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Career Path - LLM Developer

Build Intelligent Apps with Large Language Models – Learn Prompt Engineering, API Integration, Fine-Tuning, Agentic AI, LangChain, Hugging Face Models
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Course Duration: 200 hours
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Career Path - LLM Developer: Artificial Intelligence, Data Science & Machine Learning with Python – Self-Paced Online Course

Large Language Models (LLMs) and AI-powered technologies are redefining the future of automation, communication, and intelligent decision-making across every sector. This self-paced, hands-on course offers a comprehensive learning journey for individuals aiming to become proficient in AI, data science, and machine learning with a specialization in building and deploying applications using LLMs. Whether you're an aspiring developer, data analyst, ML engineer, or technical innovator, this course delivers practical, real-world knowledge to prepare you for the evolving job market.

The course begins by establishing a solid foundation in Python programming, ensuring learners are comfortable with syntax, data types, control structures, and modular coding practices. It then introduces essential data science tools like Pandas, NumPy, and Matplotlib to manipulate, analyze, and visualize data efficiently. Core statistical concepts, including distributions, hypothesis testing, and correlation, are explored to strengthen analytical thinking and interpretation of data.

As learners progress, the curriculum shifts into the domain of machine learning using scikit-learn. You'll work on supervised learning techniques such as linear and logistic regression, decision trees, and ensemble methods like Random Forest and XGBoost. For unsupervised learning, topics include clustering, dimensionality reduction, and anomaly detection. These concepts are reinforced through hands-on labs, where you’ll train models, tune hyperparameters, and evaluate performance using accuracy, precision, recall, and F1 scores.

With a solid ML foundation, the course dives into the fascinating world of artificial intelligence and natural language processing (NLP). Learners explore key NLP concepts such as tokenization, stemming, lemmatization, POS tagging, sentiment analysis, and named entity recognition using libraries like spaCy and NLTK. Special attention is given to transformer models like BERT, T5, and GPT, introducing learners to the architecture and mechanisms behind state-of-the-art LLMs.

Building upon this foundation, the course emphasizes application development with LLMs. Using the OpenAI API, Hugging Face Transformers, and LangChain, you’ll create intelligent agents capable of answering questions, summarizing documents, generating content, and integrating memory and context. Learners will gain hands-on experience building prompt-driven workflows, few-shot learning templates, and Retrieval Augmented Generation (RAG) pipelines using vector databases such as FAISS and Pinecone.

You’ll learn how to generate, store, and query embeddings to enable semantic search and intelligent retrieval. Projects will include developing a document question-answering app, a resume-matching engine, and an industry-specific chatbot using OpenAI's GPT models. These applications bring together prompt engineering, vector search, and user interfaces to deliver practical solutions for enterprise and consumer environments.

The course also focuses on front-end and back-end integration for LLM applications. You’ll use Streamlit and Gradio to build web interfaces, and FastAPI to create scalable RESTful backends. Deployment strategies with Docker, GitHub Actions, and cloud platforms such as AWS and Hugging Face Spaces are covered to ensure your AI apps are production-ready and maintainable.

An additional module is dedicated to responsible AI and ethics. This includes understanding bias in training data, model interpretability, hallucinations in LLMs, and strategies for building transparent and accountable AI systems. Learners are encouraged to consider the social and organizational impact of deploying AI at scale.

Throughout the course, practical projects reinforce learning and help learners build a personal portfolio of real-world AI applications. These capstone projects include building a semantic search engine, a multi-lingual translator bot, a market trend analyzer, and a customer support assistant. Each project is accompanied by templates, datasets, and guided instructions to help learners succeed regardless of their prior experience.

Upon successful completion, learners receive a Course Completion Certificate from Uplatz. This certificate validates your ability to apply machine learning and AI techniques in professional environments, showcasing your proficiency in Python, NLP, LLMs, and full-stack AI app development. The skills you acquire in this course align with job roles such as LLM Developer, AI Engineer, NLP Specialist, and Data Scientist.

The growing demand for professionals who can harness the power of AI and LLMs means that the career opportunities are abundant across industries like healthcare, education, finance, e-commerce, and government. Whether you're automating customer service, optimizing business workflows, or innovating in research, the ability to build with LLMs sets you apart in the competitive tech landscape.

With lifetime access to the course materials, community forums, regular content updates, and expert support, this course offers a complete pathway for anyone serious about mastering modern AI development. It is ideal for learners at all levels, providing a structured, engaging, and future-proof approach to launching a successful career in artificial intelligence.

 
 

Course/Topic 1 - Deep Learning Foundation - all lectures

  • In this session we will learn about the introduction to Deep Learning. This video talks about Deep Learning as a series introduction and what is a neural network. Furthermore, we will talk about the 3 reasons to go deep and your choice of Deep net.

    • 52:39

  • In this video tutorial we will discuss about the neural networks and the 3 reasons to go Deep. Further we will also learn about the use of GPU in artificial intelligence and your choice of deep net.

    • 30:06

  • In this session we will learn about the deep learning models basics. After this video you will be able to understand the concept of restricted Boltzmann machines and deep belief network. Furthermore, you will learn about the convolution neural network and recurrent neural network.

