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a-deep-dive-into-data-wrangling-with-python

Perform DataFrame operations in Pandas for a more in-depth look at data wrangling in practice

advanced-operations-on-python-data-structures

Explore more advanced data structures in Python and how to handle them with some basic file operations

advanced-rnns

This module covers the implementation of advanced RNN models that overcome the drawbacks of plain RNNs. We will particularly look at LSTM, GRU-based model, Bi-directional and Stacked RNNs.

advanced-web-scraping-and-data-gathering

Decode responses and extract text from the Request and BeautifulSoup libraries, read and scrape data from XML files, and implement regular expressions to practice advanced web scraping on APIs.

aggregate-and-window-functions

This module enables you summarize and identify the quality of the data using concepts such as aggregation and window functions.

an-introduction-to-data-visualization-and-data-exploration-with-python

Learn the fundamental concepts of data wrangling and statistics, and understand how they relate to data visualization.

analytics-using-complex-data-types

This module covers performing descriptive analytics on time series data, geospatial data, complex data types (arrays, JSON, and JSONB), and text.

analyzing-documents-and-text-with-natural-language-processing

In this module you will look at AWS AI services and examine an emerging computing paradigm – the Serverless Computing. We will then proceed to applying NLP and the Amazon Comprehend service to analyze documents.

analyzing-the-bank-marketing-dataset

Analyze marketing campaign data related to new financial products. Discover linear and logistic regression models, and explore the relationships between the different features in the data

analyzing-the-heart-disease-dataset

Identify missing values, outliers and trends in medical data. Create bar charts, heatmaps and other visualizations to understand how the features impact the target column of the data set

analyzing-the-sequence-of-data-with-rnns-using-pytorch

Understand the learning process of RNNs and discover the LSTM network architecture. Solve problems and perform Natural Language Processing using sequences of data

applications-in-business-use-cases

Fix, clean, merge, and connect new data to perform data wrangling tasks on UN and GDP data

applying-foundational-probability-concepts

This module covers probability theory and looks at how you can use NumPy and SciPy to solve probability problems.

autoencoders

This course will take a look at autoencoders and their applications will help you see how autoencoders are used in dimensionality reduction and denoising. You will implement an artificial neural network and an autoencoder using the Keras framework. By the end of this course, you will be able to implement an autoencoder model using convolutional neural networks.

becoming-pythonic

Discover what it means to be "Pythonic", learn to write succinct, readable expressions for creating lists; use Python comprehensions with lists, dictionaries, and sets.

beginning-calculus-with-python

This module covers derivatives and integrals and how Python can be used to perform basic calculus.

building-a-trained-model

This module will cover the key stages involved in building a comprehensive program. It also explains how to build and save a model such that you get the same results every time it is run and call a saved model to use it for predictions on unseen data.

building-an-artificial-intelligence-algorithm

Learn how to build a machine learning mode and get started on the popular deep learning framework PyTorch. You will delve into one of the most exciting fields in deep learning research - reinforcement learning - and take a closer look at the deep Q-learning algorithm

building-artificial-neural-networks-in-keras

Review the mathematics that comprise Artificial Neural Networks, apply linear transformations in Python, and build a logistic regression model with Keras

building-blocks-of-deep-learning

This module provides you with a good understanding what deep learning is and how programming with TensorFlow works

clustering

This module covers the concept of clustering in machine learning. It explains three of the most common clustering algorithms, with a hands-on approximation to solve a real-life data problem. The three clustering algorithms covered are k-means, mean-shift and DBSCAN algorithms.

clustering-fundamentals

This chapter will get you introduced to the fundamentals of Clustering which will be illustrated with two unsupervised learning algorithms. You will be implementing flat clustering with the k-means algorithm and hierarchical clustering with the mean shift algorithm. By the end of this chapter you will have a firm grasp on the basics of Clustering.

computer-vision-with-convolutional-neural-networks

Explore the architecture of CNNs and related techniques to build image processing applications and classify models with Keras

constructing-python-classes-and-methods

Discover classes in Python, one of the cornerstones of object-oriented programming. Also explore how to use methods in Python programming.

creating-ensemble-models-with-python

Recognize underfitting and overfitting, implement bagging and boosting, and build a stacked ensemble model using a number of classifiers.

creating-plots-with-matplotlib

Discover how to use Matplotlib to create visualizations using the built-in plots that are provided by the library. Customize your visualizations and write mathematical expressions using TeX.

cross-validation-and-keras-wrappers

Study the form and function of two major cross-validation methods, build a scikit learn interface, and use cross-validation to perform image classification and selection on example datasets

deep-learning-for-sequences

This module explores how important Recurrent Neural Networks (RNNs) are for sequence modeling. It particularly focuses on deep learning approaches for sequences, particularly plain RNNs and 1D convolutions Foundations more advanced RNN-based models are laid in this module

deep-learning-for-text-embeddings

This module demonstrates the power of word embeddings and explains the popular deep learning-based approaches for embeddings

deep-neural-networks-with-keras

Experiment with Neural Network architectures to build and evaluate both single and multi-layer sequential models in Keras

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