Machine Learning and AI Training with IBM Certification

It is the best course in Machine Learning (ML) and Artificial Intelligence (AI) in Delhi from the house of ADMEC Multimedia. AI is a talk of the world which has revolutionized not only computers but everything of our lives. Our course is covering from basic to advanced techniques and algorithms used in ML & AI.

This course covers a few most important concepts of AI i.e. deep learning, neural networks, Natural Language Processing (NLP), Transfer Learning, Python, Statistics, ML essential math, databases, and everything needed. You will be fully supported in acquiring IBM certification after this course at no cost.

What is Machine Learning and AI

Machine learning is one of the most important parts of AI or artificial intelligence along with deep learning and natural language processing. In simple words, when a machine learns like a human is machine learning. There are multiple ways to make machines learn.

Why You should Join ML & AI

AI is the revolution whose effect is quite wide-spread. It has affected so many fields and everyone’s life. People are feared to be unemployed due to it if they don’t learn to use it. Machine learning is one of the most important building blocks of it. Check how it can contribute to personal and professional growth.

• The growing demand – The growing demand for ML and AI professionals is high. The reason behind this is the need of it in every sector.
• Impact of ML and AI – It has impacted various industries like agriculture, electronics, education, healthcare, finance, banking, service sector, automobile, computers, etc.
• Change is necessary – AI and ML is the need of time. Growing population is pushing to live fast life and AI can help in it for sure.
• Good for data analysis and GenAI development – Machine learning gives you strong foundation if interested in advanced data analysis or generative AI development.

Why Join from ADMEC Multimedia

ADMEC is a leader in professional courses training since 2006 in Rohini, Delhi. Our programming and database courses for web, software, and mobile apps are very popular. The track record of training and placement makes us the best institute to offer this course. Highlighting the unique benefits and opportunities provided by this course from the ADMEC.

• Comprehensive curriculum covering advanced topics.
• Hands-on projects and real-world case studies.
• Access to expert instructors and industry professionals.
• Networking opportunities with peers and industry leaders.
• IBM certification at no additional cost.

Prerequisites for Machine Learning and AI Course

Although this course doesn’t need any advanced skill as it starts from the scratch. Please check out the necessary background knowledge and skills required to successfully complete the course.

• 12th of Intermediate with Math or equivalent
• Strong interest in mathematics and algorithms
• Knowledge of any programming language is not required

Who Else can Join this Course

Although anyone with the above eligibility can join this course. But this course is very good for a set of people.

• Data scientists and analysts looking to advance their skills
• Software engineers and web developers interested in GenAI development
• Researchers and academicians in the field of ML and AI
• Students seeking to learn AI

What will you Learn

There are various techniques, tools, languages, and methodologies involved in it.

• Overview of advanced ML and AI
• Statistics and math essentials for AI
• Types of Machine Learning
  • Supervised Learning
  • Unsupervised Learning
  • Reinforcement Learning, etc.
• Deep Learning
  • Convolutional Neural Networks (CNNs)
  • Recurrent Neural Networks (RNNs)
  • Generative Adversarial Networks (GANs), etc.
• Transfer Learning and its applications
• Natural Language Processing (NLP) and its uses
• Database systems like MySQL and MongoDB
• Python Language and its libraries

MySQL, MongoDB, Python, Python Libraries, Statistics and Essential Math for ML, advanced ML, NLP, Deep Learning, etc.

Course Content of Machine Learning Course

• Module 1: Databases and Python Programming for ML (8 Weeks)
• Module 2: Foundations of Machine Learning (6 Weeks)
• Module 3: Advanced ML, NLP, and Types of Machine Learning (6 Weeks)
• Module 4: Introduction to Deep Learning & Applications (6 Weeks)
• Module 5: Capstone Project (4 Weeks)
• Module 6: Interview Preparation and Portfolio (2 Weeks)

Detailed breakdown of the modules covered in the course.

1. Introduction to Machine Learning

• What is Machine Learning
• Machine Learning vs Deep Learning vs Data Science
• Application of Machine Learning
• Types of Machine Learning
  • Supervised
  • Unsupervised
  • Reinforced
• Basic ML concepts
• Overview of advanced ML techniques

2. Setup of Essential Tools and Languages

• Python
• Python Libraries
• Jupyter Notebook
• IDE
• SQL

3. Exploring Databases

• Database Systems
• SQL and NoSQL Databases
• MongoDB
• MySQL

4. Learning Python

5. Python Libraries for ML & AI

• Core Machine Learning (ML)
  • Scikit-learn – classification, regression, clustering, etc.
  • XGBoost – Top boosting algorithm
• NLP – Python Libraries
  • spaCy
  • Transformers (by Hugging Face)
• Deep Learning Python Libraries
  • TensorFlow (by Google)
  • Keras
  • PyTorch (by Meta)
  • FastAI
• Computer Vision (CV)
  • OpenCV
  • YOLO
• Data Manipulation & Visualization
  • NumPy
  • Matplotlib
  • Pandas
  • Seaborn
• Utilities & Tools
  • Pickle / Joblib – Save and load ML models
  • Optuna – Powerful hyperparameter tuning library

6. Statistics Basics

• What is Statistic?
• Types of Statistics
• Measurements of Central Tendency
• Measurements of Dispersion
• Types of Data and Graphs
• Probability

