Regular Expressions Quiz (12 Questions)

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Interview for NLP and ML Profile (2 Sep 2023) - For Data Scientist Role at Accenture

Problem 1

First question related to NLP.

What is ‘Dependency Parsing’?

Dependency Parsing is the process to analyze the grammatical structure in a sentence and find out related words as well as the type of the relationship between them.

Each relationship:

- Has one head and a dependent that modifies the head.

- Is labeled according to the nature of the dependency between the head and the dependent.

Ref: towardsdatascience

In the above diagram, there exists a relationship between car and black because black modifies the meaning of car. Here, car acts as the head and black is a dependent of the head. The nature of the relationship here is amod which stands for “Adjectival Modifier”. It is an adjective or an adjective phrase that modifies a noun.

sentence = 'Deemed universities charge huge fees'

Using SpaCy

Using NLTK and NetworkX

Visualization using DOT Definition

Problem 2

2.1) What is NER?

2.2) How do you build a custom NER?

Answer 2.1

NER refers to Named Entity Recognition.

Named-entity recognition (NER) is a subtask of information extraction that seeks to locate and classify named entities mentioned in unstructured text into pre-defined categories such as person names, organizations, locations, medical codes, time expressions, quantities, monetary values, percentages, etc.

Ref: custom-named-entity-recognition-with-bert

train-ner-with-custom-training-data-using-spacy

Answer 2.2

Most articles you would find on through Google search about how a custom NER can be built would point to a solution showcasing a supervised version of this problem.

Two very common approaches to building Custom NER using supervised learning are:

1. Using BERT

2. Using SpaCy

SpaCy uses labeled data in the form of Annotations.

Ref: Custom_Named_Entity_Recognition_with_BERT

Ref: custom-named-entity-recognition-using-spacy

Problem 3

How do you decide if you need Machine Learning for solving a problem?

Solution 3

This is a subjective question.

You can start by explaining the definition of Machine Learning as given by Tom Mitchell:

“A computer program is said to learn from experience E with respect to some class of tasks T and performance measure P, if its performance at tasks in T, as measured by P, improves with experience E.”

Then you can start by asking questions like if it is a problem that falls into one these problems that Machine Learning can solve such as:

1. Classification

2. Regression

3. Clustering

4. Outlier Detection

Or is it some problem related to Data Mining like Frequent Pattern mining, or Market Basket analysis.

Problem 4

How do you select an approach / a model for a Machine Learning problem?

Solution 4

Questions to ask the client so that you can figure out which model will work best:

1. Explainable AI

Do you need to explain every prediction to the client or customer?

If yes, you need Explainable AI as in Decision Tree, Logistic Regression, etc.

If no, you can go with Black Box AI models like Artificial Neural Networks, Deep Learning, etc.

2. Prediction Time

Does the model need to give out predictions instantly?

You need to know the prediction time of your model and compare it with other choices you have. For example: prediction time of Isolation Forest is lesser than prediction time of ANN for the problem of Outlier Detection.

3. Complexity

Is there a need for simple model or a complex model would work?

4. Maintainability

The project stakeholders may have specific requirements, such as maintainability and limited model complexity. As such, a model that has lower skill but is simpler and easier to understand may be preferred.

5. Available resources

If number of available resources (in terms of ‘computing power’ and ‘training data’) is not limited, you can think of deploying a Deep Learning based solution like the LLMs, for example.

On a side note:

Methods to assist in choosing the set of candidate models

# Data transformation (statistics)

# Exploratory data analysis

# Model specification

As in In statistics, model specification is part of the process of building a statistical model: specification consists of selecting an appropriate functional form for the model and choosing which variables to include.

# Scientific method (as in Hypothesis Testing)

Ref: wikipedia

Problem 5

What experience do you have of the ‘Cloud’?

Tags: Natural Language Processing,Machine Learning,

Deep Learning Roadmap A Step-by-Step Guide to Learning Deep Learning

Introduction

Deep Learning, a subfield of Artificial Intelligence, has made astounding strides in recent years, powering everything from image recognition to language translation. If you're eager to embark on your journey into the world of Deep Learning, it's essential to have a roadmap. In this article, we'll provide you with a concise guide on the key milestones and steps to navigate as you master the art of Deep Learning.

Deep Learning Roadmap

Step 1: The Foundation - Understand Machine Learning Basics

Before diving deep, ensure you have a solid grasp of Machine Learning concepts. Familiarize yourself with supervised and unsupervised learning, regression, classification, and model evaluation. Books like "Machine Learning for Dummies" can be a great starting point.

Step 2: Python Proficiency

Python is the lingua franca of Deep Learning. Learn Python and its libraries, particularly NumPy, Pandas, and Matplotlib. Understanding Python is crucial as it's the primary language for developing Deep Learning models.

Step 3: Linear Algebra and Calculus

Deep Learning involves complex mathematics. Brush up on your linear algebra (vectors, matrices, eigenvalues) and calculus (derivatives, gradients) as they form the foundation of neural network operations.

Step 4: Dive into Neural Networks

Start with understanding the basics of neural networks. Learn about artificial neurons, activation functions, and feedforward neural networks. The book "Deep Learning" by Ian Goodfellow is an excellent resource.

