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Reading list

6 Steps of Machine learning Lifecycle Introduction to Predictive Modeling
Introduction to Exploratory Data Analysis & Data Insights Descriptive Statistics Inferential Statistics How to Understand Population Distributions?
Reading Data Files into Python Different Variable Datatypes
Probability for Data Science Basic Concepts of Probability Axioms of Probability Conditional Probability
Central Tendencies for Continuous Variables Spread of Data KDE plots for Continuous Variable Overview of Distribution for Continuous variables Normal Distribution Skewed Distribution Skeweness and Kurtosis Distribution for Continuous Variable
Central Tendencies for Categorical Variables Understanding Discrete Distributions Performing EDA on Categorical Variables
Dealing with Missing Values Understanding Outliers Identifying Outliers in Data Outlier Detection in Python Outliers Detection Using IQR, Z-score, LOF and DBSCAN
Sample and Population Central Limit Theorem Confidence Interval and Margin of Error
Bivariate Analysis Introduction
Covariance Pearson Correlation Spearman's Correlation & Kendall's Tau Correlation versus Causation Tabular and Graphical methods for Bivariate Analysis Performing Bivariate Analysis on Continuous-Continuous Variables
Tabular and Graphical methods for Continuous-Categorical Variables Introduction to Hypothesis Testing P-value Two sample Z-test T-test T-test vs Z-test Performing Bivariate Analysis on Continuous-Catagorical variables
Chi-Squares Test Bivariate Analysis on Categorical Categorical Variables
Multivariate Analysis A Comprehensive Guide to Data Exploration The Data Science behind IPL
Supervised Learning vs Unsupervised Learning Reinforcement Learning Generative and Descriminative Models Parametric and Non Parametric model
Machine Learning Pipeline Preparing Dataset Build a Benchmark Model: Regression Build a Benchmark Model: Classification
Evaluation Metrics for Machine Learning Everyone should know Confusion Matrix Accuracy Precision and Recall AUC-ROC Log Loss R2 and Adjusted R2
Dealing with Missing Values Replacing Missing Values Imputing Missing Values in Data Working with Categorical Variables Working with Outliers Preprocessing Data for Model Building
Understanding Cost Function Understanding Gradient Descent Math Behind Gradient Descent Assumptions of Linear Regression Implement Linear Regression from Scratch Train Linear Regression in Python Implementing Linear Regression in R Diagnosing Residual Plots in Linear Regression Models Generalized Linear Models Introduction to Logistic Regression Odds Ratio Implementing Logistic Regression from Scratch Introduction to Scikit-learn in Python Train Logistic Regression in python Multiclass using Logistic Regression How to use Multinomial and Ordinal Logistic Regression in R ? Challenges with Linear Regression Introduction to Regularisation Implementing Regularisation Ridge Regression Lasso Regression
Introduction to K Nearest Neighbours Determining the Right Value of K in KNN Implement KNN from Scratch Implement KNN in Python
Bias Variance Tradeoff Introduction to Overfitting and Underfitting Visualizing Overfitting and Underfitting Selecting the Right Model What is Validation? Hold-Out Validation Understanding K Fold Cross Validation
Introduction to Feature Selection Feature Selection Algorithms Missing Value Ratio Low Variance Filter High Correlation Filter Backward Feature Elimination Forward Feature Selection Implement Feature Selection in Python Implement Feature Selection in R
Introduction to Decision Tree Purity in Decision Tree Terminologies Related to Decision Tree How to Select Best Split Point in Decision Tree? Chi-Squares Information Gain Reduction in Variance Optimizing Performance of Decision Tree Train Decision Tree using Scikit Learn Pruning of Decision Trees
Introduction to Feature Engineering Feature Transformation Feature Scaling Feature Engineering Frequency Encoding Automated Feature Engineering: Feature Tools
Understanding how to solve Multiclass and Multilabled Classification Problem Evaluation Metrics: Multi Class Classification
Introduction to Ensemble Techniques Basic Ensemble Techniques Implementing Basic Ensemble Techniques Finding Optimal Weights of Ensemble Learner using Neural Network Why Ensemble Models Work well?
Introduction to Stacking Implementing Stacking Variants of Stacking Implementing Variants of Stacking Introduction to Blending Bootstrap Sampling Introduction to Random Sampling Hyper-parameters of Random Forest Implementing Random Forest Out-of-Bag (OOB) Score in the Random Forest IPL Team Win Prediction Project Using Machine Learning Introduction to Boosting Gradient Boosting Algorithm Math behind GBM Implementing GBM in python Regularized Greedy Forests Extreme Gradient Boosting Implementing XGBM in python Tuning Hyperparameters of XGBoost in Python Implement XGBM in R/H2O Adaptive Boosting Implementing Adaptive Boosing LightGBM Implementing LightGBM in Python Catboost Implementing Catboost in Python
Different Hyperparameter Tuning methods Implementing Different Hyperparameter Tuning methods GridsearchCV RandomizedsearchCV Bayesian Optimization for Hyperparameter Tuning Hyperopt
Understanding SVM Algorithm SVM Kernels In-depth Intuition and Practical Implementation SVM Kernel Tricks Kernels and Hyperparameters in SVM Implementing SVM from Scratch in Python and R
Introduction to Principal Component Analysis Steps to Perform Principal Compound Analysis Computation of Covariance Matrix Finding Eigenvectors and Eigenvalues Implementing PCA in python Visualizing PCA A Brief Introduction to Linear Discriminant Analysis Introduction to Factor Analysis
Introduction to Clustering Applications of Clustering Evaluation Metrics for Clustering Understanding K-Means Implementation of K-Means in Python Implementation of K-Means in R Choosing Right Value for K Profiling Market Segments using K-Means Clustering Hierarchical Clustering Implementation of Hierarchial Clustering DBSCAN Defining Similarity between clusters Build Better and Accurate Clusters with Gaussian Mixture Models
Understand Basics of Recommendation Engine with Case Study
8 Ways to Improve Accuracy of Machine Learning Models
Introduction to Dask Working with CuML
Introduction to Machine Learning Interpretability Framework and Interpretable Models model Agnostic Methods for Interpretability Implementing Interpretable Model Understanding SHAP Out-of-Core ML Introduction to Interpretable Machine Learning Models Model Agnostic Methods for Interpretability Game Theory & Shapley Values
Introduction to AutoML Implementation of MLBox Introduction to PyCaret TPOT Auto-Sklearn EvalML
Pickle and Joblib Introduction to Model Deployment
Deploying Machine Learning Model using Streamlit Deploying ML Models in Docker Deploy Using Streamlit Deploy on Heroku Deploy Using Netlify Introduction to Amazon Sagemaker Setting up Amazon SageMaker Using SageMaker Endpoint to Generate Inference Deploy on Microsoft Azure Cloud Introduction to Flask for Model Deploying ML model using Flask
Model Deployment in Android Model Deployment in Iphone

