Artificial Intelligence and Applied Gen AI Certification

90 hours of Full-Stack AI, Gen AI & Agentic Systems training program

Globally recognised industry-standard AI certification

Neural networks expertise using Automation Sandboxes

30+ Modules | Capstone Projects | Real-World Deployment

Flexible learning options with easy instalments

4.8/5

3245 Enrolled

Overview

We will teach you to:

Master Python, LangChain, and TensorFlow to develop advanced deep learning models in 90 hours training
Build real-world applications with natural language processing, including chatbots
Implement reinforcement learning techniques such as Q-learning and policy optimisation
Develop cutting-edge Generative AI projects using GANs, Transformers, and diffusion models
Apply AI solutions to business scenarios with tools like Power BI and Flask
Architect and train sophisticated neural networks using advanced deep learning techniques and optimisation strategies
Designed to prepare professionals for production-ready AI roles
Consists of 5 learning levels: Foundation → ML → DL → GenAI → Agentic AI

Learning Objectives

Upon finishing the training, you will:

Master deep learning with TensorFlow, Keras, and Python for advanced AI

Create NLP models in Python for tasks like sentiment analysis

Build and refine Generative AI models using GANs and Transformers

Apply reinforcement learning for complex decision-making

Develop AI business solutions using Power BI and Flask integration

Gain hands-on experience with real-world projects like chatbots and predictive models

Ready to get started?

Overall ratings by our students

Upcoming sessions

Our Trainers

Learners Point has a reputation for high-quality training that makes a difference in people's lives. We undertake a practical and innovative approach to working closely with businesses to improve their workforce. Our expertise is wide-ranging with ample support from our expert trainers who are globally recognized and hold a diverse set of experiences in their field of expertise. We are proud of our instructors who take ownership of our distinctive and comprehensive training methodologies, help our students imbibe those with ease, and accomplish gracefully.

We at Learners Point believe in encouraging our students to embark upon a journey of lifelong learning and self-development, with the aid of our comprehensive and distinctive courses tailored to current market trends. The manifestation of our career-oriented approach is what we assure through a pleasant professional enriched environment with cutting-edge technology, and an outstanding while highly acknowledged training staff that uses up-to-date methodologies and quality course material. With our aim to mold professionals to be future leaders, our industry expert trainers provide the best in town mentorship to our students while endowing them with the thirst for knowledge and inspiring them to strive for professional and human excellence.

KHDA Certificate

Earn a KHDA attested Course Certificate. The Knowledge and Human Development Authority (KHDA) is the educational quality assurance and regulatory authority of the Government of Dubai, United Arab Emirates.

Learners Point Certificate

Earn a Course Completion Certificate, an official Learners Point credential that confirms that you have successfully completed a course with us.

Related courses

Curriculum

Python syntax, variables, data types
Conditional statements and loops
Functions and lambda functions
Lists, tuples, dictionaries, sets
Automating data workflows

Jupyter/Colab usage
NumPy arrays and indexing
Vectorized operations
Matrix transformations
Numerical computation workflows

Data loading and cleaning
Handling missing values
Data merging
Data transformation
Dataset preparation workflows

Statistical measures
Distribution analysis
Probability rules
Normal and binomial distributions
Business data interpretation

Central Limit Theorem
Confidence intervals
t-tests, chi-square, ANOVA
A/B testing
Decision-making using statistics

Histograms and bar charts
Boxplots and heatmaps
Correlation analysis
Visualization best practices
Dashboard-ready charts

Feature engineering
Scaling and encoding
Outlier detection
Data exploration workflows
Pattern identification

ML types and workflows
Model training and evaluation
Performance metrics
Model validation
Business ML applications

Linear regression
Logistic regression
Model interpretation
ROC-AUC and confusion matrix
Prediction systems

Decision trees
Random forests
KNN algorithm
Model tuning
Predictive modeling use cases

K-means clustering
Hierarchical clustering
PCA
Feature reduction
Segmentation models

Data integration
Power Query transformations
DAX calculations
Dashboard building
Business reporting

Model export and integration
Dashboard automation
Predictive analytics visualization
Business intelligence workflows
Data storytelling

ANN concepts
Activation functions
Forward and backward propagation
Keras implementation
Model design

