Corporate Training

Artificial Intelligence

Course ID
AI
Department
Cloud
Campus
1 Cornhill
Level
Certificate
Method
Lecture + Project
Duration
3 Months
Software Manual Testing
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Optional Add-on Programs

Job Guarantee Program

The Job Guarantee program is available only to candidates who enroll in Option 6 (Project and Industrial Training and Paid Internship Program + Pro Plan). It is important to note, however, that the Job Guarantee program has its own selection criteria, so not everyone may be considered for the program. To learn more about the Job Guarantee program, please visit Job Guaranteed Software Courses

Pro Plan Card

LSET PRO PLAN

Are you eager to enter the workforce fully prepared? Look no further than our LSET PRO PLAN! This is an add-on program that you can select during your course enrolment, it offers a personalised learning experience that helps you succeed in your course, build your technical portfolio, and advance your professional journey.
Curious about how to embark on this journey? Simply “click” here to learn more and kickstart your professional development with us!

The Artificial Intelligence Course provides comprehensive training on core concepts in artificial intelligence. This course will allow students to explore real-life industry scenarios and use cases, allowing them to enhance their knowledge of artificial intelligence. By enrolling in this hands-on training program, you will acquire the skills necessary to become proficient in artificial intelligence.

Become a professional in artificial intelligence by applying now

Are you looking for corporate training?
We tailor our courses to meet the specific needs of your team. If you would like to discuss your training requirements, please email [email protected] today.		 Explore Now
Course feature icons
Options
Topic
Add-On
Duration
Options
Option 1
Topic
Artificial Intelligence
Add-On
 
Duration
3 Months
Options
Option 2
Topic
Artificial Intelligence
Add-On
Project (Online)
Duration
5 Months
Options
Option 3
Topic
Artificial Intelligence
Add-On
Project (Online) &
Industrial Training and Paid Internship Program (Remote)
Duration
12 Months

Tuition Fees

Options
Course Pack
Home (UK) & International
Online
Home (UK)
Classroom
International
Classroom
Options
Option 1
Course Pack
Artificial Intelligence – (3 Months)
Home & International Online
Pay Upfront (with 20% Disc): £1,860
Pay Per Module:
Number of Module: 2
Per Module Fee: £1,116
Home Classroom
Pay Upfront (with 20% Disc): £3,660
Pay Per Module:
Number of Module: 2
Per Module Fee: £2,196
International Classroom
£6,060
Options
Option 2
Course Pack
Artificial Intelligence + Project (Online) – (5 Months)
Home & International Online
Pay Upfront (with 20% Disc): £3,060
Pay Per Module:
Number of Module: 3
Per Module Fee: £1,224
Home Classroom
Pay Upfront (with 20% Disc): £6,060
Pay Per Module:
Number of Module: 3
Per Module Fee: £2,424
International Classroom
£8,460
Options
Option 3
Course Pack
Artificial Intelligence + Project (Online) + Industrial Training and Paid Internship Program (Remote) – (12 Months)
Home & International Online
Pay Upfront (with 20% Disc): £6,060
Pay Per Module:
Number of Module: 6
Per Module Fee: £1212
Home Classroom
Pay Upfront (with 20% Disc): £13,260
Pay Per Module:
Number of Module:6
Per Module Fee: £2,652
International Classroom
£15,660
Options
Option 4
Course Pack
Artificial Intelligence + Pro Plan
Home & International Online
Pay Upfront (with 20% Disc): £2,340
Pay Per Module:
Number of Module: 2
Per Module Fee: £1,404
Home Classroom
Pay Upfront (with 20% Disc): £4,140
Pay Per Module:
Number of Module: 2
Per Module Fee: £2,484
International Classroom
£6,540
Options
Option 5
Course Pack
Artificial Intelligence + Project (Online) + Pro Plan
Home & International Online
Pay Upfront (with 20% Disc): £3,540
Pay Per Module:
Number of Module: 3
Per Module Fee: £1,416
Home Classroom
Pay Upfront (with 20% Disc): £6,540
Pay Per Module:
Number of Module: 3
Per Module Fee: £2,616
International Classroom
£8,940
Options
Option 6
Course Pack
Artificial Intelligence + Project (Online) + Industrial Training and Paid Internship Program (Remote) + Pro Plan
Home & International Online
Pay Upfront (with 20% Disc): £6,540
Pay Per Module:
Number of Module: 6
Per Module Fee: £1,308
Home Classroom
Pay Upfront (with 20% Disc): £13,740
Pay Per Module:
Number of Module: 6
Per Module Fee: £2,748
International Classroom
£16,140
   Note: Our Industrial Training and Internship program includes a guaranteed 6 months paid internship (from 10 hours to 40 hours per week) with a technology company. Due to visa restrictions, some international students may not be able to participate in this program.

