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- Data Science & Machine Learning (NLP/LLM Specialization)
Data Science & Machine Learning
NLP/LLM Specialization
About This Course
This course blends Data Science and Machine Learning fundamentals with a focus on Natural Language Processing (NLP) and Large Language Models (LLM), empowering learners in text analytics and advanced Al.
- 8 Months Course
- 4 Months Internship
- Certificate
Tools and Programs You Will Learn
Key Points
- Expert Instructors to Learn from industry leading professionals.
- Flexible Learning to Study anytime anywhere at your pace
- Interactive Courses to Engaging materials for the best experience
- Affordable Excellence and High-quality education at a reasonable cost
Scope
NLP Engineer | Business Intelligence Analyst | Machine Learning Engineer | Data Scientist (NLP Focus) | Al Research Scientist | Text Mining Specialist | Speech Recognition Engineer | Conversational Al Developer
Curriculum
The Rootsys International has been exclusively founded to groom young leaders in the art of savoir-vivre or social etiquette, Our curriculum and programmes for complete personality enhancement are designed to be a life changing experience.
MODULE 1 Foundations of Data Science
– Introduction to data science and its applications
– Data lifecycle and key concepts
– Ethics and best practices in data science
MODULE 2 Fundamentals of Machine Learning
– Introduction to machine learning algorithms and models.
– Supervised, unsupervised, and reinforcement learning.
– Model evaluation and validation techniques.
MODULE 3 Jupyter Notebook and Google Colab
– Introduction to Jupyter Notebook: Understanding the interactive
computing environment for data science and machine learning.
– Exploring Jupyter Notebook interface and features: Markdown cells, code cells, and keyboard shortcuts.
– Data exploration and visualization with Jupyter Notebook: Using libraries like Matplotlib and Seaborn.
– Collaboration and sharing with Jupyter Notebook: Exporting notebooks, version control.
– Introduction to Google Colab: Overview of the cloud-based Jupyter Notebook environment provided by Google.
– Google Colab for collaborative projects: Real-time collaboration, sharing
notebooks, and accessing resources like GPUs and TPUs for training models
MODULE 4 Mastering Python for Data Science
– Python fundamentals: Variables, data types, operators, and control structures.
– Functions and modules: Defining, calling, and organizing code into reusable units
– Object-oriented programming (OOP) in Python: Classes, objects, inheritance,and polymorphism.
– File handling and input/output operations: Reading from and writing to files.
– Error handling and exceptions: Handling runtime errors gracefully.
– Advanced Python topics: Decorators, generators, context managers, and comprehension techniques.
MODULE 5 Building a Foundation in Statistics and Mathematics (Basics)
– Fundamentals of statistics: descriptive statistics, probability distributions.
– Linear algebra essentials: vectors, matrices, matrix operations.
– Calculus basics: derivatives, integrals.
MODULE 6 Version Control with Git
– Introduction to Git: Version Control System.
– Basic Git Commands.
– Branching and Merging.
– Collaboration with Git.
MODULE 7 Practing Ai Tools
– Practicing Command Prompt to seek codes and logics.
– Using the codes and logics in the appropriate circumstanses.
AI tools we use common:
ChatGPT, Gemini, Copilot, Blackbox,Phind
MODULE 8 Data Analysis with NumPy and Pandas
– Introduction to NumPy: Arrays, indexing, and operations.
– Data Manipulation with Pandas: Series, DataFrames, and Data Wrangling.
– Data Cleaning and Preprocessing Techniques.
– Working with Missing Data and Outliers.
MODULE 9 Data Visualization with Matplotlib and Seaborn
– Introduction to Data Visualization: Importance and Principles.
– Visualization with Matplotlib: Creating plots, charts, and graphs.
– Advanced Visualization with Seaborn: Styling and customization.
MODULE 10 Understanding Databases
– Introduction to Relational Databases.
– SQL fundamentals for data retrieval and manipulation.
– Data access with Python using SQLAlchemy.
– NoSQL databases: Overview of document, key-value, and columnar
databases.