    • 43:00

  • In this video course further topics of Deep learning models. After this video you will be able to understand the convolution neural network and its characteristics in detail.

    • 1:26:51

  • In this video course further topics of Deep learning models. After this video you will be able to understand the recurrent neural network and its characteristics.

    • 15:43

  • In this session the tutor talks about the basic Additional Deep Learning Models. In this video you will learn about Auto encoders, Recursive neural tensor network and generative adversarial networks

    • 44:28

  • This session is in continuation to the previous session. In this video we will learn about the Recursive Neural Tensor Network in detail and hierarchical structure of data.

    • 31:44

  • In this Additional Deep Learning Models tutorial, we will proceed with the Generative Adversarial Networks (GAN) and its uses.

    • 22:22

  • In this video the tutor explains the Platforms and Libraries of Deep Learning. We will start with what is a deep net platform, H2O.ai and Dato Graph Lab. Further we will see what is a Deep Learning Library and Theano and Caffe. We will also cover a bit of Keras and TensorFlow.

    • 53:43

  • This tutorial will cover the further part of DatoGraph Lab and its history. Further we wil see the benefits and uses of DatoGraph Lab.

    • 28:19

  • This tutorial will cover the further part of DatoGraph Lab and its history.

    • 28:15

  • In this video we will cover the further topics of Deep Learning platform and Libraries such as what is a Deep Learning Library? when and how to use Theano and Caffe as Deep Learning Library.

    • 29:19

  • In previous video we have leant about Theano and Caffe Deep Learning Library. In this video we will learn about the TensorFlow (free and open source library) as a Deep Learning Library and building Deep Learning Models.

    • 40:18

  • In this video we will learn about the last type of Library i.e. Keras. Keras is an open source neural network library and runs on top of Theano or TensorFlow. We will further see the advantages of Keras in Deep Learning.

    • 25:34

Course/Topic 2 - Python Programming - all lectures

  • In this lecture session we learn about introduction to python programming for beginners and also talk about features of python programming.

    • 10:21

  • In this lecture session we learn about basic elements of python in python programming and also talk about features of elements of python.

    • 19:37

  • In this lecture session we learn about installation of python in your system and also talk about the best way of installation of python for beginners.

    • 13:18

  • In this lecture session we learn about input and output statements in python programming and also talk about features of input and output statements.

    • 24:05

  • In this lecture session we learn about data types in python programming and also talk about all the data types in python programming.

    • 23:05

  • In this lecture session we learn about operators in python and also talk about how we use operators in python programming.

    • 47:07

  • In this lecture session we learn about different types of operators in python programming and also talk about features of operators in python.

    • 29:47

  • In this lecture session we learn about type conversion in python programming and also talk about features of type conversion in python.

    • 23:39

  • In this lecture session we learn about basic programming in python programming for beginners.

    • 15:56

  • In this lecture session we learn about features of basic programming in python and also talk about the importance of programming in python.

    • 05:13

  • In this lecture session we learn about math modules in python programming and also talk about features of math modules in python.

    • 26:43

  • In this lecture session we learn about conditional statements in python and also talk about conditional statements in python programming.

    • 28:24

  • In this lecture session we talk about basic examples of conditional statements in python.

    • 19:27

  • In this lecture session we learn about greater and less then conditional statements in python programming.

    • 13:39

  • In this lecture session we learn about nested IF Else statements and also talk about features of nested IF else statements.

    • 11:04

  • In this lecture session we learn about looping in python in programming for beginners and also talk about looping in python.

    • 25:06

  • In this lecture session we learn about break and continue keywords and also talk about features of break continue keywords.

    • 20:48

  • In this lecture session we learn about prime number programs in python and also talk about functions of prime number programs in python.

    • 17:31

  • In this lecture session we learn about while loop in python programming and also talk about features of while loop in python.

    • 35:35

  • In this lecture session we learn about nested For loop in python programming and also talk about features of nested For loop.

    • 12:34

  • In this lecture session we learn about features of nested for loop in python and also talk about the importance of nested For loop in python.

    • 12:49

  • In this lecture session we learn about functions in python and also talk about different types of functions in pythons.

    • 19:28

  • In this lecture session we learn about passing arguments to functions in python programming and also talk about features of passing arguments to functions

    • 08:59

  • In this lecture session we learn about return keywords in python and also talk about features of return keywords in python.

    • 12:16

  • In this lecture session we learn about calling a function in python programming and also talk about calling a function.

    • 15:07

  • In this lecture session we learn about factors of calling a function in python programming and also talk about features of calling a function.

    • 20:17

  • In this lecture session we learn about a program to swap 2 numbers using calling a function in python programming.

    • 19:27

  • In this lecture session we learn about functions of arbitrary arguments in python programming and also talk about features of arbitrary arguments.

    • 10:34

  • In this lecture session we learn about functions keywords arguments in python programming and also talk about features of keyword arguments.

    • 06:55

  • In this lecture session we learn about functions default arguments in python programming and also talk about features of default argument.

