– Beginner to Advanced (With Python)

Machine Learning

This course is designed for students, job seeker

Machine Learning – Beginner to Advanced (With Python)

Welcome to Machine Learning by Codes With Pankaj, a complete beginner-to-advanced online course designed to help students and working professionals master Machine Learning using Python through clear theory, hands-on coding, and real-world projects.

Book Demo

Hours

Modules

Lessons

Online Learning

Course Overview

This course takes you step by step from Machine Learning fundamentals to advanced model building and deployment.
You will learn:

Core Machine Learning concepts explained in simple language
Hands-on implementation using Python
Popular ML algorithms with real datasets
Model evaluation, tuning, and optimization
End-to-end ML projects
Model deployment and Streamlit-based web apps

By the end of the course, you will be able to build, evaluate, and deploy Machine Learning models confidently for real-world problems.

Course Highlights

Students can learn online at their own pace and gain industry-relevant skills that are actually used in real projects.s, and professionals who want to master Machine Learning from scratch using Python. It covers theory + practical implementation + projects, progressing from beginner to advanced topics.

Target Students

Who Is This Course For?

Anyone tired of theoretical ML and wants hands-on, implementation-focused learning
Anyone who wants to start a career in Machine Learning
Developers and programmers looking to add ML skills to their toolkit
Data analysts who want to move into predictive modeling and AI
Students and professionals interested in real-world, practical ML projects
Anyone tired of theoretical ML and wants hands-on, implementation-focused learning

Prerequisites

What You Should Know First

To get the most out of this course, you should be comfortable with:

Basic Python programming
Fundamental concepts of mathematics (basic algebra and statistics)
Working with data using lists, arrays, and DataFrames

Helpful but not mandatory:

Basic knowledge of NumPy and Pandas
Some familiarity with data analysis or programming

Course Highlights

Beginner to Advanced

Beginner to Advanced structured learning
58+ Hours of Content
13 Modules | 130+ Lessons
Practical coding with real datasets
Assignments and mini-projects
Model Deployment + Streamlit projects
100% Online Learning

No prior experience in Machine Learning or AI is required.
All core concepts—from fundamentals to model deployment—are explained step by step.

A Perfectly Structured Course

Course Content

58 Hours . 13 Sections . 130 Lessons

Module 1: Introduction to Machine Learning

1. What is AI, ML, DL ?
2. Types of ML (Supervised, Unsupervised, Reinforcement)
3. ML Workflow (Data → Model → Evaluation → Deployment)
4. Parametric vs Non-Parametric Algorithms
5. Use Cases of ML in Real Life
6. Assignment 1: Identify ML applications around you

Module 2: Exploratory Data Analysis (EDA)

1. Data Overview (info, shape, describe)
2. Handling Missing Values
3. Handling Outliers
4. Handling Skewness
5. Data Encoding (Label, One-Hot, Ordinal)
6. Feature Scaling (Normalization, Standardization)
7. Feature Engineering (Feature Creation & Selection)
8. Correlation Analysis & Heatmaps
Assignment 2: Perform EDA on a real dataset

Module 3: Regression Models

1. Simple Linear Regression
2. Multiple Linear Regression
3. Polynomial Regression
4. Regularisation ,Lasso & Ridge
5. Model Evaluation Metrics (R²-score, MSE, MAE, RMSE)

Logistic Regression

1. Maximum Likelihood Estimation
2. Performance Metrics (Confusion Matrix, Precision, Recall, F1, ROC AUC)

Module 4: Tree-based Models

Decision Trees

1. Entropy, Information Gain, Gini Index
2. CART vs CHAID
3. Pruning Techniques
4. Performance Metrics

Random Forest

1. Bagging & Bootstrap Sampling
2. Feature Importance
3. OOB Score
4. Hyperparameter Tuning
Assignment 4: Build decision tree & random forest on Heart Disease dataset

Module 5: Ensemble Techniques

1. Bagging vs Boosting vs Stacking
2. AdaBoost
3. Gradient Boosting
4. XGBoost

Module 6: Nearest Neighbours (KNN)

1. Concept of KNN
2. Distance Metrics (Euclidean, Manhattan, Cosine)
3. Choosing Optimal K (Elbow Method)
4. KNN in Classification vs Regression
Assignment 6: Implement KNN on Iris dataset

Module 7: Support Vector Machines (SVM)

1. Understanding Hyperplanes & Support Vectors
2. Hard vs Soft Margin
3. Kernels (Linear, Polynomial, RBF, Sigmoid)
4. SVM for Regression (SVR)
5. Multi-class SVM (One-vs-One, One-vs-Rest)
Assignment 7: Implement SVM with kernels on Breast Cancer dataset

Module 8: Naive Bayes

1. Bayes Theorem
2. Gaussian, Multinomial, Bernoulli NB
3. Applications (Spam Filtering, Text Classification)
Assignment 8: Spam/Ham email classification using Naive Bayes

Module 9: Dimensionality Reduction

1. Curse of Dimensionality
2. PCA (Steps, Explained Variance)
3. LDA (Linear Discriminant Analysis)
4. t-SNE & UMAP (Intro)
Assignment 9: Apply PCA on MNIST dataset

Module 10: Clustering

K-Means Clustering

1. Working of Algorithm
2. Choosing K (Elbow Method, Silhouette Score)
3. Distance Measures

Hierarchical Clustering

1. Agglomerative vs Divisive
2. Dendrograms
3. Linkage Criteria

DBSCAN

1. Density-based clustering
2. Epsilon & MinPts
Assignment 10: Apply clustering on customer segmentation dataset

Module 11: Model Evaluation & Tuning

1. Train-Test Split & Cross Validation
2. Bias-Variance Tradeoff
3. Hyperparameter Tuning (GridSearchCV, RandomSearchCV, Optuna)
4. Feature Importance (SHAP, LIME)
Assignment 11: Hyperparameter tuning on Random Forest & XGBoost

Module 12: Model Deployment

1. Model Deployment Basics
2. Introduction to Streamlit for ML Model Deployment
3. Web Application for ML Predictions

Module 13: Model Streamlit

1. Introduction to Streamlit
2. Streamlit Installation and Setup
3. Streamlit Basics
4. Streamlit Input Widgets
5. Layout and Design in Streamlit
6. Streamlit with Machine Learning
7. Data Visualization in Streamlit
8. Streamlit Session State
9. Streamlit Performance Optimization
10. Deployment of Streamlit Apps
11. Error Handling and Debugging
12. Streamlit Best Practices

Recommended Prerequisite Courses

If you need to strengthen your foundation, we recommend:

Python for Beginners
Python for Data Analysis (NumPy & Pandas)
Basic Statistics for Data Science
Introduction to Data Analysis

Our Mission

– Learn, Apply, Innovate !

Why Learn with Me ?

Hi, I’m Pankaj Chouhan, a software engineer with 10+ years of experience, passionate about helping others master coding and problem-solving. My mission is to bridge the gap between theory and real-world application, making coding simple, engaging, and career-focused.

Hands-On Training – Work on real-world projects to gain practical skills.
Beginner to Pro – Courses for all levels, from first-timers to professionals.
Job-Ready Skills – Learn what tech companies actually look for.
Engaging & Fun – No boring lectures, just interactive, practical learning.
Industry Insights – Stay updated with best coding practices, tips, and trends.
Community Support – Join a thriving community of like-minded learners.

💡 Whether you’re learning your first programming language or sharpening advanced skills, Codes With Pankaj provides a structured, fun, and effective way to succeed.

📌 Start your journey today & build the future with code! 🚀