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- Understand how supervised and unsupervised machine learning methods can be used to construct and implement a text classifier in Python.
- Learn how to collect, clean and categorize data for your projects. Then extract the key textual features and present them visually.
- Understand what vectors are, and how they can be used to compare the frequencies of words and similar documents, and group them accordingly.
- Discover the basic tools and techniques required to preprocess data to use in an NLP project.
- Explore the libraries and frameworks used to perform sentiment analysis on textual data.
- Learn about various Topic Modeling algorithms, and how to apply them to datasets. Compare the strengths of different algorithms with some practical challenges.
- Generate and paraphrase text using different models for use in Python. Understand the applications and challenges of text summarization models.
- Collect data by scraping web pages, then analyze your findings. Learn how to use APIs to retrieve real-time data from Twitter.
- This module covers handling new data and creating a model that can learn continuously from the patterns and help make better predictions.
- This module covers the basics components of a neural network and its essential operations. It also explores a trained neural network created using TensorFlow
- This module discusses the current state of reinforcement learning and describes some promising approaches being taken to advance the field.
- This module introduces the architecture of CNN and explains how to implement it to develop image classifiers from scratch
Neural-Networks
PublicThis module will introduce you to Artificial Neural Networks and a practical approach to build single and multilayer neural networks to solve supervised learnin…Advanced-RNNs
PublicThis 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-d…- This module demonstrates the power of word embeddings and explains the popular deep learning-based approaches for embeddings
- The module covers the theory behind reinforcement learning and introduces Markov chains and Markov Decision Processes
- In this module you will learn about Generative Adversarial Networks (GAN) and their basic components along with some of the use cases of GAN.
- This chapter introduces you to two types of supervised learning algorithms in detail. The first algorithm will help us to classify data points using decision tr…
- This module introduces you to the fundamentals of Artificial Intelligence. You will be implementing your first AI through a simple Tic-Tac-Toe game where you wi…
- In this chapter you will be introduced to the final topic on neural networks and deep learning. You will come across TensorFlow, Convolutional Neural Networks (…
- This module provides you with a good understanding what deep learning is and how programming with TensorFlow works
- This module looks at policy based methods of reinforcement learning, principally the drawbacks to value based methods like Q learning that motivate the use of p…
- This module explores how important Recurrent Neural Networks (RNNs) are for sequence modeling. It particularly focuses on deep learning approaches for sequences…
- This module discusses the motivation for evolutionary strategies, and breaks down the components of genetic algorithms and how they can be tailored for reinforc…
Clustering-Fundamentals
PublicThis chapter will get you introduced to the fundamentals of Clustering which will be illustrated with two unsupervised learning algorithms. You will be implemen…- This module introduces classification — you will be implementing the various techniques such as k-nearest neighbors and Support Vector Machines. You will be usi…
- In this module you will be introduced to regression which plays an important role while it comes to prediction of the future by using the past historical data. …
- Explore basic machine learning algorithms and learn to build, train, and evaluate Artificial Neural Networks in Keras.
- Briefly review the foundational components of data wrangling and Python data structures.