation metric on the test set

It gives us a better evaluation of the model without needing a third dataset split

It gives us the best possible split for the training and testing sets

It automatically chooses the best hyperparameters for our model

What is the purpose of cross-validation?

What is logistic regression used for?

The number of subsets of the training set used to train the model each iteration

The number of total subsets that the training set is split into

The number of subsets of the training set used to validate the model each iteration

The number of total subsets that the test set is split into

What do the number of folds in a K-fold cross-validation represent?

What do the number of folds in a K-fold cross-validation represent?

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