    • 06:57

  • In this lecture session we learn about global and local variables in python programming and also talk about features of global and local variables.

    • 19:37

  • In this lecture session we learn about global and local keywords and also talk about features of global and local keywords.

    • 10:44

  • In this lecture session we learn about strings in python programming and also talk about features of string in python.

    • 17:42

  • In this lecture session we learn about string methods in python programming and also talk about features of string methods in python.

    • 21:53

  • In this lecture session we learn about string functions in python and also talk about features of strings functions in python.

    • 28:02

  • In this lecture session we learn about string indexing in python programming and also talk about features of string indexing in python programming.

    • 13:51

  • In this lecture session we learn about introduction of lists in python programming and also talk about features of introduction to lists.

    • 06:31

  • In this lecture session we learn about basics of lists python programming and also talk about features of basics of lists in python.

    • 33:09

  • In this lecture session we learn about list methods and also talk about features of list method python programming.

    • 32:43

  • In this lecture session we learn about linear search on list and also talk about features of linear search on list in brief.

    • 23:20

  • In this lecture session we learn about the biggest and smallest number of the list and also talk about features of MAX and Min in a list.

    • 14:40

  • In this lecture session we learn about the difference between 2 lists in python programming and also talk about features of 2 lists.

    • 13:22

  • In this lecture session we learn about tuples in python programming and also talk about tuples in python programming.

    • 20:19

  • In this lecture session we learn about introduction to sets in python and also talk about functions of introduction to sets in python.

    • 32:43

  • In this lecture session we learn about set operations in python programming and also talk about features of set operation in brief.

    • 26:56

  • In this lecture session we learn about set examples and also talk about features set examples.

    • 11:05

  • In this lecture session we learn about introduction to dictionaries in python programming and also talk about featured dictionaries.

    • 14:47

  • In this lecture session we learn about creating and updating dictionaries in python programming and also talk about features of creating and updating dictionaries.

    • 32:49

  • In this lecture session we learn about deleting items in a dictionary in python programming and also talk about features of deleting items in a dictionary.

    • 08:06

  • In this lecture session we learn about values and items in a dictionary in python programming and also talk about features of values and items in the dictionary.

    • 13:14

  • In this lecture session we learn about dictionary methods in python programming and also talk about features of dictionary methods.

    • 18:46

  • In this lecture session we learn about built in methods in python programming and also talk about features of built in methods in python.

    • 20:25

  • In this lecture session we learn about lambda functions and also talk about features of lambda function in python programming.

    • 15:29

  • In this lecture session we learn about file handling in python programming and also also talk about the importance of file handling in python.

    • 15:58

  • In this lecture session we learn about file handling in python programming and also talk about features of file handling in python.

    • 36:13

  • In this lecture session we learn about exception handling in python and also talk about features of exception handling in python.

    • 08:46

  • In this lecture session we learn about exception handling examples in python programming.

    • 25:04

  • In this lecture session we learn about python programs in python programming and also talk about features of python programs

    • 18:40

  • In this lecture session we learn about the program of printing odd numbers in python programming and also talk about the best way of printing.

    • 10:46

  • In this lecture session we learn about counting the number of vowels and consonants in a string and also talk about features of these programs.

    • 21:38

  • In this lecture session we learn about python programs of swapping two numbers in a list by taking indexes as parameters.

    • 14:08

  • In this lecture session we learn about bubble sort and also talk about features of bubble sort in brief.

    • 35:36

  • In this lecture session we learn about operator precedence in python and also talk about features of operator precedence in python.

    • 14:51

  • In this lecture session we learn about operator precedence in python and also talk about features of operator precedence types.

    • 11:28

  • In this lecture session we learn about recursion in python and also talk about features of recursion in python.

    • 22:15

  • In this lecture session we learn about binary search in python and also talk about features of binary search in python programming.

    • 23:18

  • In this lecture session we learn about binary search in python and also talk about the importance of binary search in python.

    • 35:04

  • In this lecture session we learn about object oriented programming and also talk about features of object oriented programming in brief.

    • 21:52

  • In this lecture session we learn about factors and types of object oriented programming in python programming.

    • 17:41

  • In this lecture session we learn about OOPS and procedural programming and also talk about features of OOPS and procedural programming in OOPS.

    • 06:36

  • In this lecture session we learn about OOPS programs in python and also talk about the importance of OOPS.

    • 27:50

  • In this lecture session we learn about inheritance in python programming and also talk about features of inheritance.

    • 37:24

  • In these lecture sessions we learn about features of object creation in python programming and also talk about object creation in python.

    • 24:10

  • In this lecture session we learn about OOPS terminology and functions and also talk about features of OOPS terminology and functions.

    • 24:41

  • In this lecture session we learn about built in class attributes and garbage collection in python programming.

    • 27:26

  • In this lecture session we learn about inheritance in python and also talk about features of inheritance in python.

    • 19:02

  • In this lecture session we learn about the importance of inheritance in python programming and also talk about functions of inheritance.

    • 29:26

  • In this lecture session we learn about programs in inheritance in python programming and also talk about features of inheritance in python.

