Advanced Diploma in AI and Machine Learning

Description

Course Name: Advanced Diploma in AI and Machine Learning
Course Id: ADAIML/Q1001.

Eligibility: 10+2 (Higher Secondary) or Equivalent.

Objective: To provide an in-depth understanding of AI and ML concepts, tools, and techniques, enabling students to develop intelligent systems and solve complex real-world problems.

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 completion of Advanced Diploma in AI and Machine Learning course:

Graduates of the Advanced Diploma in AI and Machine Learning (Artificial Intelligence and Machine Learning) program have a wide range of career options due to the rapid growth of AI technologies in India and globally. With industries like technology, finance, healthcare, manufacturing, and e-commerce increasingly relying on AI, the demand for skilled professionals in this field is high.

Here’s an overview of the career options and salary ranges for graduates of this program in India:

1. AI/Machine Learning Engineer

• Role: AI/Machine Learning Engineers design, develop, and implement machine learning models and algorithms to solve complex business problems. They work with large datasets, train models, and evaluate their performance to make data-driven predictions.
• Skills Required: Knowledge of Python, R, TensorFlow, PyTorch, natural language processing (NLP), data science, and deep learning.
• Salary:
  • Entry-level: ₹6–₹10 LPA
  • Mid-level: ₹10–₹18 LPA
  • Senior-level: ₹18–₹30 LPA or higher (for highly experienced professionals or those with expertise in niche areas like NLP, computer vision, or reinforcement learning)

2. Data Scientist

• Role: Data scientists leverage machine learning and statistical models to analyze large datasets and provide actionable insights. They use AI techniques to automate and improve data-driven decision-making.
• Skills Required: Machine learning, statistical analysis, Python, R, SQL, big data technologies (Hadoop, Spark), and data visualization tools.
• Salary:
  • Entry-level: ₹6–₹12 LPA
  • Mid-level: ₹12–₹20 LPA
  • Senior-level: ₹20–₹35 LPA (senior data scientists or those in managerial roles can earn higher)

3. AI Research Scientist

• Role: AI Research Scientists focus on advancing the theoretical foundations of AI, developing new algorithms, and improving existing AI models. They may work in academic research or corporate R&D departments.
• Skills Required: Deep understanding of AI theory, machine learning algorithms, mathematics, and strong programming skills in Python, Java, or C++.
• Salary:
  • Entry-level: ₹8–₹12 LPA
  • Mid-level: ₹12–₹25 LPA
  • Senior-level: ₹25–₹50 LPA or more (especially in tech giants like Google, Microsoft, or research institutions)

4. Data Engineer

• Role: Data Engineers focus on preparing and optimizing data for machine learning models. They build and maintain data pipelines and work with large databases to ensure data is clean and accessible for analysis.
• Skills Required: Big data technologies, SQL, ETL processes, cloud platforms (AWS, Azure, GCP), Python, and data warehousing tools.
• Salary:
  • Entry-level: ₹6–₹10 LPA
  • Mid-level: ₹10–₹18 LPA
  • Senior-level: ₹18–₹30 LPA (for senior or lead positions)

5. AI Software Developer

• Role: AI Software Developers create software applications powered by artificial intelligence. This includes building intelligent apps, integrating AI systems, and ensuring AI models work seamlessly within business systems.
• Skills Required: Strong programming knowledge (Python, Java, C++), AI frameworks, problem-solving skills, and familiarity with cloud services and databases.
• Salary:
  • Entry-level: ₹6–₹10 LPA
  • Mid-level: ₹10–₹18 LPA
  • Senior-level: ₹18–₹30 LPA (depending on project size, company, and expertise)

6. NLP Engineer

• Role: Natural Language Processing (NLP) Engineers specialize in developing systems that allow computers to understand, interpret, and generate human language. They work on chatbots, voice assistants, and other AI applications that require language understanding.
• Skills Required: NLP, machine learning, Python, deep learning frameworks, knowledge of linguistics, and libraries like NLTK, spaCy, or Hugging Face.
• Salary:
  • Entry-level: ₹6–₹12 LPA
  • Mid-level: ₹12–₹20 LPA
  • Senior-level: ₹20–₹35 LPA or higher (especially in AI-focused companies)

