Professional Certificate in Artificial Intelligence & Machine Learning: Syllabus and Fee
Professional Certificate in Artificial Intelligence & Machine Learning
Build practical knowledge in Artificial Intelligence, Machine Learning, Python, Data Science, Generative AI, NLP, Deep Learning and Computer Vision through a structured 12-month integrated programme.
A comprehensive programme covering AI fundamentals to a final Artificial Intelligence & Machine Learning capstone project.
📞 ENQUIRE NOW – 8840458141 💬 WHATSAPP NOWLearn the fundamentals of Artificial Intelligence and progressively move toward Machine Learning, Deep Learning, Generative AI, Natural Language Processing and Computer Vision in a structured 12-month programme.
🎓 GET COURSE DETAILSArtificial Intelligence and Machine Learning are transforming the way businesses, organisations and technology platforms operate. From conversational AI and predictive modelling to computer vision and Generative AI, intelligent technologies are becoming an important part of the modern digital ecosystem.
The Professional Certificate in Artificial Intelligence & Machine Learning is designed as a comprehensive learning programme for students, professionals, technology enthusiasts and aspiring AI learners who want structured exposure to the major concepts and tools used across the AI and ML landscape.
The programme has a 12-month integrated structure covering Python programming, data processing, statistics, machine learning algorithms, NLP, neural networks, Generative AI, Large Language Models, computer vision, AI ethics and a final capstone project.
🚀 ENROL FOR ₹20,000Professional AI & Machine Learning Certificate – Course Highlights
Course Details
| Course Name | Professional Certificate in Artificial Intelligence & Machine Learning |
| Programme Duration | 12 Months |
| Total Fee | ₹20,000 |
| Learning Areas | AI, Python, Data Science, Machine Learning, NLP, Deep Learning, Generative AI & Computer Vision |
| Final Project | Artificial Intelligence & Machine Learning Capstone Project |
Complete 12-Month AI & Machine Learning Syllabus
- Introduction to Artificial Intelligence
- Evolution and Applications of AI
- AI, Machine Learning and Deep Learning
- Generative AI: Introduction and Applications
- AI Problem-Solving Methodology
- AI Project Cycle
- Problem Scoping and Goal Definition
- Data Acquisition and Exploration
- AI Modelling and Evaluation
- Introduction to Responsible AI
- Introduction to Python Programming
- Variables, Data Types and Operators
- Conditional Statements and Control Flow
- Loops and Iterations
- Strings and Collections
- Lists, Tuples, Sets and Dictionaries
- Functions and Modules
- File Handling
- Exception Handling
- Introduction to Object-Oriented Programming
- Python Libraries for AI and Data Science
- Fundamentals of Data and Datasets
- Structured, Semi-Structured and Unstructured Data
- Data Collection and Acquisition
- Data Preparation and Preprocessing
- Introduction to NumPy
- Introduction to Pandas
- DataFrames and Data Manipulation
- Data Filtering, Sorting and Aggregation
- Data Integration and Transformation
- Preparing Data for AI/ML Models
- Fundamentals of Data Analysis
- Descriptive Statistics
- Mean, Median and Mode
- Variance and Standard Deviation
- Probability Fundamentals
- Correlation and Relationships in Data
- Introduction to Linear Algebra for AI
- Data Visualisation Principles
- Charts, Graphs and Histograms
- Scatter Plots and Heatmaps
- Exploratory Data Analysis
- Introduction to Matplotlib and Seaborn
- Importance of Data Quality
- Missing Data and Data Imputation
- Duplicate and Inconsistent Data
- Handling Noisy and Dirty Data
- Data Normalisation
- Standardisation and Z-Score
- Feature Scaling
- Outlier Detection and Treatment
- Feature Selection
- Feature Engineering
- Preparing High-Quality Data for Machine Learning
- Introduction to Machine Learning
- Machine Learning Workflow
- Supervised Learning
- Unsupervised Learning
- Reinforcement Learning
- Training, Validation and Testing Data
- Features and Target Variables
- Model Training and Prediction
- Model Evaluation
- Overfitting and Underfitting
- Introduction to Scikit-learn
- Regression and Classification
- Simple and Multiple Linear Regression
- Logistic Regression
- Decision Trees
- Random Forest
- K-Nearest Neighbours
- Introduction to Support Vector Machines
- Confusion Matrix
- Accuracy, Precision, Recall and F1 Score
- Cross-Validation
- Model Comparison and Selection
- Introduction to Natural Language Processing
- Applications of NLP
- Text Data and Preprocessing
