Advanced Ml Topics & Ethical Considerations

This section explores Advanced Machine Learning topics such as Recurrent Neural Networks (RNNs) and Recommender Systems, alongside ethical considerations in their applications.

Sequence Models & Recommender Systems

This section covers the fundamentals and applications of Sequence Models, chiefly Recurrent Neural Networks, and Recommender Systems, highlighting key architectures like LSTMs and GRUs.

Recurrent Neural Networks (Rnns) For Sequential Data: Lstms, Grus (Conceptual Overview)

This section introduces Recurrent Neural Networks (RNNs), specifically focusing on Long Short-Term Memory (LSTM) networks and Gated Recurrent Units (GRUs), exploring their significance in handling sequential data.

The Core Idea Of Recurrent Neural Networks (Rnns)

Recurrent Neural Networks (RNNs) are specialized neural networks designed to handle sequential data by utilizing a hidden state to maintain memory of previous inputs.

Long Short-Term Memory (Lstm) Networks

LSTM networks are a special type of Recurrent Neural Network designed to overcome the vanishing gradient problem and effectively learn long-term dependencies in sequential data.

Gated Recurrent Units (Grus)

Gated Recurrent Units (GRUs) are a streamlined version of Long Short-Term Memory (LSTM) networks designed to improve computational efficiency while overcoming issues such as the vanishing gradient problem in recurrent neural networks (RNNs).

Applications In Nlp (Sentiment Analysis) & Time Series Forecasting (Conceptual)

This section explores the application of Recurrent Neural Networks (RNNs), specifically LSTMs and GRUs, in Natural Language Processing for sentiment analysis and in time series forecasting.

Natural Language Processing (Nlp) - Sentiment Analysis

This section discusses sentiment analysis as a key application of Natural Language Processing, leveraging Recurrent Neural Networks (RNNs) to interpret the sentiment of text based on word sequences and contextual understanding.

Time Series Forecasting (Conceptual)

This section covers the conceptual framework and relevance of time series forecasting using Recurrent Neural Networks (RNNs) for making predictions based on historical sequential data.

Association Rule Mining (Apriori Algorithm: Support, Confidence, Lift)

This section introduces Association Rule Mining and the Apriori Algorithm, focusing on key metrics like support, confidence, and lift to identify interesting relationships among data items.

Core Concepts: Items And Itemsets

This section introduces core concepts in association rule mining, defining items, itemsets, and transactions to evaluate relationships within large datasets.

Association Rules

Association rules are 'if-then' statements that identify relationships between items, widely used in market basket analysis.

Key Metrics For Evaluating Association Rules

This section covers the key metrics used to evaluate association rules in data mining, focusing on support, confidence, and lift.

The Apriori Algorithm (Conceptual Steps)

The Apriori algorithm efficiently discovers frequent itemsets in a dataset, leveraging the Apriori property to prune the search space and derive association rules.

Recommender Systems: Content-Based Vs. Collaborative Filtering (Conceptual)

This section explores the two main types of recommender systems: content-based and collaborative filtering, highlighting their mechanisms, advantages, and disadvantages.

Content-Based Recommender Systems

Content-based recommender systems suggest items to users based on the attributes of items they have previously liked.

Collaborative Filtering Recommender Systems

Collaborative filtering recommends items based on the preferences of similar users, utilizing past interactions to identify and predict user interests.

Basic Text Classification With Rnns, Or Implementing Apriori

This section introduces practical lab exercises that focus on understanding and implementing text classification using RNNs or applying the Apriori algorithm for Association Rule Mining.

Basic Text Classification With Recurrent Neural Networks (Conceptual Walkthrough)

The section outlines the conceptual steps for building a text classification model using Recurrent Neural Networks (RNNs), emphasizing data preprocessing, model construction, and evaluation.

Implementing Apriori Algorithm (Conceptual/pseudocode Walkthrough)

This section provides a conceptual and pseudocode-based overview of the Apriori algorithm, focusing on its application in Association Rule Mining and key metrics for evaluating rules.