7. Computer Vision Engineer

• Role: Computer Vision Engineers design algorithms that allow machines to interpret and understand visual information from the world, such as in facial recognition, object detection, or autonomous vehicles.
• Skills Required: Image processing, deep learning, Python, TensorFlow, OpenCV, and knowledge of convolutional neural networks (CNNs).
• Salary:
  • Entry-level: ₹8–₹12 LPA
  • Mid-level: ₹12–₹20 LPA
  • Senior-level: ₹20–₹40 LPA (for highly skilled professionals with experience in cutting-edge technologies)

8. AI Consultant

• Role: AI Consultants advise businesses on implementing AI technologies to improve operations, automate processes, and drive innovation. They help companies understand how AI can benefit them and provide tailored solutions.
• Skills Required: Deep understanding of AI technologies, business acumen, communication skills, and knowledge of industry-specific applications of AI.
• Salary:
  • Entry-level: ₹8–₹12 LPA
  • Mid-level: ₹12–₹20 LPA
  • Senior-level: ₹20–₹40 LPA (for highly experienced consultants with multiple successful projects)

9. Business Intelligence Analyst

• Role: Business Intelligence Analysts use AI and machine learning to analyze business data and provide strategic insights. They help companies make data-driven decisions to improve profitability and business efficiency.
• Skills Required: Data analysis, machine learning, business analytics, Python, SQL, data visualization tools like Tableau or Power BI.
• Salary:
  • Entry-level: ₹5–₹8 LPA
  • Mid-level: ₹8–₹15 LPA
  • Senior-level: ₹15–₹25 LPA

10. AI Product Manager

• Role: AI Product Managers oversee the development and deployment of AI-powered products. They work closely with engineering, data science, and marketing teams to ensure the product meets user needs and performs effectively.
• Skills Required: Product management, knowledge of AI technologies, strong communication and leadership skills, project management tools.
• Salary:
  • Entry-level: ₹10–₹15 LPA
  • Mid-level: ₹15–₹25 LPA
  • Senior-level: ₹25–₹40 LPA (for experienced product managers at leading AI companies)

11. AI/ML Trainer or Educator

• Role: AI/ML Trainers develop and teach educational programs in AI and machine learning, providing training to students, professionals, or corporate teams.
• Skills Required: Expertise in AI/ML, teaching skills, knowledge of training platforms, and experience in curriculum development.
• Salary:
  • Entry-level: ₹6–₹10 LPA
  • Mid-level: ₹10–₹18 LPA
  • Senior-level: ₹18–₹30 LPA (for well-known trainers or those running successful educational programs)

Salary Overview

• Entry-level: ₹6–₹12 LPA (for fresh graduates or those with limited experience)
• Mid-level: ₹12–₹20 LPA (for professionals with 3-5 years of experience)
• Senior-level: ₹20–₹40 LPA or more (for those with advanced skills, niche expertise, or leadership roles)

Industry Growth and Job Outlook

India is a growing hub for AI and machine learning technologies, with significant investments being made in AI-driven solutions across sectors such as healthcare, finance, automotive, e-commerce, and education. As companies adopt AI for automation and innovation, the demand for skilled professionals in AI/ML is projected to increase.

The salary potential for AI and machine learning professionals is also growing due to the demand for expertise in this rapidly evolving field, and professionals with specialized skills in deep learning, natural language processing, computer vision, and reinforcement learning are especially sought after.

Conclusion

Graduates of the Advanced Diploma in AI and Machine Learning program can pursue various high-paying roles across industries. The rapid advancement in AI technologies is driving a surge in job opportunities and career growth in the AI and machine learning space. By acquiring expertise in machine learning algorithms, data processing, and artificial intelligence applications, graduates are well-positioned for a rewarding career in one of the most dynamic fields of technology today.