TensorFlow and Keras
Loss functions and optimizers
Overfitting and underfitting
Model tuning
Trainer workflows

Text preprocessing
Tokenization and lemmatization
Vectorization techniques
NLP workflows
Sentiment analysis

Logistic regression and Naive Bayes
Word embeddings
Classification models
Text categorization
Model evaluation

Image processing
CNN architecture
Transfer learning
Model storage
Deployment basics

Flask and FastAPI
REST API development
Model deployment
Integration workflows
Production considerations

RL concepts
Q-learning
Agent-environment interaction
Exploration vs exploitation
Simulation models

Self-attention mechanisms
Transformer architecture
BERT vs GPT
Positional encoding
Text generation

GAN architecture
Generator and discriminator
GAN variants
Image generation
Model training

Diffusion process
DDPM
Tools like Stable Diffusion
Image generation workflows
Model comparison

GPT and LLM concepts
Prompt engineering
Fine-tuning techniques
Domain-specific models
Chatbot development

LangChain architecture
Retrieval-augmented generation
Knowledge integration
AI pipelines
LangChain architecture

Vision-Language Models
Media generation
Multimodal systems
AI creativity tools
Application design

Multi-agent systems
Autonomous workflows
Decision agents
Task orchestration
AI assistants

Copilot Integration:

Generate agent workflows

Multi-agent collaboration
LangChain dynamic decision agents
Build a simple autonomous assistant

Copilot Integration:

Generate orchestration logic

Task decomposition & planning
Reflex vs learning agents
CrewAI orchestration
Multi-agent workflows

Copilot Integration:

Generate planning logic

LangChain tools (Search, APIs, Zapier)
Agent memory (vector, long-term, summary)
Context retention strategies
Web-aware chatbot systems

Copilot Integration:

Generate tool workflows

Function calling
ReAct prompting
Toolformer concept
Tool-based reasoning

Copilot Integration:

Generate reasoning workflows

Prompt → tools → output orchestration
Guardrails and observability
Evaluation metrics
Production workflows

Copilot Integration:

Generate production workflows

Ethics and bias
Model governance
Deployment platforms
Monitoring and evaluation
Public hosting

Copilot Integration:

Generate governance insights

Participants will build end-to-end AI solutions including data pipelines, predictive models, generative AI systems, and autonomous agents. Projects include employee attrition prediction, sentiment analysis systems, and building autonomous chatbots using LangChain.

The Automation Sandbox enables participants to apply the knowledge gained during the training to their own professional responsibilities and everyday work processes.
This activity encourages participants to examine their existing workflows and identify opportunities to improve efficiency, streamline tasks, and enhance operational outcomes.
Through guided exercises, participants review and analyze their current workflows, identify opportunities for improvement, and redesign processes using structured approaches. They translate their expertise into well-defined workflow structures outlining key steps, decision points, and expected outcomes.
These structured workflows are designed to be easily understood by technical teams, enabling effective automation implementation while bridging the gap between business processes and technical execution.

Frequently asked questions

The Artificial Intelligence and Applied Gen AI Certification equips you with skills across Python, data analysis, machine learning, deep learning, NLP, and Generative AI. This course helps you build real-world AI solutions across predictive modelling, AI deployment, RAG, and autonomous AI applications.

This certification helps you build practical capability for AI, machine learning, and applied GenAI roles. It also supports progression into AI-driven industries through hands-on learning, projects, and end-to-end solution building.

This Applied Gen AI course covers essential topics like Python, data analysis, machine learning, deep learning, NLP, Generative AI, reinforcement learning, and AI deployment. You will gain hands-on experience building models with TensorFlow, Keras and Python.

You also learn to apply these techniques to real-world problems like sentiment analysis and predictive modelling, chatbot development, and AI-powered business applications.

Earning the Artificial Intelligence and Applied Gen AI certification can lead to career advancement by opening doors to higher-paying roles and leadership positions. This AI certification supports career advancement by building practical capability across Python, machine learning, deep learning, NLP, Generative AI, and model deployment. Professionals learn to move beyond theory and build AI solutions that solve business problems.

This credential also signals readiness for roles in AI engineering, machine learning, data science, GenAI development, and automation. With hands-on projects, industry simulations, and autonomous agent workflows, participants gain evidence of workplace-ready skills, not just conceptual understanding.