Our Artificial Intelligence Course offers a comprehensive learning experience that delves into the applications of AI in fundamental sciences, providing a platform for students to gain practical insights and skills in this dynamic field. The course encompasses various topics, including the evolution of AI, its impact on multiple industries, ethical considerations, and hands-on training in key AI concepts and technologies.

This ensures a well-rounded understanding of AI and its practical applications. The program offers a blend of recorded lectures, real-life case studies, interactive quizzes, and mentor-led sessions To furnish participants with foundational knowledge and skills—expertise required to navigate the intricate landscape of artificial intelligence.

Technologies Covered

The course encompasses various emerging technologies including artificial intelligence, machine learning, cybersecurity, cloud computing, big data, internet of things (IoT), virtual reality, augmented reality, blockchain, and digital privacy.

Specifically, the course covers topics such as machine learning concepts, natural language processing (NLP), applications of AI in healthcare and finance, autonomous systems, edge AI, cybersecurity, and the ethical implications and biases in AI.

The course also explores the technological scenario of AI from an IT perspective, emphasising machine learning and deep learning solutions for IoT and edge computing.

Furthermore, the program delves into practical applications of AI, such as using machine learning tools to improve workplace efficiency, the increased use of autonomous vehicles, smart home devices, and generative AI tools like ChatGPT.

Furthermore, students will acquire valuable insights into how AI influences diverse sectors such as healthcare, finance, transportation, and beyond.

The curriculum also emphasises the ethical implications of AI and the latest trends in the field.

Complementary Workshops

Git Management

Learn More
Agile Project Management

Agile Project Management

Learn More

Team Building

Learn More

Personality Development

Learn More

Interview Preparation

Learn More

Course Information

Course Intakes

September

End: December

January

End: April

May

End: August

Entry Criteria

  • Ability to work in Group
  • If a potential student’s first language is not English, they must also reach the English Language requirements of either any one of the following - IELTS 5.5 or NCC Test or GCE “O” Level English C6.
  • Have access to personal laptop

Course Highlights

  • Hands-on Sessions
  • Project-based Learning
  • Live or Offline Capstone Project
  • Real world development experience
  • Industry Mentors
  • Interactive Teaching Methodologies

Evaluation Criteria

  • 18 Coding exercises
  • 5 Assignments
  • 5 Quizzes
  • Capstone Project
  • Group activities
  • Presentations

Learning Objectives

  • Apply AI techniques to real-world problems and scenarios.
  • Demonstrate problem-solving and critical thinking skills in developing AI solutions.
  • Create intelligent systems by constructing solutions for specific computational challenges.
  • Analyze and evaluate the ethical implications and societal impacts of artificial intelligence.
  • Acquire data manipulation, analysis, and visualization skills using relevant libraries and tools.
  • Demonstrate proficiency in programming languages commonly used in AI, such as Python, R, or Java.
  • Develop an interest in the field sufficient to pursue further advanced studies or professional applications.
  • Gain an understanding of the frameworks in which artificial intelligence and the Internet of Things function.
  • Develop the ability to design user interfaces to improve human-AI interaction and real-time decision-making.
  • Explain the basic knowledge representation, problem-solving, and learning methods of Artificial Intelligence.
  • Generate an interest in the field significant enough to pursue advanced subjects, as indicated by the exit survey.
  • Comprehend the significance of knowledge representation, problem-solving, and learning in intelligent systems engineering.
  • In practical applications, utilize machine learning algorithms and techniques, including regression, classification, clustering, and neural networks.
  • Evaluate the relevance, advantages, and drawbacks of the fundamental knowledge representation—problem-solving and learning methods in solving particular engineering problems.

Weekday Batches

Batch 01
09:00 am – 11:00 am
(Tue, Thu)

Batch 02
12:00 pm – 02:00 pm
(Tue, Thu)

Batch 03
03:00 pm – 05:00 pm
(Tue, Thu)

Batch 04
05:30 pm – 07:30 pm
(Tue, Thu)

Weekend Batches

Batch 01
08:00 am – 10:00 am
(Sat, Sun)

Batch 02
10:00 am – 12:00 pm
(Sat, Sun)

Hands-on Workshops

Interview Preparation

CV Preparation

Personality Development

Join Now

Join our Artificial Intelligence Course to build a solid foundation in machine learning, increase job opportunities, and gain practical experience in AI project creation. Take advantage of this opportunity to immerse yourself in the world of artificial intelligence with LSET.

Apply Now
 

Course Content

Browse the LSET interactive and practical curriculum

Introduction to Python

>> Python Basics >> Python Functions and Packages >> Working with Data Structures, Arrays, Vectors & Data Frames >> Jupyter Notebook – Installation & Function
>> Pandas, NumPy, Matplotlib, Seaborn

Applied Statistics

>> Descriptive Statistics >> Probability & Conditional Probability >> Hypothesis Testing >> Inferential Statistics
>> Probability Distributions

Supervised Learning

>> Linear Regression >> Multiple Variable Linear Regression >> Logistic Regression >> Naive Bayes Classifiers
>> K-NN Classification >> Support Vector Machines

Ensemble Techniques

>> Decision Trees >> Bagging >> Random Forests >> Boosting

Unsupervised Learning

>> K-means Clustering >> Hierarchical Clustering >> Dimension Reduction-PCA

Featurisation, Model Selection & Tuning

>> Feature Engineering >> Model Selection and Tuning >> Model Performance Measures >> Regularising Linear Models
>> MI Pipeline >> Bootstrap Sampling >> Grid Search CV >> Randomised Search CV
>> K Fold Cross-Validation

Introduction to SQL

>> Introduction To DBMS >> ER Diagram >> Schema Design >> Key Constraints and Basics of Normalization
>> Joins >> Subqueries Involving Joins and Aggregations >> Sorting >> Independent Subqueries
>> Correlated Subqueries >> Analytic Functions >> Set Operations >> Grouping and Filtering

Introduction to Neural Networks and Deep Learning

>> Introduction to Perceptron & Neural Networks >> Activation and Loss functions >> Gradient Descent >> Batch Normalization
>> TensorFlow & Keras for Neural Networks >> Hyper Parameter Tuning

Computer Vision

>> Introduction to Convolutional Neural Networks >> Introduction to Images >> Convolution, Pooling, Padding & its Mechanisms >> Forward Propagation & Backpropagation for CNNs
>> CNN architectures like AlexNet, VGGNet, InceptionNet & ResNet >> Transfer Learning >> Object Detection >> YOLO, R-CNN, SSD
>> Semantic Segmentation >> U-Net >> Face Recognition Using Siamese Networks >> Instance Segmentation

NLP (Natural Language Processing)

>> Introduction to NLP >> Stop Words >> Tokenization >> Stemming and Lemmatization
>> Bag of Words Model >> Word Vectorizer >> TF-IDE >> POS Tagging
>> Named Entity Recognition >> Introduction to Sequential Data >> RNNs and its Mechanisms >> Vanishing & Exploding gradients in RNNs
>> LSTMs – Long Short-Term Memory >> GRUS – Gated Recurrent Unit >> LSTMs Applications >> Time Series Analysis
>> LSTMs with Attention Mechanism >> Neural Machine Translation >> Advanced Language Models: Transformers, BERT, XLNet

Demystifying ChatGPT and its Applications

>> Overview of ChatGPT and OpenAl >> Timeline of NLP and Generative Al >> Frameworks for Understanding ChatGPT and Generative Al >> Implications for Work, Business, and Education
>> Output Modalities and Limitations >> Business Roles to Leverage ChatGPT >> Prompt Engineering for Fine-Tuning Outputs >> Practical Demonstration and Bonus Section on RLHF

ChatGPT: The Development Stack

>> Mathematical Fundamentals for Generative Al >> VAES: First Generative Neural Networks >> GANS: Photorealistic Image Generation >> Conditional GANs and Stable Diffusion
>> Transformer Models: Generative Al for Natural Language >> ChatGPT: Conversational Generative Al >> Hands-On ChatGPT Prototype Creation >> Next Steps for Further Learning and Understanding

Introduction to Generative Al

>> Al vs ML vs DL vs GenAl >> Supervised vs Unsupervised Learning >> Discriminative vs Generative Al >> A Brief Timeline of GenAl
>> Basics of Generative Models >> Large Language Models >> Word Vectors >> Attention Mechanism
>> Business Applications of ML, DL and GenAl >> Hands-on Bing Images and ChatGPT

Prompt Engineering 101 What is a prompt?

