Certificate Programme in AI-Driven Full Stack Development,, Machine Learning
at Aiqubez Spark: The Innovation Hub
Programme Overview
The AI-Driven Full Stack Developer Certificate Programme in Machine Learning empowers aspiring developers and tech professionals with future-ready AI skills.
Through a dynamic blend of theory, practical labs, and real-world projects, participants will master the foundations of Artificial Intelligence, advance through Machine Learning and Deep Learning frameworks, and explore Generative AI models transforming industries today.
By the end of the programme, learners will gain hands-on experience and technical expertise to design, develop, and deploy AI-powered full stack applications, becoming job-ready for the next era of technology.
Certificate of Completion from AiQubez
Live Online Learning Sessions
Real-World, Industry-Led Pedagogy
AI Code Immersion
Practical AI Simulation Labs
Explore the Power of AI, ML & Next-Gen Innovation
Apply theoretical concepts in practical, hands-on scenarios.
Develop in-depth knowledge of Generative AI concepts and applications.
Advanced Reinforcement Learning for Generative Applications
Understand the Real-World Applications of AI Across Industries
Online Sessions
Live Immersion classes
Mini Quizzes
Project
Programming Sessions
Programme Modules
Module I
Introduction to the Programme
- DS vs ML vs DL vs AI
- What is AI?
- The power of AI/ML
- The limitations of AI/ML
- Brief about Definitions, Use Cases, Lifecycle
Module II
Python Fundamentals
- Variables, Numbers
- Strings Lists, Dictionaries, Sets
- Tuples If condition
- For loop Functions
- Lambda Functions
- Modules (pip install)
- File Handling (Read/Write)
- Exception Handling
- Classes & Objects
Module III
Data Libraries
- NumPy: Arrays, Functions, Save/Load
- Pandas: Series, DataFrames, Combining DataFrames
- Data Manipulation Functions
- Saving & Loading Datasets
Module IV
Data Structures & Algorithms (DSA)
- Big O Notation
- Arrays, Linked List, Hash Table, Stack, Queue
- Trees & Graphs
- Algorithms: Binary Search, Sorting (Bubble, Quick, Merge)
- Recursion
Module V
Mathematics for ML
- Linear Algebra: Vectors, Matrices, Eigenvalues, SVD
- Probability: Random Variables, Distributions, Bayes’ Rule, Gaussian
- Statistics: Descriptive & Inferential, Hypothesis Testing
Module VI
Exploratory Data Analysis (EDA)
- Data Overview & General Statistics
- Univariate, Bivariate, Multivariate Analysis (Histograms, Scatter, Heatmaps, Pairplots, etc.)
- Missing Value Treatment
- Outlier Detection & Treatment
Module VII
Feature Engineering
- Handling Missing Values & Outliers
- Data Normalisation
- Encoding (One-Hot, Label)
- Feature Selection
- Train-Test Split, Cross Validation
Module VIII
Regression & Classification Models
- Scikit-learn ML Workflow
- Regression: Linear, Logistic
- Evaluation Metrics: MSE, MAE, MAPE
- Classification: Accuracy, Precision, Recall, F1 Score, ROC Curve
- Confusion Matrix
Module IX
Non-Linear Models (Tree-Based)
- Decision Trees
- Random Forest
- XGBoost
Module X
Advanced Classifiers
- Support Vector Machines (SVM)
- K-Nearest Neighbors (KNN)
- Naïve Bayes
Module XI
Clustering, PCA & Model Tuning
- K-Means Clustering
- Principal Component Analysis (PCA)
- Hyperparameter Tuning: GridSearchCV, RandomSearchCV
Module XII
Neural Networks & Transfer Learning
- Neural Network Basics: Forward/Backpropagation
- Building MLP (Sigmoid, ReLU, Softmax, Weights & Biases)
- ANN & CNN Basics
- Tensors & Operations
- Training Neural Networks
- Pre-trained Models & Use Case
Module XIII
Transformers & Attention
- Introduction to Transformers
- Components & Architectures
- Applications of Transformers
- Hugging Face Library Basics
Module XIV
LLM Fine-Tuning & RAG
- Data Overview & General Statistics
- Univariate, Bivariate, Multivariate Analysis (Histograms, Scatter, Heatmaps, Pairplots, etc.)
- Missing Value Treatment
- Outlier Detection & Treatment
Module XV
AI Agents & APIs
- OpenAI APIs
- AWS Bedrock
- Agent Frameworks
Module XVI
Diffusion, DSP & Generative AI
- Diffusion Models & Audio Noise Distillation
- DSP Pipelines
- Image & Audio Synthesis
- Retrieval-Augmented Generation (RAG)
Module XVII
MLOps Fundamentals
- Introduction to MLOps
- Experiment Tracking with MLFlow
- Cloud Familiarity (AWS, Azure, etc.)
- Packaging & Running Projects
- Model Lifecycle Management
Module XVIII
Full Stack AI Deployment
- FastAPI Deployment
- Model Monitoring
- Gradio & Streamlit Basics
- Backend + Frontend Integration
Eligibility
Academic Criteria
Who Can Attend?
Selection Criteria
- Qualification: Graduate/4-year Engineering/Technical Degree/B.Sc/BCA/M.Sc/MCA from a recognized university (UGC/AICTE/DEC/AIU/State Government/recognized international universities).
- Minimum 50% and above is required for qualification.
- Industry Targeting (Preference): IT, Tech, Software, Engineering Research, Business Analytics, etc.
- Professionals from a tech background must have a minimum of 1 year of work experience.
- Professionals working in the IT industry, software development, programming, and related fields who want to specialize in data science and AI.
- Professionals involved in business analysis, market research, and strategic planning who wish to develop expertise in data-driven decision-making.
- Professionals satisfying the eligibility criteria with at least one year of background will be admitted to the course.
Certification That Accelerates Your Career
Industry-Recognized AI & ML Certificate
- Gain an industry-accredited AI & ML certification upon successful completion.
- Promote your upgraded skills across your professional network and career opportunities.