    • 31:43

  • In this lecture session we learn about polymorphism in python programming polymorphism and also talk about polymorphism in python.

    • 24:47

  • In this lecture session we learn about features of polymorphism in python and also talk about the importance of polymorphism in python.

    • 14:01

  • In this lecture session we learn about the time module in python and also talk about features time module in python in features.

    • 36:22

  • In this lecture session we learn about the importance of time modules in python time module in python in brief.

    • 44:51

  • In this lecture session we learn about the calendar module in python programming in brief.

    • 32:04

  • In these lecture sessions we learn about calendar methods in python programming and also talk about the importance of calendar methods.

    • 37:03

  • Class 28.1 - Boolean in Python

    • 09:32

  • In this lecture session we learn about python iterators and also talk about features of python iterators in brief.

    • 09:30

  • In this lecture session we learn about python programs and summary in python programming and also talk about python programs.

    • 46:37

  • In this lecture sessions we learn about python programs and also talk about features of python programs and summary.

    • 23:27

Course/Topic 3 - Generative AI Specialization - all lectures

  • Lecture 1 - Introduction to Generative AI - part 1

    • 34:55

  • Lecture 2 - Introduction to Generative AI - part 2

    • 30:05

  • Lecture 3 - Introduction to Generative AI - part 3

    • 31:14

  • Lecture 4 - Introduction to Large Language Models (LLMs) - part 1

    • 23:54

  • Lecture 5 - Introduction to Large Language Models (LLMs) - part 2

    • 36:54

  • Lecture 6 - Prompt Engineering Basics - part 1

    • 17:13

  • Lecture 7 - Prompt Engineering Basics - part 2

    • 46:41

  • Lecture 8 - Responsible AI

    • 44:48

  • Lecture 9 - Generative AI - Impact - Considerations - Ethical Issues

    • 53:37

Course/Topic 4 - API Design & Development - all lectures

  • In this lecture session we learn about basic introduction to API Design and development with RAML and also talk about some key features of API design with RAML.

    • 1:26:59

  • In this lecture session we learn about data formats and authentication of API design and development with RAML and also talk about the importance of RAML in API design and development.

    • 1:08:47

  • In this lecture session we learn about how we start designing API and also talk about basic resources and method of API design and development in RAML.

    • 1:36:12

  • In this lecture session we learn about API design center and features of API and also talk about some function of API design center in brief.

    • 1:39:11

  • In this tutorial we learn about API best practices is to Provide language-specific libraries to interface with your service and also talk about features of API design and development with RAML.

    • 2:03:24

  • In this tutorial we learn about Schemes define which transfer protocols you want your API to use. If your API is enforced by an API Connect gateway, only the HTTPS protocol is supported and also talks about features of API security schemes.

    • 2:30:57

  • In this tutorial we learn about API Designer provides a visual or code-based guided experience for designing, documenting, and testing APIs in any language and also talk about the importance of API design principles in brief.

    • 1:01:30

  • In this lecture session we learn about RESTful API Modeling Language (RAML) makes it easy to manage the API lifecycle from design to deployment to sharing. It's concise and reusable; you only have to write what you need to define and you can use it again and again.

    • 1:22:27

  • In this lecture session we learn about RESTful API Modeling Language (RAML) is a YAML-based language for describing RESTful APIs. It provides all the information necessary to describe RESTful or practically RESTful APIs and also talk about the importance of API design and development with RAML.

    • 1:27:44

  • In this lecture session we learn about RAML stands for RESTful API Modeling Language. It's a way of describing practically-RESTful APIs in a way that's highly readable by both humans and computers. We say "practically RESTful" because, today in the real world, very few APIs today actually obey all constraints of REST.

    • 2:07:24

  • In this lecture session we learn about RAML (RESTful API Modeling Language) provides a structured, unambiguous format for describing a RESTful API. It allows you to describe your API; the endpoints, the HTTP methods to be used for each one, any parameters and their format, what you can expect by way of a response and more.

    • 47:27

  • In this lecture session we learn about The RAML specification (this document) defines an application of the YAML 1.2 specification that provides mechanisms for the definition of practically-RESTful APIs, while providing provisions with which source code generators for client and server source code and comprehensive user documentation can be created.

    • 1:46:59

  • In this tutorial we learn about RESTful API Modeling Language (RAML) is a YAML-based language for describing RESTful APIs. It provides all the information necessary to describe RESTful or practically RESTful APIs.

    • 50:42

  • In this lecture session we learn about API is the acronym for Application Programming Interface, which is a software intermediary that allows two applications to talk to each other.

    • 42:31

  • In this lecture session we learn about RAML can be used in a multitude of ways: to implement interactive PAI consoles, generate documentation, describing an API you are planning to build, and more. Despite the name, RAML can describe APIs that do not follow all of the REST rules (hence why it's referred to as "practically RESTful").

    • 53:46

  • In this lecture session we learn about API architecture refers to the process of developing a software interface that exposes backend data and application functionality for use in new applications.

