🚀 Learn Artificial Intelligence & Machine Learning with Experts Near You
Looking for AI training near Panchkula or AI ML coaching near Chandigarh, Mohali, or Zirakpur? 🤔 At Inx Infotech, we bring you the best AI and ML courses to prepare you for the future. As one of the most trusted AI training centers in Panchkula, we ensure you gain both practical skills and deep theoretical knowledge to stand out in the tech world! 🌍
👉 Learn about our AI Course
👉 Explore the ML Course Details
👉 Strengthen your basics with Math Foundation Course
💡 Why Choose inx Infotech for AI & ML Courses?
✔️ Top AI Training Institute Near You: Conveniently located in Panchkula, we’re a top choice for students from Chandigarh, Mohali, Zirakpur, and beyond!
✔️ Expert Faculty: Learn from AI & ML experts with years of hands-on experience.
✔️ Beginner to Advanced Curriculum: Cover everything from foundational concepts to advanced machine learning algorithms 🤖.
✔️ Real-World Projects: Gain hands-on experience working on real-world projects to strengthen your resume 💼.
✔️ Accessible Learning Location: Ideal for learners across the Tricity region searching for AI ML coaching near me or AI classes nearby!
📚 What You’ll Learn in Our AI & ML Training Courses
- Machine Learning Basics: Understand how AI and ML are transforming industries.
- Python for AI & ML: Learn Python, the most widely used programming language in AI.
- Data Science Foundations: Master concepts like data pre-processing, visualization, and feature engineering 📊.
- AI Algorithms: Explore neural networks, decision trees, and deep learning fundamentals 💡.
🎓 Course Prerequisites – Build Your Foundation First!
To ensure you excel in our courses, we recommend a solid foundation in:
💻 Python Programming: Python is a must for AI & ML. Don’t worry – we also offer a popular Python course near Panchkula that’s highly rated in the region! 🌟
📐 Math for Machine Learning: Build confidence in math concepts like calculus, statistics, and linear algebra. Our specialized Foundational Math for ML course is here to help. 📈
👩💻 Who Should Join?
Our courses are tailored for:
- 🧑💼 Professionals: Upgrade your career in AI and machine learning.
- 🎓 Students: Start your journey toward becoming a data scientist or ML engineer.
- 🤓 Enthusiasts: Curious minds eager to explore the exciting world of artificial intelligence.
📍 Learn AI & ML Near Panchkula, Chandigarh, Mohali, and Zirakpur
Our institute is conveniently located in Panchkula 🏙️, making it accessible for students from nearby cities. When you search for AI ML courses near me, Inx Infotech is your go-to destination for quality AI and ML training.
🎉 Take the First Step Towards a Bright Future in AI!
Don’t miss the chance to lead the future of technology 🚀. Enroll in our AI and ML training courses today and gain industry-relevant skills that make you job-ready.
📞 Contact Us: Call us now at 09815944904 ☎️ or
visit inxinfotech.in/ai-ml-courses-panchkula to learn more.
Mathematical Foundation for AI & ML 🧮✨
Module 1: Basics of Linear Algebra (Foundation for ML) 📐
- Introduction to Linear Algebra in ML
- 🤔 Why do we need linear algebra in ML?
- 🌟 Real-world examples (e.g., Image Processing, NLP, Recommendation Systems)
- Vectors and Their Operations ➡️
- 📌 What are vectors?
- 📊 Vector representation (geometric & numerical)
- ➕ Operations: Addition, Subtraction, Scalar Multiplication
- 🔍 Dot product (How it helps in similarity measurement)
- ❌ Cross product (Where it is used in ML)
- Matrices and Their Operations 🧩
- 🗂️ What are matrices?
