Atul Kumar Pandey AI, ML,Deep learning, Mathematics and statistic
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I with my collegeaue offer structured, in-depth training in AI, ML, and Deep Learning aligned with best standards and industry expectations. The course is designed for Graduation, Post Graduation, students, research aspirants, and working professionals

๐Ÿ“˜ ARTIFICIAL INTELLIGENCE (Advanced Foundations)

๐Ÿ”น Mathematical Foundations
Linear Algebra for Machine Learning
Probability & Statistics
Multivariable Calculus
Optimization Techniques
Convex Optimization

๐Ÿ”น Core AI Topics

Intelligent Agents & Rational Decision Making
State Space Search (BFS, DFS, A*)
Heuristics & Adversarial Search
Constraint Satisfaction Problems
Knowledge Representation & Reasoning
Propositional & First-Order Logic
Bayesian Networks
Markov Decision Processes (MDP)
Reinforcement Learning (Intro to Advanced Concepts)

๐Ÿ“Š MACHINE LEARNING (Academic + Practical Depth)
๐Ÿ”น Supervised Learning

Linear & Logistic Regression (with mathematical derivations)
Bias–Variance Tradeoff
Regularization (L1, L2)
K-Nearest Neighbors (KNN)
Decision Trees & Random Forest
Support Vector Machines (including kernels)
Naive Bayes

๐Ÿ”น Unsupervised Learning
K-Means & K-Medoids
Hierarchical Clustering
Gaussian Mixture Models
Expectation–Maximization Algorithm
Principal Component Analysis (PCA)
Dimensionality Reduction Techniques

๐Ÿ”น Advanced Machine Learning (Master’s Level)

Ensemble Learning
Boosting (AdaBoost, Gradient Boosting, XGBoost)
Hidden Markov Models
Graphical Models
Statistical Learning Theory
PAC Learning
Model Selection & Cross-Validation

๐Ÿ”น Practical Implementation Using

Scikit-learn
TensorFlow
PyTorch

๐Ÿง  DEEP LEARNING (Industry + Research Oriented)

Neural Networks (from scratch with derivations)
Backpropagation (mathematical understanding + coding)
Optimization Techniques (SGD, Adam, RMSProp)
Convolutional Neural Networks (AlexNet, VGG overview)
RNN, LSTM, GRU
Attention Mechanism
Transformers (Fundamentals)
Natural Language Processing Basics
Word Embeddings
Computer Vision Applications
Transfer Learning
Generative Models (Autoencoders, GAN – Introduction)

Conceptual understanding aligned with systems built by leading AI organizations such as OpenAI.

๐ŸŽ“ Additional Advanced / Research Track Topics

Reinforcement Learning (Q-Learning, Policy Gradients)
Deep Reinforcement Learning
Time Series Forecasting
Recommender Systems
Explainable AI (XAI)
Ethics in AI
Research Paper Reading & Interpretation
Thesis, Dissertation & Major Project Support

๐Ÿ›  Teaching Methodology

Strong emphasis on mathematical intuition
Whiteboard explanations + live coding sessions
Problem-solving approach
Regular assignments
Mini-projects + Capstone projects
Interview & Viva preparation
Dedicated 1:1 mentorship available

๐Ÿ’ผ About Me

5+ years of teaching
Mentored B.Tech, M.Tech, and working professionals
Guided students for placements, higher studies, and research
Structured roadmap from beginner level to research depth

Subjects

  • Calculus Beginner-Expert

  • Linear Algebra Beginner-Expert

  • Deep Learning Beginner-Expert

  • Statistics and Probability Beginner-Expert

  • Neural Networks Beginner-Expert

  • NLP basics Beginner-Expert

  • Deep learning with Python programming Beginner-Expert

  • Machine learning Python Beginner-Expert

  • ML, DL, Neural Networks, and OpenCV in Python Beginner-Expert

  • Python for Data science, Machine Learning and Artificial Intelligence Beginner-Expert

  • AI and ML Beginner-Expert

  • Machine and Deep Learning project Beginner-Expert

  • NLP Project Beginner-Expert

Experience

  • Atal tinkering Lab Instructor (Apr, 2025–Present) at SunBeam school
    I am helping students build lots of AI and ML Projects which Can solve real world problems and guiding them to write Research Papers
  • Projects Freelancer (Jan, 2021–Mar, 2025) at Freelancer.com
  • Academic Teacher (May, 2019–Apr, 2020) at Ideal Public School
    I was teaching several technical subjects along with Mathematics, physics and Chemistry.

Education

  • Machine Learning for engineering and Science Application (Jul, 2022–Oct, 2022) from IIT MADRAS
  • Deep Learning With Python (Jan, 2022–Mar, 2022) from NIT WARANAGAL
  • Machine Learning With Python (Oct, 2021–Dec, 2021) from NIT WARANAGAL
  • Introduction to machine Learning (Jan, 2020–May, 2020) from IIT MADRAS
  • Control Systems (Jan, 2018–May, 2018) from IIT MADRAS
  • B Tech (Jul, 2015–Jun, 2019) from kashi institute of technology, varanasi

Fee details

    โ‚น1,500–5,000/hour (US$15.79–52.63/hour)

    When i take extra classes, I will Charge 4500/hr

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