AI and ML Course with Python by Rohan Pandey

This Artificial Intelligence and Machine Learning (AI & ML) Online Course is designed to help learners build strong foundations and advance toward real-world AI applications. The course covers everything from core machine learning concepts to modern topics like deep learning, generative AI, and AI tools used in industry.

It is ideal for students, aspiring data scientists, programmers, and professionals who want to develop practical AI skills. The program focuses on both theoretical understanding and hands-on implementation, enabling learners to work confidently with real datasets and machine learning models.

With a structured learning path and project-based approach, students will gain a clear understanding of how AI technologies are applied across industries such as healthcare, robotics, finance, and automation.

What Students Will Learn

By enrolling in this course, learners will develop expertise in:

Machine Learning Fundamentals

• Introduction to Artificial Intelligence and Machine Learning

• Types of machine learning: supervised and unsupervised learning

• Regression and classification techniques

• Clustering and dimensionality reduction

• Model evaluation methods and cross-validation

Core Machine Learning Algorithms

• Linear regression and classification models

• Decision trees and ensemble methods

• Support Vector Machines (SVM)

• K-Nearest Neighbors (KNN)

• Introduction to neural networks

Deep Learning Concepts

• Artificial neural networks (ANN)

• Convolutional Neural Networks (CNN) for computer vision

• Recurrent Neural Networks (RNN) for sequence data

• Introduction to transformer architectures

Advanced AI Topics

• Natural Language Processing basics

• Sentiment analysis and text processing

• Computer vision applications

• Reinforcement learning fundamentals

• Generative AI concepts including LLMs and GANs

AI Tools & Industry Practices

• AI development using Python

• Model selection and hyperparameter tuning

• Version control using Git

• AI ethics and bias mitigation

• Scalable tools like TensorFlow and PySpark

Practical Projects

• Hands-on capstone projects

• Real-world case studies such as AI applications in automation

• Building AI solutions for real datasets

Teaching Method

The course is delivered through live online interactive sessions focusing on practical implementation. Teaching methods include:

• Concept-driven explanations

• Real-time coding demonstrations

• Hands-on exercises and assignments

• Project-based learning approach

• Case study discussions

• Doubt-clearing and personalized guidance

This ensures learners gain both theoretical knowledge and practical AI development skills.

Why This Tutor

The tutor follows a practical, industry-oriented teaching style that emphasizes real-world applications of AI rather than purely theoretical learning. The sessions are structured to help students build confidence in applying machine learning techniques independently.

Benefits & Outcomes

After completing this course, learners will:

• Understand core AI and machine learning concepts

• Gain practical experience in building ML models

• Learn modern deep learning and generative AI techniques

• Develop strong data analysis and problem-solving skills

• Prepare for careers in AI, data science, and analytics

• Build a portfolio of real-world AI projects

This course provides a strong foundation for entering the rapidly growing field of artificial intelligence.