    • 2:09:27

  • In this lecture session we learn about RAML (RESTful API Modeling Language) provides a structured, unambiguous format for describing a RESTful API. It allows you to describe your API; the endpoints, the HTTP methods to be used for each one, any parameters and their format, what you can expect by way of a response and more.

    • 47:18

  • In this session we learn about RESTful API Modeling Language (RAML) is a YAML-based language for describing RESTful APIs. It provides all the information necessary to describe RESTful or practically RESTful APIs.

    • 36:21

  • In this lecture session we learn about RAML libraries that may be used to modularize any number and combination of data types, security schemes, resource types, traits, and annotations.

    • 30:13

  • In this lecture session we learn about API fragments that are reusable components of RAML to make the design and build of a reusable API even quicker and easier. Another advantage of building an API spec out of reusable API fragments is that consistency of definitions reduces the effort of implementing APIs.

    • 39:57

  • In this tutorial we learn about The RAML type system borrows from object oriented programming languages such as Java, as well as from XML Schema (XSD) and JSON Schema. RAML Types in a nutshell: Types are similar to Java classes. Types borrow additional features from JSON Schema, XSD, and more expressive object oriented languages

    • 38:27

  • In this lecture session we learn about Properties is nothing but in terms of JAVA ,Its Object Oriented Name. But Facet is nothing but More information about Property like MinLength,MaxLength,Minimum and Maximum and many more what you have said as well.

    • 1:26:47

  • In this lecture session we learn about how API fragments are reusable components of RAML to make the design and build of a reusable API even quicker and easier. Another advantage of building an API spec out of reusable API fragments is that consistency of definitions reduces the effort of implementing APIs.

    • 1:22:25

  • In this lecture session we learn that RAML belongs to the "API Tools" category of the tech stack, while YAML can be primarily classified under "Languages". According to the StackShare community, RAML has a broader approval, being mentioned in 9 company stacks & 6 developers stacks; compared to YAML, which is listed in 5 company stacks and 4 developer stacks.

    • 08:41

  • In this lecture session we learn about The WSDL document represents a contract between API providers and API consumers. RAML is a modern WSDL counterpart specifically for REST APIs. The RAML Spec is an open standard that was developed by the RAML workgroup and with support from MuleSoft.

    • 1:35:09

  • In this lecture session we learn about RAML to HTML is a documentation tool that outputs a single HTML page console based on a RAML definition. It's written in NodeJS and it can be executed as a command line.

    • 1:42:02

  • In this lecture session we learn about A resource node is one that begins with the slash and is either at the root of the API definition or a child of a resource node.

    • 1:20:44

  • In this lecture session we learn about RAML stands for RESTful API Modeling Language. It's a way of describing practically-RESTful APIs in a way that's highly readable by both humans and computers. We say "practically RESTful" because, today in the real world, very few APIs today actually obey all constraints of REST.

    • 37:40

  • In this lecture session we learn about RAML is a Rest API Modeling Language and it is based on YAML for describing your API's. It is basically used to describe your API, which can be easily readable by humans and computers.

    • 10:00

  • In this lecture session we learn about The baseURI im raml definition is a optional field that serves initially to identify the endpoint of the resources you will describe in the raml definition of a api. The baseURI may also be used to specify the URL at which the api is served.

    • 1:32:24

  • In this lecture session we learn about RAML stands for RESTful API Modeling Language. It's a way of describing practically RESTful APIs in a way that's highly readable by both humans and computers. It is a vendor-neutral, open-specification language built on YAML 1.2 and JSON for describing RESTful APIs.

    • 1:00:04

  • In this lecture session we learn about RESTful API Modeling Language (RAML) makes it easy to manage the API lifecycle from design to deployment to sharing. It's concise and reusable; you only have to write what you need to define and you can use it again and again. Uniquely among API specs, it was developed to model an API, not just document it.

    • 1:35:58

  • In this lecture session we learn about The WSDL document represents a contract between API providers and API consumers. RAML is a modern WSDL counterpart specifically for REST APIs. The RAML Spec is an open standard that was developed by the RAML workgroup and with support from MuleSoft.

    • 1:54:39

  • In this tutorial we learn about The RAML specification (this document) defines an application of the YAML 1.2 specification that provides mechanisms for the definition of practically-RESTful APIs, while providing provisions with which source code generators for client and server source code and comprehensive user documentation can be created.

    • 1:03:19

  • In this lecture session we learn about A string is a data type used in programming, such as an integer and floating point unit, but is used to represent text rather than numbers. It consists of a set of characters that can also contain spaces and numbers.

    • 16:09

  • In this RAML is used to design and manage the whole REST API lifecycle. MULE API Kit: Helps to build the APIs from Anypoint Studio using a RAML file. I will be explaining the generating flows from the RAML file and executing it.

    • 3:08:57

  • In this lecture session we learn about APIs (application programming interfaces) are simply communication tools for software applications. APIs are leading to key advances within the banking industry as financial institutions continue to collaborate with third parties.

    • 5:49:34

  • In this lecture session we learn about Music (alternatively called the Music app; formerly iPod) is a media player application developed for the iOS, iPadOS, tvOS, watchOS, and macOS operating systems by Apple Inc.