Don’t Miss this Short Course on Embedding Models by Andrew Ng

ayushi9821704
ayushi9821704 21 Aug, 2024
5 min read

Introduction

Imagine a world where machines not only understand your questions but also respond with pinpoint accuracy. Thanks to the latest advancements in artificial intelligence, this vision is becoming a reality. Andrew Ng, a leading figure in AI and founder of DeepLearning.AI, has just launched a short course titled “Embedding Models: From Architecture to Implementation.“

This course delves into the heart of embedding models—vital components of modern AI systems. Whether you’re a seasoned AI professional or just starting your journey, this course offers a unique opportunity to explore the evolution of embedding models, from their historical roots to their role in cutting-edge applications like semantic search and voice interfaces. Prepare to embark on an educational adventure that not only enhances your technical skills but also transforms how you interact with the world of AI.

New Short Course on Embedding Models by Andrew Ng

Learning Outcomes

  • Learn about word embeddings, sentence embeddings, and cross-encoder models, and their application in Retrieval-Augmented Generation (RAG) systems.
  • Gain insights as you train and use transformer-based models like BERT in semantic search systems.
  • Learn to build dual encoder models with contrastive loss by training separate encoders for questions and responses.
  • Build and train a dual encoder model and analyze its impact on retrieval performance in a RAG pipeline.

Table of contents

Course Overview

The course provides an in-depth exploration of various embedding models. It starts with historical approaches and covers the latest models in modern AI systems. Voice interfaces, a key part of AI systems, rely on embedding models. These models help machines understand and accurately respond to human language.

This course covers fundamental theories and trusts learners’ understanding. It guides them through building and training a dual encoder model. By the end, participants will be able to apply these models to practical problems, especially in semantic search systems.

Detailed Course Content

Let us now dive deeper into the detailing of the course content.

Introduction to Embedding Models

This section starts with an analysis of the evolution of embedding models in artificial intelligence. You will find out how the first AI systems attempted to solve the problem of how text data can be represented and the evolution to embedding models. The important tools necessary in the understanding of how the embedding models work will be looked at in the course starting with the concepts of vector space and similarity.

You will learn more uses of embedding models in the current artificial intelligence such as in the recommendation systems, natural language processing, and semantic search. This will provide the foundation necessary for further analysis in subsequent sections.

Word Embeddings

This module provides an overview of what word embeddings are; this is methods used in transforming words into continuous vectors that resides in a multi-dimensional space. You will be informed how these embeddings model semantic context between words from their application on large text collections.

