Deepak kumar Machine learning, python, Data science
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Interactive Learning:
Jupyter Notebooks: Combine explanations, code, and outputs.
Interactive Platforms: Use Codecademy, LeetCode, or Kaggle.
Hands-On Coding:

Coding Exercises: Regular practice on sites like HackerRank.
Pair Programming: Collaborate to solve problems.
Project-Based Learning:

Real-World Projects: Solve practical problems (e.g., recommendation systems).
Capstone Projects: Comprehensive end-of-course projects.
Incremental Complexity:

Start Simple: Build from basics to complex topics.
Scaffold Learning: Concepts build on each other.
Conceptual Understanding:

Visual Aids: Diagrams and flowcharts for algorithms.
Analogies: Use real-life comparisons.
Continuous Assessment:

Quizzes and Tests: Regular checks for understanding.
Code Reviews: Feedback on coding style and logic.
Engaging Content:

Gamification: Badges, leaderboards, and achievements.
Storytelling: Explain data flow and algorithms narratively.
Community and Support:

Discussion Groups: Forums for questions and collaboration.
Mentorship: Guidance and feedback from experienced mentors.
Blended Learning:

Online and Offline Mix: Combine videos, tutorials, and classroom activities.
Flipped Classroom: Review materials at home, practice in class.
Continuous Learning:

Up-to-Date Curriculum: Incorporate latest trends.
Encourage Exploration: Use additional resources and participate in competitions.

Subjects

  • Python for Data Science Beginner-Expert

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

Experience

No experience mentioned.

Education

  • M.tech (Jul, 2022–Jun, 2024) from IIT JODHPUR
  • B.TECH (Jul, 2014–Jun, 2018) from National Institute of Technology, Kurukshetra, Haryana

Fee details

    ₹250–500/hour (US$2.97–5.95/hour)

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