7. Machine Learning Essentials in Mathematics

• Calculus
• Algebra
• Discrete Mathematics

8. Machine Learning

• Life-cycle of Machine Learning
• Bias – variance Trade-off
• Overfitting & Underfitting
• Supervised learning – Regression and Classification
  • Regression
    • Linear Regression
      • Model representation (Linear Equation)
      • Cost function
    • Optimization
      • Gradient Descent
      • Batch & Mini Batch
      • Stochastic
      • Confusion Matrix
      • Bias-Variance Trade-off
      • Multiple Linear Regression
      • Polynomial Regression
      • Overfitting & Underfitting
      • Support Vector Regression (SVR)
      • Decision Tree Regression
      • Random Forest Regression
  • Classification
    • Logistic Regression
    • K-Nearest Neighbors
    • Support Vector Machine (SVM)
    • Kernel SVM
    • Naïve Bayes
    • Decision Tree Classification
    • Random Forest Classification
• Unsupervised Learning
  • Clustering
    • K-Means Clustering
    • Hierarchical Clustering
  • Association Rule Mining
    • Recommendation Systems
    • Apriori
    • Eclat
  • Dimensionality Reduction
    • Principal Component Analysis (PCA)
    • Linear Discriminant Analysis (LDA)
    • Kernel PCA
• Regularization – to reduce errors and to increase accuracy
  • Regularization
    • Ridge
    • Lasso
• Model selection – to solve real life problem we use MLL algorithm
  • K-Fold Cross Validation
  • Grid Search
• Ensemble Learning
  • Bagging & Boosting
    • XGBoost
• Reinforcement Learning
  • Types of Reinforcement Learning
    • Positive
    • Negative
• Elements of Reinforcement Learning
  • Policy
  • Reward function
  • Value function
  • Model of the environment
• Upper Confidence Bound (UCB)
• Epsilon-Greedy Algorithm
• Actor-Critic Algorithm
• Thompson Sampling
• Markov Decision Process
• Bellman Equation
• Applications of Reinforcement Learning

9. Natural Language Processing (NLP)

• Approaches to NLP and Techniques
• NLP Pipelines
• Text Processing and Features Extraction
• Text Representation
• Word2Vec in NLP
• Classification of Text in NLP
• POS – Part of Speech Tagging
• Language Models and Transformers
• Sentiment Analysis and Text Generation
• Challenges for NLP
• Applications of NLP
• Project on NLP

10. Deep Learning

• Fundamentals of Neural Networks
• Neural network and Its Types
  • Feedforward Neural Networks (CNNs)
  • Convolutional Neural Networks (CNNs)
  • Generative Adversarial Networks (GANs)
    • Conditional GANs – CGANs
    • Super Resolution GAN – SRGAN
  • Kohonen Self Organising
  • Recurrent Neural Networks (RNNs)
  • Autoencoders and Variational Autoencoders
• Cost Function
• Gradient Descent
  • Stochastic Gradient Descent
• Softmax Function

11. Transfer Learning and Domain Adaptation

• Concepts of transfer learning
• Pre-trained models and fine-tuning
• Domain adaptation techniques
• Practical applications

12. Generative AI: Building Creative Machines

Introduction to Generative AI

• What is Generative AI?
• Discriminative vs. Generative models
• Types of GenAI:
  • Text Generation
  • Image Generation
  • Audio/Video Generation
• Real-life use cases: Art, animation, medicine, marketing
• Brief history: GANs → Transformers → Diffusion models

Activity: Case study analysis of ChatGPT, DALL·E, Midjourney

Understanding Generative Models

• Introduction to:
  • GANs (Generative Adversarial Networks)
  • VAEs (Variational Autoencoders)
  • Transformers (used in GPT, BERT, etc.)
  • Diffusion Models (used in Stable Diffusion, Midjourney)
• Architecture overview and core math (high-level)
• Tools: TensorFlow, PyTorch demos

Assignment: Explore a pre-trained GAN using PyTorch or Hugging Face and modify inputs

Practical Applications of GenAI

🛠 Text Generation:

• ChatGPT, Bard, Claude
• Prompt Engineering 101
• Fine-tuning GPT models with Hugging Face

🎨 Image Generation:

• Midjourney, DALL·E, Firefly, Leonardo.ai
• Prompts and parameters
• Model outputs and variations

🎬 Video & Audio Gen:

• Tools: RunwayML, Pika Labs, ElevenLabs, Murf.ai
• Use in storytelling, branding, education

Activity: Students create one asset using any GenAI tool (image, voiceover, or video)

13. Ethics and Fairness in AI

• Understanding bias in ML models
• Fairness and accountability
• Ethical considerations in AI applications

14. Capstone Project

• Real-world project implementation
• End-to-end model development
• Presentation and evaluation

Career in Machine Learning and AI

It is engineering, it is science, it is programming, and it is too much. AI is the modern career for today’s youth. AI and ML has made things smarter, so it is a smart career option for smart people. Although there is not a single company which is not using Machine Learning yet you should look at a few best companies and industries utilizing it.

• Technology giants like Microsoft, Google, Facebook, and Amazon.
• Healthcare companies using AI for diagnostics and treatment plans.
• Financial institutions leveraging ML for fraud detection and risk management.
• Automotive industry using to make better and safer vehicles.
• Retail and e-commerce for personalized recommendations.
• News channels using for AI anchoring.

Career Options in ML and AI

Our Machine Learning and AI course offers an ocean of career options to you with very high packages because it comes with IBM certification. Please explore the various career paths and job roles available to those who complete the course.

• Machine Learning Engineer
• Data Scientist
• AI Research Scientist
• NLP Specialist
• AI Product Manager
• Deep Learning Engineer
• Consultant in AI and ML
• Academic Opportunities