>> What is prompt engineering? >> Why is prompt engineering significant? >> How are Outputs from LLMs Guided by Prompts? >> Limitations and Challenges with LLMs
>> Broad Strategies for Prompt Design >> – Template-Based Prompts >> – Fill in the blanks Prompts >> – Multiple Choice Prompts
>> – Instructional Prompts >> – Iterative prompts >> – Ethically Aware Prompt >> Best Practices for Effective Prompt Design

*Modules of our curriculum are subject to change. We update our curriculum based on the new releases of the libraries, frameworks, Software, etc. Students will be informed about the final curriculum in the course induction class.

Having Doubts?

Contact LSET Counsellor

We love to answer questions, empower students, and motivate professionals. Feel free to fill out the form and clear up your doubts related to our Artificial Intelligence Course.

Best Career Paths

Machine Learning Engineer

Responsibilities include developing and deploying machine learning models, conducting data analysis, and optimizing algorithms for specific applications.

Data Scientist

Data scientists analyze and interpret complex datasets to extract valuable insights. They frequently leverage machine learning methodologies to construct models that can make predictions.

AI Research Scientist

Researchers in AI focus on advancing the field by conducting experiments, proposing new algorithms, and contributing to academic or industrial research.

Computer Vision Engineer

Computer vision engineers work on applications related to image and video analysis, such as facial recognition, object detection, and autonomous vehicles.

AI Software Developer

AI software developers write the code that powers AI applications, integrating machine learning models into software solutions.

Robotics Engineer

Robotics engineers design and build robotic systems, incorporating AI to enable machines to perceive and interact with their environment.

Top Companies Hiring

Google

GOOGLE

American Express

AMERICAN EXPRESS

Barclays

BARCLAYS

SAP

SAP

Microsoft

MICROSOFT

Accenture

ACCENTURE

EY

EY

DBS

DBS BANK

Faculties & Mentors

Mayur Ramgir

Mayur Ramgir

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Bruno LSET Mentor

Bruno Bossola

View Profile

Course Overview

The course is a comprehensive Artificial Intelligence (AI) program that covers a wide array of foundational and advanced topics, providing a deep understanding of the core competencies of AI. It encompasses various modules and subjects, spanning from fundamental concepts to advanced applications, and is designed to cater to individuals with diverse skill levels and backgrounds. Here's an overview of the course content:

Foundational Modules

Pre-Work: Math for AI and Statistics Refresher: Revisits essential mathematical concepts and statistical principles crucial for effectively understanding and applying AI algorithms.

Programming with Python: Delves into Python, a widely used language in AI and data science, covering essential programming skills and data manipulation.

Data Structures and Algorithms: Explores critical data structures and algorithms essential for efficient problem-solving and building effective AI applications.

Machine Learning: Introduces key machine learning concepts, techniques, and models, including regression, classification, and probability theory.

Advanced Machine Learning: Provides a deep dive into advanced machine learning concepts, including decision trees, ensemble methods, and feature engineering.

Specialised Modules

Natural Language Processing (NLP): Offers insights into computer and human language interaction, covering applications like chatbot development and sentiment analysis.

Computer Vision (CV): Concentrates on empowering machines to understand and interpret visual data, covering areas such as image classification and recognition.

Additional Modules and Subjects

Ethical Implications of AI: Explores ethical concerns, bias, and regulations surrounding AI, fostering responsible and ethical AI development and deployment.

Industry Applications and Use Cases: Showcases real-world applications of AI in diverse domains, including impactful examples of AI technologies.

The course is designed to be accessible to individuals with or without a technical background, offering a thorough introduction to AI concepts, terminology, and applications without requiring prior programming or computer science expertise. Throughout the program, learners can engage in practical demonstrations of AI through mini-projects, further solidifying their understanding of AI in action.

