Customer service Chatbot
Project Overview: Create a system that can assist clients at every stage of the process and respond to their inquiries. utilizing NLP approaches to address client inquiries and receive pertinent feedback.
Important Technologies:
NLP libraries, such as spaCy and NLTK
Frameworks for chatbots (like Microsoft Bot Framework and Rasa)
Machine learning models (such as GPT-3 and BERT)
Why It Is Unique:
Additionally, you will get the opportunity to create an intelligent chatbot and demonstrate your mastery of NLP and ML, two widely used AI technologies.
Sentiment Analysis Tool
Provide a structure for a sentiment analysis instrument that is capable of classifying textual data (e.g. Sentiment analysis is the process of classifying text data into positive, negative, and neutral sentiments. Examples of this include social media, blogs, product or service descriptions, and words like emails, Facebook updates, status updates, tweets from Twitter, and online reviews.
Important Technologies:
Text preprocessing (such as stemming and tokenization)
Algorithms for machine learning (such as SVM and logistic regression)
Deep learning models, such as transformers and LSTM,
Recognition System
Create an image recognition system that can identify different types of objects, animals, and scenes in pictures. Try utilizing a convolutional neural network (CNN) to design your model and train it on some dataset, such as Imagenet.
Important Technologies:
frameworks for deep learning (such as PyTorch and TensorFlow)
CNN structures
Libraries for image processing
Industrial Equipment Predictive Maintenance
Create an algorithm to build a function that can predict equipment failures before they happen, based on the data that is provided. This instance involves forecasting and anomaly analysis, which is mostly concerned with time.
Important Technologies:
Forecasting models for time series (e.g., LSTM, ARIMA)
Algorithms for detecting anomalies (such as autoencoders and isolation forests)
Gathering and preparing data
Personalized Recommendation Engine
A customization system that can recognize user preferences or behavior and then recommend material based on that identification is necessary to create a recommendation engine.
Important Technologies:
Joint filtering
Filtering according to content
Systems of hybrid recommendations
Autonomous Navigation System for Robots
Create an Android-compatible N-Gram generator capable of producing 145,300,000 animal names. Reinforcement learning and the sensor data would be used in this case to train the model.
Important Technologies:
Reinforcement learning (e.g., DQN, Q-Learning): Different kinds of sensors are employed, such as: Integration of sensor data (cameras, LIDAR, etc.)
Frameworks for robotics, such as ROS
Real-Time Speech Recognition
IT/To perform this, the following actions are necessary: Develop a speech recognition system that can translate spoken words into text in real time. It suggests using audio processing techniques and using deep learning models to increase recognition accuracy.
Important Technologies:
While some libraries (Librosa, PyDub, etc.) are more widely used for audio processing of various sound collections,
The resources listed below can be used to create a speech recognition application: APIs for speech recognition (e.g. The following are some of the widely used open-source voice recognition systems: Applications Voice recognition software, such CMU Sphinx and Google Speech-to-Text
cloud computing (such as transformers and RNNs)
Fraud Detection System
Create a model to identify fraudulent transactions in order to stop financial market fraud from happening. Use machine learning algorithms to find further indications of suspicious activity and employ heuristics through the creation of an analytical model to identify early informative signals of the situations.
Important Technologies:
Classification algorithms (e.g., artificial neural networks (\|ai|>decision trees, random forests) are among the many machine learning algorithms that are employed.
Techniques for detecting anomalies
These consist of feature extraction, feature selection, data normalization, and data cleansing.
AI-Powered Content Generation
Investigate and create an AI model that will be used, among other things, to write poetry, essays, and little bits of code. This should be accomplished by employing generative models, like GPT-3, in which the AI uses the given instructions to produce a text that is legible by humans.
Important Technologies:
Generative models (OpenAI Codex, GPT-3, etc.)
The prompt is thoughtfully created, and the content is preprocessed to eliminate stop words in order to guarantee excellent results.
optimizing previously learned models
Healthcare Diagnostic Tool
Create an artificial intelligence-based healthcare diagnostic system to assist in the diagnosis of illnesses. I’ll accomplish this by either using patient data or disease-related photos. Predictive modeling and picture categorization are both required for this project and can be used separately or together.
Important Technologies:
diagnosis and analysis of MRIs and X-rays, among other medical imaging
Artificial neural networks and other machine learning models are examples of artificial systems that can learn from experience by comprehending the underlying data and then utilizing that understanding to make predictions (e.g., CNNs, decision trees).
Preprocessing and data integration are involved. The majority of AI freelance platforms have portfolios, which enable experts’ work and projects to be more visible and potentially aid in career advancement. These projects cover a broad range of industries, including robotics, healthcare, computer vision, natural language processing, and more, providing you with numerous and abundant chances to demonstrate your problem-solving and engineering skills.