    • 3:45:19

  • In this lecture session we learn about An API application program interface is used in mobile apps just like it is in web apps. It allows developers to access another application or platform. APIs are the foundational element of a mobile app strategy.

    • 1:38:45
Course Objectives Back to Top

This course is designed to equip learners with the essential knowledge and technical expertise required to become proficient LLM developers with a strong foundation in AI, data science, and machine learning using Python. Through practical projects and real-world applications, learners will develop skills that are immediately applicable across industries.

By the end of this course, learners will be able to:

● Write clean and efficient Python code for data analysis, machine learning, and LLM development.
● Manipulate, analyze, and visualize data using Pandas, NumPy, and Matplotlib.
● Apply core ML techniques such as regression, classification, clustering, and model evaluation using scikit-learn.
● Understand key NLP concepts and implement them using spaCy, NLTK, and Hugging Face Transformers.
● Fine-tune and deploy transformer-based models such as BERT, GPT, and T5.
● Create LLM-powered applications using LangChain, OpenAI API, and vector databases like FAISS and Pinecone.
● Generate, store, and use embeddings for semantic search and retrieval-augmented generation (RAG).
● Build interactive AI applications using Streamlit, Gradio, and FastAPI.
● Deploy production-ready AI applications using Docker, GitHub Actions, and cloud platforms.
● Implement responsible AI practices, including bias detection, explainability, and ethical AI design.
● Prepare for careers in AI and LLM development through capstone projects and real-world applications.

 
 
Course Syllabus Back to Top

LLM Developer – Course Syllabus

Artificial Intelligence, Data Science & Machine Learning with Python

1. Introduction to AI and LLMs

  • Overview of Artificial Intelligence and Machine Learning
  • Evolution and use cases of Large Language Models (LLMs)
  • Tools setup: Python, Jupyter, Anaconda, VS Code
  • Understanding LLM developer career paths
  • Introduction to OpenAI, Hugging Face, and modern AI platforms

2. Python for Data Science

  • Python syntax and data structures (lists, dicts, sets, tuples)
  • Functions, loops, and control flow
  • Working with NumPy and Pandas
  • Data visualization with Matplotlib and Seaborn
  • File handling, exceptions, and working with APIs

3. Machine Learning Basics

  • Supervised vs unsupervised learning
  • Regression, classification, clustering algorithms
  • Model evaluation: accuracy, confusion matrix, precision/recall
  • Scikit-learn pipelines and preprocessing
  • Hyperparameter tuning and cross-validation

4. Deep Learning with TensorFlow & PyTorch

  • Neural network fundamentals (layers, activation, loss)
  • Building models with TensorFlow and Keras
  • Introduction to PyTorch: tensors, autograd, modules
  • Training and validating deep learning models
  • Model saving, loading, and versioning

5. Natural Language Processing (NLP)

  • Text preprocessing: tokenization, stopwords, stemming, lemmatization
  • Word embeddings: Bag of Words, TF-IDF, Word2Vec
  • Named Entity Recognition, sentiment analysis, text classification
  • RNNs, LSTMs, GRUs overview
  • Dataset handling with NLTK and spaCy

6. Transformers and LLMs

  • Transformer architecture: attention mechanism, encoder-decoder
  • Introduction to BERT, GPT, T5, and LLaMA
  • Text generation, summarization, translation use cases
  • Using Hugging Face Transformers
  • Comparing fine-tuning vs prompt-based learning

7. Prompt Engineering and Fine-Tuning

  • Prompt templates and few-shot learning
  • Designing effective prompts for real-world use cases
  • Fine-tuning LLMs with domain-specific datasets
  • Evaluating and optimizing model output quality
  • Use cases: chatbots, auto-reply, content creation

8. LLM Application Development

  • Integrating OpenAI and Hugging Face APIs
  • LangChain framework basics
  • Vector databases: FAISS, Pinecone, Chroma
  • Building document-based Q&A systems and assistants
  • Handling embeddings, context windows, and session memory

9. Model Deployment and MLOps

  • Deploying models with Flask and FastAPI
  • Dockerizing ML applications
  • Hosting on cloud: AWS, Azure, or GCP basics
  • Introduction to CI/CD and ML model monitoring
  • Managing updates, rollback, and scalability

10. Ethics and Responsible AI

  • Ethical challenges: bias, fairness, privacy
  • AI safety, prompt filtering, and response moderation
  • GDPR compliance and data security
  • Explainability and model transparency
  • Responsible usage of public LLM APIs

11. Final Project & Portfolio Building

  • Build a real-world LLM solution: chatbot, AI tool, or mini app
  • Apply Python, ML, LLM integration, and deployment
  • Include error handling, testing, and documentation
  • Project code review and best practices
  • Career prep: portfolio showcase, mock interviews, and LLM coding challenges
Certification Back to Top

Upon successful completion of the LLM Developer course, you will receive a Certificate of Completion from Uplatz, which serves as a formal acknowledgment of your skills in AI, machine learning, and LLM development with Python.