In addition to the core AI curriculum, the course offers specialized modules covering data science, machine learning models, deep learning, computer vision, and natural language processing. It also provides a comprehensive overview of AI’s ethical considerations and societal impacts, ensuring a well-rounded understanding of the field.

The course syllabus is meticulously crafted to prepare aspiring AI enthusiasts for successful careers by providing hands-on experience, foundational knowledge, and exposure to cutting-edge AI technologies.

Why learn Artificial Intelligence ?

Learning an Artificial Intelligence (AI) course offers numerous benefits and opportunities due to the increasing integration of AI technologies across various industries. Here are several compelling reasons to consider learning AI:
  • Career Opportunities: AI skills are in high demand, and learning AI can open up diverse Career paths in areas like data science, machine learning engineering, AI research, and more.
  • Industry Relevance: AI is transforming industries, including healthcare, finance, retail, and automotive sectors. Acquiring AI skills can make individuals highly relevant in these evolving industries.
  • Innovation and Problem-Solving: AI equips individuals with the ability to innovate and solve complex problems using data-driven approaches and predictive analytics.
  • Competitive Advantage: As AI becomes more prevalent, professionals with AI skills have a competitive edge in the job market and are often sought after by employers.
  • Future-Proofing Skills: With the increasing integration of AI technologies, learning AI can future-proof one's skill set and flexibility in a swiftly evolving job market.
  • Ethical Considerations: Understanding AI also involves understanding its moral implications and fostering responsible and ethical AI development and deployment.
  • Personal and Professional Development: Learning AI enhances critical thinking, analytical, and programming skills, contributing to personal and professional growth.
  • In summary, learning AI can lead to promising career prospects, provide a competitive advantage, and contribute to personal and professional development in an increasingly AI-driven world.

The Course Provides Shared Expertise by

LSET Trainers

LSET Trainers

Industry Experts

Industry Experts

Top Employers

Top Employers

Skills You will Gain

  • ChatGPT
  • Computer Vision
  • Prompt Engineering
  • Reinforcement Learning
  • Supervised and Unsupervised Learning
  • Explainable AI
  • Deep Learning
  • Speech Recognition
  • Machine Learning Algorithms
  • Model Training and Optimization
  • Statistics
  • Generative AI
  • Ensemble Methods
  • Natural Language Processing
  • Model Evaluation and Validation

Complete Learning Experience

This course provides a hands-on, guided learning experience to help you learn the fundamentals practically.
  • We constantly update the curriculum to include the latest releases and features.
  • We focus on teaching the industry's best practices and standards.
  • We let you explore the topics through guided hands-on sessions.
  • We provide industry professional mentor support to every student.
  • We give you an opportunity to work on real world examples.
  • Work with hands-on projects and assignments.
  • We help you build a technical portfolio that you can present to prospective employers.

Reasons to Choose LSET

  • Interactive live sessions by industry experts.
  • Practical classes with project-based learning with hands-on activities.
  • International learning platform to promote collaboration and teamwork.
  • Most up-to-date course curriculum based on current industry demand.
  • Gain access to various e-learning resources.
  • One-to-one attention to ensure maximum participation in the classes.
  • Lifetime career guidance to get the students employed in good companies.
  • Free lifetime membership to the LSET Alumni Club

What Will Be Your Responsibilities?

  • Work creatively in a problem-solving environment.
  • Ask questions and participate in class discussions.
  • Work on assignments and quizzes promptly.
  • Read additional resources on the course topics and ask questions in class.
  • Actively participate in team projects and presentations.
  • Work with the career development department to prepare for interviews
  • Respond promptly to the instructors, student service officers, career development officers, etc.
  • And most importantly, have fun while learning at LSET.
Your Responsibilities

How Does Project-Based Learning Work?

LSET project-based learning model allows students to work on real-world applications and apply their knowledge and skills gained in the course to build high-performing industry-grade applications. As part of this course, students learn agile project management concepts, tools, and techniques to work on the assigned project collaboratively. Each student completes project work individually but is encouraged to enhance their solution by collaborating with their teammates.

Following are the steps involved in the LSET’s project-based learning;

  1. Step 1: Project Idea Discussion

    In this step, students get introduced to the problem and develop a strategy to build the solution.

  2. Step 2: Build Product Backlog

    This step requires students to enhance the existing starter product backlog available in the project. This helps students to think about real-life business requirements and formulate them in good user stories.

  3. Step 3: Design Releases and Sprints

    In this step, students define software releases and plan sprints for each release. Students must go through sprint planning individually and learn about story points and velocity.

  4. Step 4: Unit and Integration Tests

    In this step, students learn to write unit tests to ensure every application part works fine.

  5. Step 5: Use CICD to Deploy

    In this step, students learn to use CICD (Continuous Integration Continuous Delivery) pipeline to build their application as a docker image and deploy it to Kubernetes.

Capstone Project

LSET gives you an opportunity to work on the real world project which will greatly help you to build your technical portfolio

Project Topic: Online Banking

London has been a leading international financial centre since the 19th century. In recent years, London has seen many FinTech start-ups and significant innovations in the banking sector. This project aims to introduce students to the financial industry and technologies used to handle billions of daily transactions. As part of this project, students will learn the current technological advances and build up their knowledge to start a simple banking application. This application uses agile project management practices to build basic functionality. Students will be presented with user stories to create the initial project backlog. Students need to enhance this backlog by adding more relevant user stories and working on them.

LSET emphasises project-based learning as it allows the students to master the course content by going through near real-world work experience. LSET projects are carefully designed to teach the industry-required skills and mindset. It motivates the students on various essential aspects like learning to work in teams, improving communication with peers, taking the initiative to look for innovative solutions, enhancing problem-solving skills, understanding the end user requirements to build user-specific products, etc.

Capstone Projects build students’ confidence in handling projects and applying their newly learned skills to solve real-world problems. This allows the students to reflect upon their learning and find the opportunity to get the most out of the course. Learn more about Capstone Projects here.

Learning Outcome

  • Students will learn to work in an agile environment
  • Students will learn the agile project management terms used in the industry, like product backlog, user stories, story points, epics, etc.
  • Students will learn to use a Git repository and understand the concepts like commit, pull, push, branch, etc.
  • Students will learn to communicate in a team environment and effectively express their ideas.

Guidance and Help

A dedicated project coordinator who can mentor students on the process will be assigned to this project. Students can also avail of the instructor’s hours as and when needed. LSET may get an industry expert with subject-specific experience to help students understand the industry and its challenges.

Execution Process

This project will be carried out in steps. Each step teaches students a specific aspect of the subject and development paradigm. Following are the steps students will follow to complete this project.

Step 1: Project Introduction Self Study [6 days]

In the first step, students will learn about the financial industry and review the project introduction documentation to build up the subject knowledge. This is a self-learning stage; however, instructor hours are available if required.

Step 2: Project Build-up and Environment Setup [2 days]

In this step, students are required to follow the project guide to set up the development environment. The project document guides students to find and connect to the LSET Git repository and install the necessary libraries or tools.

Step 3: Product Backlog and Sprint Planning [2 days]

In this step, students will use the existing product backlog and enhance it per their project scope. Students can seek help from the project coordinator and the instructor. The project coordinator will help students do sprint planning and assign story points to the stories. This process is meant to give students real-world work environment experience. Students can consider this a mock exercise on agile project management practices.

Step 4: User Stories Execution and Development [12 days]

Students will work on the user stories identified in the Step 3 process in this step. Students will write code and algorithms to complete the development objectives. The project coordinator will be available to help students to guide them on the development and answer any questions they may have. Students can also discuss this with the instructor.

Step 5: Testing, Deployment and Completion [5 days]

In this step, students will test and deploy the application to the cloud environment. Students will experience the deployment process in the cloud and learn the best practices. After the successful deployment, students will present their project to the instructor and the external project reviewer. Feedback will be given to the students. Students will have one week to work on the feedback and submit the final copy of the project, which will be sent to the external examiner for evaluation.

Project Presentation

LSET emphasises preparing students for the work environment by allowing them to learn the required soft skills. After completing the project, students must present their work to the instructor and an invited project reviewer panel. Please note that the assigned external examiner will not be part of this panel and hence will not know about the students. This ensures an unbiased assessment by the external examiner. This exercise aims to allow students to experience an environment they may face in their actual job. Also, it gives them a chance to get feedback from industry experts who can guide students on various parts of the project. This will help students to learn and fix anything they find necessary in their project. This ensures quality output and allows students to learn about industry requirements.