- Tokenisation
- Stop Words
- Stemming and Lemmatization
- Bag-of-Words and TF-IDF
- Text Classification
- Sentiment Analysis
- Named Entity Recognition
- Text Summarisation
- Spam Detection
- Introduction to Chatbots and Conversational AI
- Introduction to Deep Learning
- Machine Learning vs Deep Learning
- Fundamentals of Artificial Neural Networks
- Artificial Neurons and Perceptrons
- Neural Network Architecture
- Input, Hidden and Output Layers
- Activation Functions
- Forward Propagation
- Backpropagation
- Loss Functions and Optimisation
- Training, Validation and Testing
- Introduction to TensorFlow and Keras
- MNIST and Basic Neural Network Applications
- Fundamentals of Generative AI
- Generative AI vs Traditional AI
- Generative AI Applications
- Large Language Models (LLMs)
- Tokens and Context
- Embeddings: Fundamentals
- Transformers: Introduction
- Prompt Engineering
- Prompt Design Techniques
- Zero-Shot and Few-Shot Prompting
- AI-Powered Content and Productivity Applications
- Conversational AI
- Introduction to Retrieval-Augmented Generation (RAG)
- AI Hallucinations, Limitations and Responsible Usage
- Introduction to Computer Vision
- Digital Images and Image Representation
- Image Processing Fundamentals
- Image Classification
- Image Recognition
- Object Detection
- Object Tracking
- Colour Detection
- Face Detection and Recognition
- Introduction to Image Segmentation
- Introduction to OpenCV
- Real-World Computer Vision Applications
- Responsible Use of Computer Vision Technologies
- AI Applications Across Industries
- AI in Healthcare
- AI in Education
- AI in Finance
- AI in Business and Marketing
- AI in Manufacturing
- AI in Agriculture
- AI Ethics and Responsible AI
- Bias, Fairness and Transparency
- Data Privacy and Security
- AI Model Evaluation and Deployment: Introduction
- AI Career Pathways and Emerging Opportunities
- Capstone Project Planning and Development
- Final Artificial Intelligence & Machine Learning Capstone Project
- Project Presentation and Evaluation
What Will You Learn in This AI & Machine Learning Course?
The programme takes learners from the fundamentals of Artificial Intelligence to advanced topics such as Machine Learning, Deep Learning, Generative AI and Computer Vision.
- Understand the fundamentals of Artificial Intelligence
- Build programming foundations with Python
- Work with datasets using NumPy and Pandas
- Perform data analysis and visualisation
- Clean data and perform feature engineering
- Understand supervised and unsupervised Machine Learning
- Study important ML algorithms
- Evaluate predictive models
- Understand Natural Language Processing
- Explore neural networks and Deep Learning
- Understand Generative AI and Large Language Models
- Learn the fundamentals of prompt engineering
- Explore Retrieval-Augmented Generation
- Understand Computer Vision
- Explore responsible and ethical AI
- Develop an AI/ML capstone project
Key Technologies & Concepts Covered
🐍 Python
Programming fundamentals and Python libraries for AI and Data Science.
🔢 NumPy
Introduction to numerical data handling and processing.
🐼 Pandas
DataFrames, manipulation, filtering, sorting and aggregation.
📈 Matplotlib & Seaborn
Data visualisation, charts, graphs and exploratory analysis.
🤖 Scikit-learn
Introduction to machine learning workflows and algorithms.
🧠 TensorFlow & Keras
Introduction to neural networks and Deep Learning applications.
💬 NLP
Text processing, sentiment analysis, classification and conversational AI.
👁️ OpenCV
Introduction to image processing and Computer Vision.
✨ Generative AI
LLMs, embeddings, transformers, prompt engineering and RAG fundamentals.
Career Scope After Artificial Intelligence & Machine Learning Training
Artificial Intelligence and Machine Learning knowledge can be relevant across technology, business, finance, healthcare, education, manufacturing, agriculture, marketing and other sectors.
The programme provides exposure to multiple AI domains that can help learners understand different career pathways and technology applications.
🤖 AI Developer
Work with AI concepts, models and intelligent applications.
🧠 Machine Learning
Explore machine learning workflows, predictive modelling and model evaluation.
📊 Data & Analytics
Use data processing, analysis and visualisation techniques.
💬 NLP & Conversational AI
Explore text processing, chatbots and language-based AI applications.
✨ Generative AI
Explore LLMs, prompt engineering and Generative AI applications.
👁️ Computer Vision
Explore image processing, classification, recognition and object detection.
Who Should Join This AI & Machine Learning Certificate Course?
- Students interested in Artificial Intelligence
- Graduates looking to learn modern technology skills
- Working professionals interested in AI and automation
- Beginners who want a structured introduction to Machine Learning
- Programming enthusiasts interested in Python for AI
- Data enthusiasts interested in analytics and machine learning
- Technology professionals exploring Generative AI
- Entrepreneurs interested in AI-powered applications
- Learners interested in NLP and Computer Vision
Final AI & Machine Learning Capstone Project
The final module includes Capstone Project Planning and Development, followed by a final Artificial Intelligence & Machine Learning capstone project and project presentation and evaluation.
A capstone project gives learners an opportunity to bring together concepts covered throughout the programme and work toward an integrated AI/ML project.
Artificial Intelligence & Machine Learning Capstone Project with project presentation and evaluation.
How to Enrol in the Professional AI & ML Certificate Course?
Call or WhatsApp 8840458141 to enquire about the programme.
Get information about the 12-month curriculum, modules, learning structure and course requirements.
The total programme fee is ₹20,000.
Complete the applicable registration and admission formalities.
Start your structured journey through Artificial Intelligence and Machine Learning.
Frequently Asked Questions – AI & Machine Learning Certificate
It is a 12-month integrated programme covering Artificial Intelligence, Python, data processing, Machine Learning, NLP, Deep Learning, Generative AI, Large Language Models, Computer Vision and AI ethics.
The total programme fee provided for this course is ₹20,000.
The programme duration is 12 months.
Yes. The second module focuses specifically on Python Programming for Artificial Intelligence, including Python fundamentals, functions, file handling, OOP concepts and Python libraries for AI and Data Science.
Yes. The programme covers Machine Learning fundamentals, supervised and unsupervised learning, regression, classification, decision trees, random forest, KNN, SVM fundamentals, model evaluation and cross-validation.
Yes. The Generative AI module covers Generative AI fundamentals, Large Language Models, tokens, context, embeddings, transformers, prompt engineering, zero-shot and few-shot prompting and an introduction to RAG.
The syllabus includes Conversational AI and Generative AI concepts, including Large Language Models, prompt design and AI-powered productivity applications.
Yes. The NLP module includes tokenisation, stop words, stemming, lemmatisation, Bag-of-Words, TF-IDF, text classification, sentiment analysis, named entity recognition, text summarisation and spam detection.
Yes. The Deep Learning module covers neural networks, artificial neurons, perceptrons, network architecture, activation functions, forward propagation, backpropagation, loss functions, optimisation and introductory TensorFlow and Keras.
Yes. The Computer Vision module covers digital images, image processing, image classification, image recognition, object detection, object tracking, face detection, segmentation fundamentals and OpenCV.
Yes. Data handling, NumPy, Pandas, data analysis, statistics, visualisation, data cleaning, preprocessing and feature engineering are included.
Yes. Responsible AI, bias, fairness, transparency, data privacy, security and responsible use of AI technologies are included in the syllabus.
Yes. Module 12 includes Capstone Project Planning and Development, a final Artificial Intelligence & Machine Learning Capstone Project and project presentation and evaluation.
The programme can be considered by students, graduates, working professionals, technology enthusiasts and learners interested in developing knowledge of AI and Machine Learning. Specific admission eligibility should be confirmed before enrolment.
The syllabus includes Python, NumPy, Pandas, Matplotlib, Seaborn, Scikit-learn, TensorFlow, Keras and OpenCV, along with concepts related to NLP, Generative AI and Large Language Models.
The skills covered can be relevant to areas such as AI development, Machine Learning, data analytics, NLP, Generative AI, Computer Vision and AI-enabled business applications. Actual employment opportunities depend on an individual’s skills, experience, qualifications and employer requirements.
Call or WhatsApp 8840458141 for course and admission information.
The complete 12-month Professional Certificate in Artificial Intelligence & Machine Learning programme is offered at the stated total fee of ₹20,000.
🚀 Start Your AI & Machine Learning Journey
Learn Artificial Intelligence from fundamentals to Machine Learning, Deep Learning, Generative AI, NLP and Computer Vision through a structured 12-month programme.
Professional Certificate in AI & Machine Learning
₹20,000 TOTAL FEE
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Disclaimer: Course duration, fee, curriculum, admission requirements, learning structure and other programme details are subject to the information provided for this programme and may be updated by the course provider. Students should confirm the latest details before enrolment or payment. Completion of a course does not by itself guarantee employment or selection for any particular job.