This certificate demonstrates your ability to work across the full AI development lifecycle—from data analysis and model training to deploying real-world AI applications. It highlights your expertise in using tools such as Scikit-learn, TensorFlow, PyTorch, FastAPI, Hugging Face Transformers, and various cloud platforms.

You can showcase this certificate on your LinkedIn profile, résumé, job applications, or personal portfolio. It is particularly valuable when applying for roles like Machine Learning Engineer, Data Scientist, AI Developer, or NLP Engineer.

In addition to being a strong standalone credential, the course also prepares you for further certifications from organizations such as Google Cloud, AWS, Microsoft Azure, and Hugging Face. These external certifications can be pursued after completing this course as a next step in your AI career.

Earning this certificate affirms that you possess both the theoretical foundation and the practical skills required to build intelligent, production-ready AI systems using modern tools and methodologies.

 
 
Career & Jobs Back to Top

LLM development is one of the fastest-growing career paths in the tech industry today. With organizations rapidly integrating AI into their products and services, the demand for professionals who understand and can work with large language models is surging across sectors.

This course prepares you to confidently pursue various in-demand roles, including:

● LLM Developer
● AI Engineer
● NLP Engineer
● Machine Learning Engineer
● Data Scientist
● Applied Scientist (AI/ML)
● Research Engineer (NLP/LLMs)

Industries hiring for these roles include:

● Technology and SaaS companies
● Healthcare and Life Sciences
● Financial Services and FinTech
● Retail and E-commerce
● Government and Public Sector
● Education and EdTech

With your project portfolio, certificate, and up-to-date skills, you’ll be ready to stand out in technical interviews and deliver real-world impact as an AI developer. Whether you’re transitioning into AI or advancing within the field, this course offers the tools and knowledge to unlock the next phase of your career.

 
 
Interview Questions Back to Top
  1. What is the difference between supervised and unsupervised learning?
     Supervised learning uses labeled data to train models, while unsupervised learning identifies patterns in unlabeled data.

  2. Explain overfitting in machine learning.
     Overfitting occurs when a model learns noise and details in the training data, reducing its generalization to new data.

  3. What is a Transformer model?
     Transformers use self-attention mechanisms for sequential data processing and are foundational for models like GPT and BERT.

  4. How do you deploy an ML model?
     Models can be deployed using APIs (FastAPI), containerization (Docker), and cloud platforms (AWS Sagemaker, GCP AI Platform).

  5. What are embeddings in NLP?
     Embeddings represent words, sentences, or documents as dense vectors capturing semantic meaning.

  6. How do you evaluate a classification model?
     Metrics include accuracy, precision, recall, F1-score, and ROC-AUC.

  7. What is fine-tuning in the context of LLMs?
     Fine-tuning is the process of adapting a pre-trained LLM to a specific task or dataset.

  8. What is the difference between AI, ML, and Deep Learning?
     AI is a broader concept; ML is a subset of AI focusing on learning from data; deep learning is a subset of ML using neural networks.

  9. What is MLOps?
     MLOps is a set of practices to automate and monitor the lifecycle of machine learning models in production.

  10. Name a use case for LLMs.
     Use cases include chatbots, code generation, summarization, translation, and question answering.
Course Quiz Back to Top
Start Quiz
Q1. What are the payment options?
A1. We have multiple payment options: 1) Book your course on our webiste by clicking on Buy this course button on top right of this course page 2) Pay via Invoice using any credit or debit card 3) Pay to our UK or India bank account 4) If your HR or employer is making the payment, then we can send them an invoice to pay.

Q2. Will I get certificate?
A2. Yes, you will receive course completion certificate from Uplatz confirming that you have completed this course with Uplatz. Once you complete your learning please submit this for to request for your certificate https://training.uplatz.com/certificate-request.php

Q3. How long is the course access?
A3. All our video courses comes with lifetime access. Once you purchase a video course with Uplatz you have lifetime access to the course i.e. forever. You can access your course any time via our website and/or mobile app and learn at your own convenience.

Q4. Are the videos downloadable?
A4. Video courses cannot be downloaded, but you have lifetime access to any video course you purchase on our website. You will be able to play the videos on our our website and mobile app.

Q5. Do you take exam? Do I need to pass exam? How to book exam?
A5. We do not take exam as part of the our training programs whether it is video course or live online class. These courses are professional courses and are offered to upskill and move on in the career ladder. However if there is an associated exam to the subject you are learning with us then you need to contact the relevant examination authority for booking your exam.

Q6. Can I get study material with the course?
A6. The study material might or might not be available for this course. Please note that though we strive to provide you the best materials but we cannot guarantee the exact study material that is mentioned anywhere within the lecture videos. Please submit study material request using the form https://training.uplatz.com/study-material-request.php

Q7. What is your refund policy?
A7. Please refer to our Refund policy mentioned on our website, here is the link to Uplatz refund policy https://training.uplatz.com/refund-and-cancellation-policy.php

Q8. Do you provide any discounts?
A8. We run promotions and discounts from time to time, we suggest you to register on our website so you can receive our emails related to promotions and offers.