The instructor and the project reviewer panel will assess the students on the following;

Project Repository on GitHub [10 points]: The instructor will ensure that the students have uploaded the project repository to the LSET’s GitHub account per the guidelines in the project requirement documentation. Full points will be awarded if the repository is appropriately set up per the instructions.

Presentation Skills [20 points]: Students must present their work in the given timeframe. Full points will be awarded if students cover everything needed to deliver their work in the given timeframe.

Communication Skills [20 points]: Students must present their work in a manner understandable by all the participants. More focus will be given to how students communicate, not the language. Full points will be awarded if students can share their work correctly.

Evaluation Criteria

LSET promotes a transparent and unbiased evaluation process. All the external examiners will follow a set process to grade students. No student’s personal or identifying information will be shared with the external examiners, so they will not know about the person they are grading. They will only get the project files and grading guidelines to follow. This will ensure equal quality standards across the institute.

Following are some critical areas the LSET external examiners will be grading on.

Project Documentation [10 points]: Project documentation is filled correctly with the information which can be used to understand the project work. Students can use the supplied project documentation template to fill up the data. External examiner to confirm if all the information is filled up. Full points will be awarded if all the sections are covered.

Project Structure [10 points]: Students must follow the proper structure while developing their projects. This structure is being taught and covered in the project requirement documentation. External examiner to confirm if the project files are correctly structured. Full points will be awarded if the structure meets the given guideline.

Solves Basic Problem [50 points]: Students must ensure that they implement all the requirements in the project documentation. External examiner to confirm if the project solves the given problem. Full points will be awarded if the students include everything asked in the project requirement.

Innovation [20 points]: Students are encouraged to bring new ideas into their development. They can improve the design, use new design patterns, code with a better coding style, or add a feature. External examiner to confirm if the students have added more than the requirement to improve the design or solution. The new addition must include a new feature and should not be similar to the requirements given. Full points will be awarded if the external examiner finds an innovation or see students going beyond the asked requirements.

Best Practices [20 points]: Students must follow the best practices in their development. This will help them to become a quality resource for their prospective employer. External examiner to confirm if the supplied best practices are followed in the project. Full points will be awarded if the best practices are properly implemented.

Performance Consideration [20 points]: Students must consider performance while working on their projects. Performance is one of the critical industry requirements. External examiner to confirm if the student thought the performance improvements in the project. Full points will be awarded if the external examiner sees efforts taken to consider performance aspects in the development.

Security Structure [20 points]: Students need to consider the security aspect If applicable in the design and development. External examiner to confirm if the security consideration is appropriate in this project; if it is applicable, the examiner to verify if the student has considered the security elements in the project. Full points will be awarded if the external examiner sees efforts taken to assess the security aspect of the development.

Benefits of LSET Certificate

Earning the LSET Certificate means you have demonstrated hard-working capabilities and learnt the latest technologies by completing hands-on exercises and real-world projects.

Following are some of the traits employers can trust you have built up through your course;
  • You know how to work in a team environment and communicate well.
  • You know the tools which are necessary for your desired job.
  • You know how to use the latest technologies to develop technologically advanced solutions.
  • You have developed problem-solving skills to navigate complex problem scenarios and find the right solutions.
  • You are now ready to take on the challenge and help your prospective employer to build the desired solutions.
Benefits of LSET Certificate
What to expect after completing the course

What to expect after completing the course?

After earning your certificate from LSET, you can join the LSET’s Alumni club. There are countless benefits associated with the Alumni Club membership. As a member of LSET Alumni, you can expect the following;
  • LSET to hold your hand to find a successful career
  • Advice you on choosing the right job based on your passion and goals
  • Connect you with industry experts for career progression
  • Provide you opportunities to participate in events to keep yourself updated
  • Provide you with a chance to contribute to the game-changing open-source projects
  • Provide you with a platform to shine by allowing you to speak at our events

Tools & Technologies You Will Learn from This Course

TENSORFLOW

TENSORFLOW

PYTHON

PYTHON

MATPLOTLIB

MATPLOTLIB

KERAS

KERAS

CHATGPT

CHATGPT

MySQL

MySQL

Register Now!

Get started on your journey to becoming a professional in artificial intelligence

Taking the LSET could provide you with the perfect starting point for your career in Artificial Intelligence.

Apply Now
 

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