- ➕ Matrix addition, subtraction, multiplication
- ✅ Identity matrix, inverse matrix
- 📏 Determinant of a matrix
- 🔄 Transpose of a matrix
- 🌍 Real-world ML applications (e.g., Transformations in Image Processing)
- Linear Transformations using Matrices 🔄
- 🖼️ Geometric transformations (Scaling, Rotation, Translation)
- 📐 Transformations in 2D and 3D
- 🤖 Connection to Neural Networks
- Solving Linear Equations Using Matrices 📝
- 🧮 Systems of linear equations
- 🔁 Matrix inversion method
- 🌟 Applications in ML
- Eigenvalues and Eigenvectors (Basic Understanding) 🔑
- 💡 Concept and intuition
- 🎯 Importance in ML (e.g., PCA – Principal Component Analysis)
Module 2: Probability & Statistics (Foundation for ML Models) 📊
- Basic Probability Concepts 🎲
- 🤔 Why probability in ML? (E.g., Spam Detection, Weather Prediction)
- ➕ Probability rules (Union, Intersection, Conditional Probability)
- 📚 Bayes’ Theorem (Basic idea, Used in Naive Bayes Classifier)
- Descriptive Statistics 📈
- 🧮 Mean, Median, Mode
- 📉 Variance and Standard Deviation
- 📏 Percentiles and Quartiles
- 💻 Use cases in ML (e.g., Feature Scaling)
- Probability Distributions 🎯
- 🔄 Normal Distribution (Used in Gaussian Naive Bayes)
- ✅ Bernoulli, Binomial, Poisson Distributions
- 🌟 Importance in ML algorithms
Module 3: Essential Calculus for ML 🧮
- Introduction to Calculus in ML
- 📚 Why calculus in ML? (E.g., Gradient Descent)
- ⚡ Derivatives and Rates of Change
- 🌟 Partial Derivatives and Gradient Calculation
- Optimization Techniques 🚀
- 🔄 Gradient Descent (Basic understanding, No heavy math)
- ⚙️ Learning Rate and Optimization Algorithms
Module 4: Regression Analysis (Foundation for ML Models) 📈
- Introduction to Regression in ML
- 🤔 What is regression?
- 🌟 Why is it important in ML?
- Linear Regression ➕
- 📏 Equation of a line (y = mx + c)
- 📐 Finding the best-fit line using Least Squares
- 💻 Cost function and Gradient Descent (Simple explanation)
- Polynomial & Multiple Regression 🌈
- ❌ When linear regression fails
- 📈 Polynomial regression (Curve fitting)
- 📊 Multiple regression (Multiple variables)
Module 5: Miscellaneous Math Concepts in ML 📚
- Distance Metrics in ML ➡️
- 📏 Euclidean Distance
- 📐 Manhattan Distance
- 🔍 Cosine Similarity (Used in NLP, Recommender Systems)
- Kernels and Kernel Trick in ML 🎯
- 🔄 Kernel functions (Used in SVM, Kernel PCA)
- 🤔 Why are they useful?
- Graph Theory Basics 📊
- 📉 Line graphs, their equations
- 🌐 Graph-based ML techniques (PageRank, Social Networks)
Final Module: Bringing It All Together 🔗
- How These Math Concepts Are Used in ML Algorithms
- 💡 Recap of key concepts
- 🎯 How they fit into ML models like SVM, Neural Networks, etc.
🤖 Machine Learning Course
Module 1: Introduction to Machine Learning
🌟 What is Machine Learning?
- Definition and key concepts
- Types of ML: Supervised, Unsupervised, Reinforcement Learning
- Real-World Applications of ML 🌍
- Examples in healthcare, finance, AI assistants, etc.
🔄 How ML Works (Pipeline Overview)
- Data collection and preprocessing 📊
- Feature selection and engineering 🛠️
- Model training and evaluation 🎯
Module 2: Essential Concepts in Machine Learning
📂 Datasets
- Training, testing, validation datasets
- Overfitting vs. underfitting ⚖️
📈 Features and Labels
- Understanding features (inputs) and labels (outputs)
- Categorical vs. numerical features
📊 Performance Metrics
- Accuracy, precision, recall, F1 score
- Mean squared error (MSE) and R² for regression
Module 3: Supervised Learning
📉 Linear Regression
- Understanding the equation y = mx + c
- Cost function and gradient descent
📊 Logistic Regression
- Classification vs. regression
- Sigmoid function and decision boundaries
🌳 Decision Trees and Random Forests
- How trees work (splitting on features)
- Bagging and boosting concepts
⚖️ Support Vector Machines (SVM)
- Hyperplanes and decision boundaries
🤖 Neural Networks Basics
- Introduction to perceptrons and multi-layer perceptrons
Module 4: Unsupervised Learning
🔗 Clustering Algorithms
- K-Means clustering
- Hierarchical clustering
📉 Dimensionality Reduction
- Principal Component Analysis (PCA)
- t-SNE for visualization
Module 5: Reinforcement Learning
🏆 Key Concepts
- Agent, environment, rewards
- Exploration vs. exploitation
📜 Basic Algorithms
- Q-Learning
- Policy gradient methods
Module 6: Deep Learning
🌊 Introduction to Deep Learning
- What makes deep learning different from ML?
🖼️ Convolutional Neural Networks (CNNs)
- Basics of image processing in ML
🔄 Recurrent Neural Networks (RNNs)
- Basics of sequence modeling
🚀 Transfer Learning
- Pre-trained models and their uses
Module 7: ML Deployment and Advanced Topics
💻 Model Deployment Basics
- Using frameworks like Flask, FastAPI, and Streamlit
⚠️ Common Challenges in ML
- Bias, fairness, and ethics
🤖 Introduction to Generative AI
- Basics of transformers, GPTs, etc.