Q9. What are overview courses?
A9. Overview courses are 1-2 hours short to help you decide if you want to go for the full course on that particular subject. Uplatz overview courses are either free or minimally charged such as GBP 1 / USD 2 / EUR 2 / INR 100

Q10. What are individual courses?
A10. Individual courses are simply our video courses available on Uplatz website and app across more than 300 technologies. Each course varies in duration from 5 hours uptop 150 hours. Check all our courses here https://training.uplatz.com/online-it-courses.php?search=individual

Q11. What are bundle courses?
A11. Bundle courses offered by Uplatz are combo of 2 or more video courses. We have Bundle up the similar technologies together in Bundles so offer you better value in pricing and give you an enhaced learning experience. Check all Bundle courses here https://training.uplatz.com/online-it-courses.php?search=bundle

Q12. What are Career Path programs?
A12. Career Path programs are our comprehensive learning package of video course. These are combined in a way by keeping in mind the career you would like to aim after doing career path program. Career path programs ranges from 100 hours to 600 hours and covers wide variety of courses for you to become an expert on those technologies. Check all Career Path Programs here https://training.uplatz.com/online-it-courses.php?career_path_courses=done

Q13. What are Learning Path programs?
A13. Learning Path programs are dedicated courses designed by SAP professionals to start and enhance their career in an SAP domain. It covers from basic to advance level of all courses across each business function. These programs are available across SAP finance, SAP Logistics, SAP HR, SAP succcessfactors, SAP Technical, SAP Sales, SAP S/4HANA and many more Check all Learning path here https://training.uplatz.com/online-it-courses.php?learning_path_courses=done

Q14. What are Premium Career tracks?
A14. Premium Career tracks are programs consisting of video courses that lead to skills required by C-suite executives such as CEO, CTO, CFO, and so on. These programs will help you gain knowledge and acumen to become a senior management executive.

Q15. How unlimited subscription works?
A15. Uplatz offers 2 types of unlimited subscription, Monthly and Yearly. Our monthly subscription give you unlimited access to our more than 300 video courses with 6000 hours of learning content. The plan renews each month. Minimum committment is for 1 year, you can cancel anytime after 1 year of enrolment. Our yearly subscription gives you unlimited access to our more than 300 video courses with 6000 hours of learning content. The plan renews every year. Minimum committment is for 1 year, you can cancel the plan anytime after 1 year. Check our monthly and yearly subscription here https://training.uplatz.com/online-it-courses.php?search=subscription

Q16. Do you provide software access with video course?
A16. Software access can be purchased seperately at an additional cost. The cost varies from course to course but is generally in between GBP 20 to GBP 40 per month.

Q17. Does your course guarantee a job?
A17. Our course is designed to provide you with a solid foundation in the subject and equip you with valuable skills. While the course is a significant step toward your career goals, its important to note that the job market can vary, and some positions might require additional certifications or experience. Remember that the job landscape is constantly evolving. We encourage you to continue learning and stay updated on industry trends even after completing the course. Many successful professionals combine formal education with ongoing self-improvement to excel in their careers. We are here to support you in your journey!

Q18. Do you provide placement services?
A18. While our course is designed to provide you with a comprehensive understanding of the subject, we currently do not offer placement services as part of the course package. Our main focus is on delivering high-quality education and equipping you with essential skills in this field. However, we understand that finding job opportunities is a crucial aspect of your career journey. We recommend exploring various avenues to enhance your job search:
a) Career Counseling: Seek guidance from career counselors who can provide personalized advice and help you tailor your job search strategy.
b) Networking: Attend industry events, workshops, and conferences to build connections with professionals in your field. Networking can often lead to job referrals and valuable insights.
c) Online Professional Network: Leverage platforms like LinkedIn, a reputable online professional network, to explore job opportunities that resonate with your skills and interests.
d) Online Job Platforms: Investigate prominent online job platforms in your region and submit applications for suitable positions considering both your prior experience and the newly acquired knowledge. e.g in UK the major job platforms are Reed, Indeed, CV library, Total Jobs, Linkedin.
While we may not offer placement services, we are here to support you in other ways. If you have any questions about the industry, job search strategies, or interview preparation, please dont hesitate to reach out. Remember that taking an active role in your job search process can lead to valuable experiences and opportunities.

Q19. How do I enrol in Uplatz video courses?
A19. To enroll, click on "Buy This Course," You will see this option at the top of the page.
a) Choose your payment method.
b) Stripe for any Credit or debit card from anywhere in the world.
c) PayPal for payments via PayPal account.
d) Choose PayUmoney if you are based in India.
e) Start learning: After payment, your course will be added to your profile in the student dashboard under "Video Courses".

Q20. How do I access my course after payment?
A20. Once you have made the payment on our website, you can access your course by clicking on the "My Courses" option in the main menu or by navigating to your profile, then the student dashboard, and finally selecting "Video Courses".

Q21. Can I get help from a tutor if I have doubts while learning from a video course?
A21. Tutor support is not available for our video course. If you believe you require assistance from a tutor, we recommend considering our live class option. Please contact our team for the most up-to-date availability. The pricing for live classes typically begins at USD 999 and may vary.
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