MODULE 11 Data Analysis With Python and Pandas
– Advanced data manipulation and analysis with Pandas.
– Handling missing data and outliers.
– Time series analysis and manipulation.
MODULE 12 Building RESTful APIs with Flask
– Introduction to Flask Web Framework.
– Building RESTful APIs for serving machine learning models.
– Introduction to Flask: Setting up a Flask project, routing, and request handling.
– RESTful architecture principles: HTTP methods, status codes, and resource endpoints.
– Building API endpoints: Handling requests, processing data, and returning responses.
– API documentation: Generating documentation with tools like Swagger and Postman.
MODULE 13 Introduction to Deep Learning
– Basics of neural networks.
– Fundamentals of Deep Learning: Neural Networks, Activation Functions.
– Convolutional Neural Networks (CNNs) and Recurrent Neural Networks (RNNs).
– Convolutional Neural Networks (CNNs) for image classification.
MODULE 14 Introduction to NLP
– Fundamentals of natural language processing (NLP): Tokenization,stemming, and lemmatization.
– Text preprocessing techniques: Stopword removal, punctuation handling,and case normalization.
– Sentiment analysis and text classification using machine learning algorithms..
– Word embeddings and distributed representations for text data.
MODULE 15 NLP Libraries and Frameworks
– Overview of popular NLP libraries and frameworks: NLTK, spaCy, Gensim,and TensorFlow NLP.
– Working with NLTK: Tokenization, POS tagging, sentiment analysis, and named entity recognition.
– Text processing and analysis using spaCy: Dependency parsing, named entity recognition, and text summarization.
– Introduction to BERT and other advanced NLP models for language understanding and generation.
MODULE 16 Natural Language Understanding (NLU)
– Introduction to Natural Language Understanding: Understanding the principles and techniques of NLU for interpreting and extracting meaning from human language.
– NLU Libraries and Tools: Exploring popular NLU libraries and tools such as NLTK, spaCy, and Hugging Face Transformers for text processing and understanding.
– NLU Applications: Applying NLU techniques to various applications such as sentiment analysis, named entity recognition (NER), and text classification.
MODULE 17 Natural Language Generation (NLG)
– Introduction to Natural Language Generation: Understanding the concepts and methodologies involved in generating human-like text using machine learning models.
– NLG Techniques and Models: Exploring NLG techniques including rule-based, template-based, and neural-based approaches, along with models such as GPT(Generative Pre-trained Transformer) for text generation.
– NLG Applications: Implementing NLG for applications such as chatbots, summarization, and content generation in marketing and advertising.
MODULE 18 Introduction to Language Models
– Overview of language modeling and its significance in NLP.
– Types of language models: n-gram models, neural language models, transformer models.
– Evaluation metrics for assessing language model performance.
MODULE 19 Legal Language Models (LLM)
– Introduction to Legal Language Models (LLM): Understanding the role of language models in legal text analysis and processing.
– Training and fine-tuning LLMs for legal tasks: Contract review, legal document summarization, and case prediction.
– Leveraging pre-trained LLMs for legal applications: BERT, GPT, and other specialized models.
– Case studies and practical exercises applying LLMs to legal text analysis and processing tasks.
– Project development using Legal Language Models: Designing and implementing solutions for real-world legal challenges.
MODULE 20 Langchain
– Understanding Langchain: Overview of the Langchain platform and its capabilities in legal document analysis and management.
– Integrating LLMs with Langchain: Harnessing the power of language models for enhanced document processing and workflow automation.
– Langchain features and functionalities: Document parsing, metadata extraction, and semantic search.
– Project development with Langchain: Creating innovative solutions for optimizing legal document workflows and improving efficiency.
MODULE 21 End-to-End Project
– Implementation and Development: Building the project infrastructure, integrating Langchain and LLM functionalities, and developing the necessary components to achieve the project goals.
– Testing, Evaluation, and Deployment: Conducting thorough testing, evaluating model performance, and deploying the project to the production environment for real-world usage.