🧠 AI Course with (PyTorch)
1. Introduction to Artificial Intelligence (AI) 🌟
- Definition of AI: What is AI, and how it differs from Machine Learning (ML) and Deep Learning (DL).
- Types of AI: Narrow AI, General AI, and Super AI.
- History of AI: Milestones and evolution of AI over time.
- Applications of AI: Real-world examples like virtual assistants, AI in healthcare, finance, and more.
2. Fundamentals of AI 📊
- AI vs. ML vs. DL: Understanding their relationship and differences.
- Core Concepts:
- Agents and Environments.
- Goal-based problem solving.
- Search techniques in AI (e.g., BFS, DFS, heuristic search).
- Knowledge Representation: Basics of how AI represents data, objects, and relationships.
3. Mathematics for AI 🍌
- Essential topics covered in the Mathematical Foundation for ML:
- Linear Algebra: Vectors, matrices, eigenvalues/eigenvectors.
- Probability: Basics of probability for AI decisions.
- Optimization: Gradients and their role in AI training.
4. AI Programming Basics 🔧
- Introduction to Python for AI:
- Popular libraries: NumPy, Pandas, Matplotlib.
- Overview of AI-specific libraries like TensorFlow and PyTorch.
- Working with Data:
- Loading and preprocessing datasets.
- Data cleaning and feature engineering basics.
5. Machine Learning in AI 🎭
- Quick Recap of ML Basics:
- Supervised vs. Unsupervised Learning.
- Introduction to Neural Networks.
- AI-Specific ML Applications:
- AI for predictions and recommendations (e.g., in finance, healthcare).
6. Deep Learning for AI Applications 🔄
- Overview of Deep Learning:
- What makes DL suitable for AI tasks.
- Differences between DL and traditional ML.
- Neural Network Architectures:
- Overview of Convolutional Neural Networks (CNNs) for vision tasks.
- Overview of Recurrent Neural Networks (RNNs) for sequential data.
7. Applications of Generative AI 🎶
- Introduction to Generative Models:
- Generative AI for creative tasks (music, video game design).
- Applications in text generation, art, and more.
- Tools for Generative AI:
- Overview of tools like DALL-E, ChatGPT, etc.
8. Reinforcement Learning (AI-Specific) 🚗
- What is Reinforcement Learning (RL)?:
- Basic concepts of agents, rewards, and actions.
- Examples in robotics and decision-making (e.g., self-driving cars).
- Real-World Applications:
- Logistics optimization, video game AI, autonomous systems.
9. Advanced Computer Vision (AI-Specific) 👀
- Overview of Computer Vision in AI:
- AI-specific applications like surveillance video analysis.
- Use in augmented reality and AI-enhanced video editing.
- Tools and Techniques:
- Overview of pre-trained models and AI toolkits for vision tasks.
10. Natural Language Processing (NLP) 🖋
- Introduction to NLP:
- Basics of text processing and language modeling.
- Applications like chatbots, virtual assistants, and sentiment analysis.
- Advanced NLP:
- Overview of transformers and models like BERT and GPT.
11. PyTorch Module for AI Applications 🔧
- Introduction to PyTorch:
- Basics of tensors and operations.
- How PyTorch differs from TensorFlow.
- Building AI-Specific Models:
- Task 1: Natural Language Reasoning (using pre-trained language models).
- Task 2: Symbolic AI (working on reasoning systems).
- Advanced PyTorch Features:
- Custom datasets and dataloaders.
- Transfer learning with PyTorch.
12. AI Ethics and Challenges ⚖️
- Ethical Issues in AI:
- Bias in AI systems.
- Privacy concerns and ethical decision-making.
- Challenges of AI:
- Limitations of current AI technologies.
- Ensuring transparency and fairness in AI.
13. Capstone AI Project 🚀
- Building an AI System:
- Choose a domain (healthcare, finance, creative arts, etc.).
- Plan, design, and implement a small AI project using PyTorch or other tools.
- Evaluation and Presentation:
- Present findings and challenges.
- Discuss the impact and potential improvements.
14. The Future of AI 🔬
- Emerging Trends:
- AI in quantum computing.
- AI in sustainable development.
- How to Stay Updated:
- Resources for continuous learning (blogs, courses, and communities).
HEAD OFFICE:
Ansal Sampark
SCO 91-92-93
Sector-5
Panchkula (Opp. Vatika Garden)
BRANCH OFFICE:
S.C.F: 23
Harmilap Nagar
Zirakpur (Punjab).
(near Ind. Area Ph-II, Panchkula).
CONTACT INFO
Cell: 9815944904, 8360624240
Follow: @inxinfotech
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