It is important to explain that the course will describe the most popular models for word embeddings learning, namely Word2Vec, GloVe, FastText. By the end of this example, you will understand the nature of these algorithms. And also how they go about creating the vectors for words.

This section will discuss word embeddings in real word applications for realization of the mentioned below information processing tasks like machine translation, opinion mining, information search etc. To show how word embeddings work in practice, real-life examples and scenarios will be included.

From Embeddings to BERT

Extending the prior approaches to word embedding, this section enunciates developments that contributed towards models such as BERT. This is because you will find out how earlier models have drawbacks and how BERT deals with them with the help of the context of each word in a sentence.

The course will also describe how BERT and similar models come up with a contextualized word embedding – a word might mean something different under different words. This kind of approach has focused on eradicating high-level understanding of language and has improved many NLP tasks.

You’ll explore the architecture of BERT, including its use of transformers and attention mechanisms. The course will provide insights into how BERT processes text data, how it was trained on vast amounts of text, and its impact on the field of NLP.

Dual Encoder Architecture

This module introduces the concept of dual encoder models. These models use different embedding models for different input types, such as questions and answers. You’ll learn why this architecture is effective for applications like semantic search and question-answering systems.

This course will also describe how the dual encoder models work, and the structure that these models will have, in order to distinguish from the single encoder models. Here, you will find information about what constitutes a dual encoder, how each of the encoders is trained to come up with an embedding relevant to its input.

This section will cover the advantages of using dual encoder models, such as improved search relevance and better alignment between queries and results. Real-world examples will show how dual encoders are applied in various industries, from e-commerce to customer support.

Practical Implementation

In this practical we will go through the process of constructing the model for dual encoder from scratch. There is TensorFlow or PyTorch where you will learn how to configure this architecture, feed your data and train the model.

You will learn how to train your dual encoder model in the course, especially using contrastive loss which is of paramount importance in training the model to learn how to disentangle between relevant and irrelevant pairs of data. Also about how how to further optimize the model to do better on certain tasks.

You will learn how to evaluate the efficiency of the model you’ve built and trained. The course discusses various measures to assess the quality of embeddings, including accuracy, recall, and F1-score. Additionally, you will discover how to compare the performance of a dual encoder model with a single encoder model.

Last but not least, the course will briefly explain how to deploy your trained model in production. The course teaches you how to fine-tune the model and keep it performing optimally, especially when incorporating new data.

Who Should Join?

This course is designed for a wide range of learners, including:

  • Data Scientists: Looking to deepen their understanding of embedding models and their applications in AI.
  • Machine Learning Engineers: Interested in building and deploying advanced NLP models in production environments.
  • NLP Enthusiasts: Explore the latest advancements in embedding models and apply them to improve semantic search and other NLP tasks.
  • AI Practitioners: With a basic knowledge of Python, who are eager to expand their skillset by learning how to implement and fine-tune embedding models.

Whether you’re familiar with generative AI applications or are just starting your journey in NLP, this course offers valuable insights and practical experience that will help you advance in the field.

Enroll Now

Don’t miss out on the opportunity to advance your knowledge in embedding models. Enroll today for free and start building the future of AI!

Conclusion

If you are looking for a detailed overview of embeddings and how they work, Andrew Ng’s new course on embedding models is the way to go. At the end of this course you will be in a good position of solving difficult AI problems related to semantic search and any other problem that involves embeddings. Whether you want to enhance your expertise in AI or learn the latest strategies, this course proves to be a boon.

Frequently Asked Questions

Q1. What are embedding models?

A. Embedding models are techniques in AI that convert text into numerical vectors, capturing the semantic meaning of words or phrases.

Q2. What will I learn about dual encoder models?

A. You’ll learn how to build and train dual encoder models, which use separate embedding models for questions and answers to improve search relevance.

Q3. Who is this course for?

A. This course is ideal for AI practitioners, data scientists, and anyone interested in learning about embedding models and their applications.

Q4. What practical skills will I gain?

A. You’ll gain hands-on experience in building, training, and evaluating dual encoder models.

Q5. Why are dual encoder models important?

A. Dual encoder models enhance search relevance by using separate embeddings for different types of data, leading to more accurate results.

Andrew NgArtificial IntelligencecareercoursesEmbedding Models
ayushi9821704
ayushi9821704 21 Aug, 2024

My name is Ayushi Trivedi. I am a B. Tech graduate. I have 3 years of experience working as an educator and content editor. I have worked with various python libraries, like numpy, pandas, seaborn, matplotlib, scikit, imblearn, linear regression and many more. I am also an author. My first book named #turning25 has been published and is available on amazon and flipkart. Here, I am technical content editor at Analytics Vidhya. I feel proud and happy to be AVian. I have a great team to work with. I love building the bridge between the technology and the learner.

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