Machine learning for beginners Course by Ronak Jha

The Machine Learning for Beginners Using Python course is a step-by-step training program designed specifically for learners who are new to artificial intelligence and data science. This course provides a clear and structured pathway to understanding how machine learning works in real-world scenarios — without requiring any prior experience in the field.

Starting from Python fundamentals and progressing toward building real predictive models, this course helps students develop both conceptual clarity and practical implementation skills. Learners will understand how data is processed, how algorithms learn patterns, and how machine learning solutions are applied to solve real business problems.

This course is ideal for school and college students, aspiring data scientists, beginners in programming, and professionals looking to transition into the data science domain.

What Students Will Learn

By the end of this course, students will be able to confidently understand and build machine learning models from scratch.

Module 1 — Python Basics for ML

Introduction to Python for Data Science

NumPy basics

Pandas for data handling

Data visualization using Matplotlib

Working in Jupyter Notebook / Google Colab

Goal: Become comfortable with Python tools used in ML.

Module 2 — Introduction to Data Science

What is Data Science & Machine Learning

Types of Machine Learning (Supervised vs Unsupervised)

Understanding datasets, features & target variable

Data cleaning and preprocessing

Goal: Understand how real-world data looks.

Module 3 — Data Preprocessing (Very Important)

Handling missing values

Feature scaling (Standardization & MinMax scaling)

Train-test split

Avoiding data leakage

Goal: Learn how to prepare data before training models.

Module 4 — Supervised Machine Learning

Students will learn and implement:

Linear Regression (House price prediction)

Logistic Regression (Classification)

Decision Trees

Random Forest (Very popular algorithm)

Goal: Build prediction models from scratch.

Module 5 — Model Evaluation

Accuracy, Precision, Recall

Confusion Matrix

Overfitting vs Underfitting

Cross-validation

Goal: Learn how to check if ML model is good or bad.

Module 6 — Unsupervised Learning

K-Means Clustering

Real project: Customer segmentation

Goal: Learn how ML works without labels.

Module 7 — Final Real Project

Students will build a complete ML project:

Data cleaning

Model training

Model evaluation

Final prediction

Teaching Method

This course is delivered through live online interactive classes, ensuring students receive hands-on learning support. The teaching approach includes:

• Beginner-friendly explanations with real examples

• Step-by-step coding demonstrations

• Practical exercises and assignments

• Real-world mini projects

• Continuous doubt-clearing support

• Guided final project implementation

The teaching style focuses on making complex ML concepts simple and approachable.

Why This Tutor

The tutor emphasizes a practical learning approach that helps beginners understand not just theory but also real implementation. The teaching focuses on simplifying technical topics, building confidence in coding, and guiding students through real-world problem solving using machine learning techniques.

Benefits & Outcomes

After completing this course, students will:

• Build strong foundations in machine learning and data science

• Gain hands-on experience with Python ML tools

• Learn to create predictive models independently

• Understand real-world data processing workflows

• Develop skills relevant to AI and data science careers

• Complete a portfolio-ready machine learning project

This course serves as an excellent starting point for learners planning to pursue advanced data science, AI, or analytics training.