Yes, this course is suitable for beginners. The training starts with Python essentials, Jupyter, NumPy, Pandas, statistics, and data handling before moving into machine learning and Generative AI. This makes it accessible to those new to AI but eager to develop practical skills.

This structure helps participants develop a proper foundation instead of jumping straight into advanced tools. As the course progresses, they work on predictive models, NLP, LLMs, RAG, and agentic AI through guided exercises and practical case studies.

This Applied Gen AI training program is ideal for aspiring AI professionals, data analysts, software developers, IT professionals, business analysts, and managers who want to build expertise in Artificial Intelligence and Generative AI. It is also suitable for fresh graduates and professionals looking to transition into AI-driven roles.

Choosing the right institute for an AI certification is as important as choosing the course itself. Learners Point has built a proven reputation for delivering industry-aligned and career-transforming training that goes far beyond the classroom. The reasons are as follows:

Industry-experienced faculty and mentors who bring real-world AI and data science expertise into every session, ensuring learners receive guidance that is directly applicable to the job market.
A future-ready curriculum that covers the full AI spectrum from Python and Machine Learning fundamentals to LLMs, Generative AI, Agentic frameworks, and Autonomous AI systems.
Hands-on, project-based learning through Industry Simulations, Capstone Projects, and the Automation Sandbox that equip learners with a job-ready portfolio.
A strong record of career outcomes with a track record of helping professionals transition into high-demand AI roles across industries including finance, healthcare, retail, and technology.
Flexible and learner-centric delivery designed to accommodate working professionals with structured support, personalised mentorship, and a collaborative learning environment.

The Automation Sandbox is one of the most distinctive and career-relevant components of this certification. It is a guided, practical activity designed to turn classroom learning into real workplace transformation. The Automation Sandbox in this training helps to:

Apply AI and automation concepts directly to your own professional workflows, making learning immediately relevant to your current role and responsibilities.
Identify automation opportunities within your existing work processes, helping you spot where time, effort, and resources are being lost to repetitive or inefficient tasks.
Restructure and simplify workflows by breaking them down into clear and logical steps, decision points, and expected outcomes.
Bridge the gap between business knowledge and technical execution by helping you create well-defined workflow blueprints that automation and AI teams can act on without ambiguity.
Build an automation-first mindset that enables you to evaluate any process critically.
Deliver a tangible output in the form of a structured automation strategy that you can present and implement within your own organisation.

No, prior coding experience is not required before enrolling in this Applied Gen AI training. This training program starts with Python fundamentals and gradually builds up to advanced AI and Generative AI concepts.

This structured and step-by-step approach ensures that even beginners can comfortably learn and progress throughout the course.

In the Artificial Intelligence and Applied Gen AI training, you will work with a wide range of AI and GenAI tools designed for building, deploying and scaling intelligent applications. These tools give you practical exposure to both foundational AI frameworks and advanced generative AI platforms. These include:

TensorFlow & PyTorch – for deep learning and model development
LangChain & AutoGPT – for building AI agents and workflows
OpenAI GPT APIs – for natural language processing and generative tasks
Google Vertex AI & Azure AI Studio – for cloud-based AI deployment
Hugging Face Transformers – for pre-trained models and fine-tuning
Stable Diffusion & MidJourney – for text-to-image generation and creativity
Tableau & Power BI with AI integration – for AI-powered data insights

Copilot is integrated into this Gen AI training as a practical support tool for building agent and automation workflows. Learners use it to generate agent workflows, orchestration logic, planning logic, and tool-based structures during advanced modules.

This makes the learning more workplace-focused. Instead of using Copilot only for prompts, participants apply it to multi-agent systems, context-aware chatbots, reasoning assistants, production AI workflows, and responsible AI governance. That helps them understand where human review still matters.

Upon successful completion of the program, participants receive the Artificial Intelligence and Applied Gen AI Certification from Learners Point, approved by KHDA (Knowledge and Human Development Authority – Dubai).

This certification validates practical skills in Machine Learning, Deep Learning, Generative AI, and AI application development. This demonstrates industry-relevant expertise in building and deploying AI solutions.

Yes, this Artificial Intelligence and Applied Gen AI course will be available for GCC locations. We provide our training across various GCC regions, which helps you to achieve this credential easily. These regions are: