Description
Course Name: Advanced Diploma in AI and Machine Learning
Course Id: ADAIML/Q1001.
Eligibility: 10+2 (Higher Secondary) or Equivalent.
Objective: The Advanced Diploma in AI and Machine Learning aims to provide a deep understanding of core and advanced concepts in artificial intelligence and machine learning. The course covers topics such as supervised and unsupervised learning, neural networks, deep learning, natural language processing, and model evaluation.
Duration: Six Months.
How to Enroll and Get Certified in Your Chosen Course:
Step 1: Choose the course you wish to get certified in.
Step 2: Click on the “Enroll Now” button.
Step 3: Proceed with the enrollment process.
Step 4: Enter your billing details and continue to course fee payment.
Step 5: You will be redirected to the payment gateway. Pay the course and exam fee using one of the following methods:
Debit/Credit Card, Wallet, Paytm, Net Banking, UPI, or Google Pay.
Step 6: After successful payment, you will receive your study material login ID and password via email within 48 hours of fee payment.
Step 7: Once you complete the course, take the online examination.
Step 8: Upon passing the examination, you will receive:
• A soft copy (scanned) of your certificate via email within 7 days of examination.
• A hard copy (original with official seal and signature) sent to your address within 45 day of declaration of result.
Step 9: After certification, you will be offered job opportunities aligned with your area of interest.
Online Examination Detail:
Duration- 120 minutes.
No. of Questions- 60. (Multiple Choice Questions).
10 Questions from each module, each carry 10 marks.
Maximum Marks- 600, Passing Marks- 40%.
There is no negative marking in this module.
| How Students will be Graded: | ||
| S.No. | Marks | Grade |
| 1 | 91-100 | O (Outstanding) |
| 2 | 81-90 | A+ (Excellent) |
| 3 | 71-80 | A (Very Good) |
| 4 | 61-70 | B (Good) |
| 5 | 51-60 | C (Average) |
| 6 | 41-50 | P (Pass) |
| 7 | 0-40 | F (Fail) |
Key Benefits of Certification- Earning a professional certification not only validates your skills but also enhances your employability. Here are the major benefits you gain:
Practical, Job-Ready Skills – Our certifications are designed to equip you with real-world, hands-on skills that match current industry demands — helping you become employment-ready from day one.
Lifetime Validity – Your certification is valid for a lifetime — no renewals or expirations. It serves as a permanent proof of your skills and training.
Lifetime Certificate Verification – Employers and institutions can verify your certification anytime through a secure and reliable verification system — adding credibility to your qualifications.
Industry-Aligned Certification –All certifications are developed in consultation with industry experts to ensure that what you learn is current, relevant, and aligned with market needs.
Preferred by Employers – Candidates from ISO-certified institutes are often prioritized by recruiters due to their exposure to standardized, high-quality training.
Free Job Assistance Based on Your Career Interests – Receive personalized job assistance and career guidance in your preferred domain, helping you land the right role faster.
Syllabus
Introduction to AI and Machine Learning: Overview of Artificial Intelligence and Its Applications, History and Evolution of AI and Machine Learning, Types of Machine Learning: Supervised, Unsupervised and Reinforcement Learning, AI vs. Machine Learning vs. Deep Learning, Role of Big Data and Cloud Computing in AI, Ethics, Bias and Challenges in AI Development, AI and ML Industry Use Cases (Healthcare, Finance, Automotive, etc.), Setting Up a Python Environment for AI/ML (Jupyter, Anaconda, Google Colab).
Mathematics and Statistics for AI/ML: Linear Algebra Essentials (Vectors, Matrices, Eigenvalues), Probability and Statistics in Machine Learning (Bayes Theorem, Distributions), Calculus for Machine Learning (Derivatives, Gradient Descent), Optimization Techniques (Stochastic Gradient Descent, Adam Optimizer), Cost Functions and Loss Functions in ML Models, Regularization Techniques (L1, L2, Dropout), Evaluation Metrics (Accuracy, Precision, Recall, F1-Score, AUC-ROC), Probability Distributions and Hypothesis Testing.
Machine Learning Algorithms and Techniques: Understanding Supervised and Unsupervised Learning, Linear and Logistic Regression for Predictive Modeling, Decision Trees, Random Forest and Ensemble Learning, Support Vector Machines (SVM) and Kernel Methods, Clustering Algorithms: K-Means, DBSCAN, Hierarchical Clustering, Dimensionality Reduction Techniques: PCA, LDA, t-SNE, Anomaly Detection and Outlier Analysis, Model Selection, Cross-Validation and Hyperparameter Tuning.
Deep Learning and Neural Networks: Introduction to Deep Learning and Neural Networks, Feedforward Neural Networks (FNN) and Backpropagation, Activation Functions: ReLU, Sigmoid, Tanh, Softmax, Optimizers: SGD, Adam, RMSprop, Momentum, Convolutional Neural Networks (CNNs) for Image Processing, Recurrent Neural Networks (RNNs) for Sequential Data Processing, Autoencoders and Generative Adversarial Networks (GANs), Implementing Deep Learning Models with TensorFlow and PyTorch.
Natural Language Processing (NLP): introduction to NLP and Text Processing Techniques, Tokenization, Stemming, and Lemmatization, Named Entity Recognition (NER) and Part-of-Speech (POS) Tagging, Sentiment Analysis and Text Classification, Word Embeddings: Word2Vec, GloVe, FastText, Transformer Models (BERT, GPT, T5) for NLP Tasks, Speech Recognition and AI Chatbots, Implementing NLP Applications Using Python (NLTK, SpaCy, Hugging Face).
Reinforcement Learning and AI Agents: Introduction to Reinforcement Learning (RL) Concepts, Markov Decision Processes (MDP) and Bellman Equation, Policy-Based vs. Value-Based RL Algorithms, Q-Learning and Deep Q-Networks (DQN), Proximal Policy Optimization (PPO) and Actor-Critic Methods, Multi-Agent Reinforcement Learning, Applications of RL in Robotics, Finance and Gaming, Implementing RL Algorithms Using OpenAI Gym and Stable Baselines.
Job Opportunities after Advanced Diploma in AI & Machine Learning
Graduates can explore high-demand roles across IT, finance, healthcare, e-commerce, and manufacturing.
Top Career Roles: AI/ML Engineer, Data Scientist, AI Research Scientist, Data Engineer, AI Software Developer, NLP Engineer, Computer Vision Engineer, AI Consultant, Business Intelligence Analyst, AI Product Manager, AI/ML Trainer or Educator
Key Skills: Python, R, TensorFlow, PyTorch, NLP, Deep Learning, SQL, Cloud (AWS/Azure/GCP), Data Analytics.
Salary Range (India):
- Entry-level (0–2 yrs): ₹6–12 LPA
- Mid-level (3–5 yrs): ₹12–20 LPA
- Senior-level (5+ yrs): ₹20–40 LPA+
Scope: Rapid industry growth ensures strong demand, especially for specialists in NLP, computer vision, and deep learning.