Step 5: Convolutional Neural Networks (CNNs)

For image-related tasks, CNNs are essential. Explore how they work, learn about convolution, pooling, and their applications in image recognition. Online courses like Stanford's CS231n provide excellent materials.

Step 6: Recurrent Neural Networks (RNNs)

RNNs are crucial for sequential data, such as natural language processing and time series analysis. Study RNN architectures, vanishing gradient problems, and LSTM/GRU networks.

Step 7: Deep Dive into Deep Learning Frameworks

Become proficient in popular Deep Learning frameworks like TensorFlow and PyTorch. These libraries simplify building and training complex neural networks.

Step 8: Projects and Hands-On Practice

Apply what you've learned through projects. Start with simple tasks like digit recognition and progressively tackle more complex challenges. Kaggle offers a platform for real-world practice.

Step 9: Natural Language Processing (NLP)

For text-related tasks, delve into NLP. Learn about word embeddings, recurrent models for text, and pre-trained language models like BERT.

Step 10: Advanced Topics

Explore advanced Deep Learning topics like Generative Adversarial Networks (GANs), Reinforcement Learning, and transfer learning. Stay updated with the latest research through journals, conferences, and online courses.

Step 11: Model Optimization and Deployment

Understand model optimization techniques to make your models efficient. Learn how to deploy models in real-world applications using cloud services or on-device deployment.

Step 12: Continuous Learning

Deep Learning is a rapidly evolving field. Stay up-to-date with the latest research papers, attend conferences like NeurIPS and CVPR, and join online forums and communities to learn from others.

Conclusion

The Deep Learning roadmap is your guide to mastering this exciting field. Remember that the journey may be challenging, but it's immensely rewarding. By building a strong foundation, exploring key neural network architectures, and constantly seeking to expand your knowledge, you'll be well on your way to becoming a proficient Deep Learning practitioner. Happy learning!

References:

Full Stack Data Science with Python Course on Github

Enhancing AI Risk Management in Financial Services with Machine Learning

Introduction:

The realm of financial services is rapidly embracing the power of artificial intelligence (AI) and machine learning (ML) to enhance risk management strategies. By leveraging advanced ML models, financial institutions can gain deeper insights into potential risks, make informed decisions, and ensure the stability of their operations. In this article, we'll explore how AI-driven risk management can be achieved using the best ML models in Python, complete with code examples.

AI Risk Management in Financial Services

Step 1: Data Collection and Preprocessing

To begin, gather historical financial data relevant to your risk management objectives. This could include market prices, economic indicators, credit scores, and more. Clean and preprocess the data by handling missing values, normalizing features, and encoding categorical variables.

Step 2: Import Libraries and Data

In your Python script, start by importing the necessary libraries:

import pandas as pd
import numpy as np
from sklearn.model_selection import train_test_split
from sklearn.preprocessing import StandardScaler
from sklearn.metrics import accuracy_score, classification_report
from sklearn.ensemble import RandomForestClassifier
from xgboost import XGBClassifier
Load and preprocess your dataset:
data = pd.read_csv("financial_data.csv") 
X = data.drop("risk_label", axis=1)
y = data["risk_label"]

Step 3: Train-Test Split and Data Scaling
Split the data into training and testing sets:
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)
Scale the features for better model performance:
scaler = StandardScaler()
X_train_scaled = scaler.fit_transform(X_train)
X_test_scaled = scaler.transform(X_test)

Step 4: Implement ML Models
In this example, we'll use two powerful ML models: Random Forest and XGBoost.
Random Forest Classifier:
rf_model = RandomForestClassifier(n_estimators=100, random_state=42)
rf_model.fit(X_train_scaled, y_train)
rf_predictions = rf_model.predict(X_test_scaled)

rf_accuracy = accuracy_score(y_test, rf_predictions)
print("Random Forest Accuracy:", rf_accuracy)
print(classification_report(y_test, rf_predictions))
XGBoost Classifier:
xgb_model = XGBClassifier(n_estimators=100, random_state=42)
xgb_model.fit(X_train_scaled, y_train)
xgb_predictions = xgb_model.predict(X_test_scaled)

xgb_accuracy = accuracy_score(y_test, xgb_predictions)
print("XGBoost Accuracy:", xgb_accuracy)
print(classification_report(y_test, xgb_predictions))

Step 5: Evaluate and Compare
Evaluate the models' performance using accuracy and classification reports. Compare their results to determine which model is better suited for your risk management goals.

Conclusion:
AI-driven risk management is revolutionizing the financial services industry. By harnessing the capabilities of machine learning, financial institutions can accurately assess risks, make informed decisions, and ultimately ensure their stability and growth. In this article, we've demonstrated how to implement risk management using the best ML models in Python. Experiment with different models, fine-tune hyperparameters, and explore more advanced techniques to tailor the solution to your specific financial service needs. The future of risk management lies at the intersection of AI and finance, and now is the time to embrace its potential.

AI and Financial Risk Management – Critical Insights for Banking Leaders 

I hope this article was helpful. If you have any questions, please feel free to leave a comment below.