MACHINE LEARNING COURSE

Machine Learning With Python

Beginner to Advanced — Build, Evaluate & Deploy Models

Welcome to Machine Learning by Codes With Pankaj, a practical online course designed for students, job seekers, and working professionals. Learn through clear theory, hands-on Python coding, real datasets, model evaluation, and deployment projects.

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Beginner to advanced Python implementation Real-world projects
Machine Learning course with Python by Codes With Pankaj
Machine Learning

From Data to Deployment

Learn algorithms, evaluate models, and build practical ML applications.

58+ hoursHands-on learning
58+ HoursCourse content
13 ModulesStructured learning
130+ LessonsGuided topics
100% OnlineLearn online
COURSE OVERVIEW

Learn ML Through Practice

Move step by step from Machine Learning fundamentals to model building, evaluation, and deployment.

Core Concepts

Understand AI, ML, learning types, workflows, and common real-world use cases in simple language.

Hands-on Python

Implement popular Machine Learning algorithms using Python and real datasets.

Evaluation & Tuning

Assess model performance, compare results, and explore tuning and optimization.

Deployment Projects

Explore model deployment and build interactive machine learning experiences with Streamlit.

Course highlights: Practical coding with real datasets, assignments and mini-projects, model deployment, and Streamlit projects. The course is designed for online learning.

TARGET STUDENTS

Who Is This Course For?

A practical learning path for learners who want to understand and implement Machine Learning.

  • Students and job seekers interested in Machine Learning
  • Developers who want to add ML skills to their toolkit
  • Data analysts moving toward predictive modeling and AI
  • Learners who prefer hands-on implementation over theory alone
  • Professionals interested in practical ML projects
GET READY TO LEARN

Prerequisites & Foundations

These foundations can help you get more from the course.

Basic Python

Comfort with Python programming fundamentals is recommended.

Basic Mathematics

Familiarity with basic algebra and statistics is helpful.

Working with Data

Basic experience with lists, arrays, and DataFrames is useful.

NumPy, Pandas, and data analysis knowledge can be helpful, but are not mandatory. No prior Machine Learning experience is required.

58+ HOURS · 13 MODULES · 130+ LESSONS

Machine Learning Course Content

Search the curriculum and open any module to explore its topics.

13 topic groups
01Module 1: Introduction to Machine LearningAI, ML, deep learning, and the machine learning workflow
  • What is AI, Machine Learning, and Deep Learning?
  • Types of Machine Learning: Supervised, Unsupervised, and Reinforcement Learning
  • Machine Learning workflow: Data → Model → Evaluation → Deployment
  • Parametric vs Non-Parametric Algorithms
  • Real-life Machine Learning use cases
  • Assignment 1: Identify ML applications around you
02Module 2: Exploratory Data Analysis (EDA)Understand, inspect, and prepare datasets
  • Explore datasets before model building
  • Use Python to inspect and understand data
  • Identify patterns, trends, and data quality issues
  • Prepare data for the modeling workflow
03Module 3: Regression ModelsPredict continuous numerical values
  • Understand regression problems and use cases
  • Build regression models with Python
  • Train models on real datasets
  • Interpret predictions and model performance
04Module 4: Tree-based ModelsLearn decision trees and tree-based prediction
  • Understand decision-tree based learning
  • Build tree-based models using Python
  • Explore how models make prediction decisions
  • Practice with real datasets
05Module 5: Ensemble TechniquesCombine models to improve predictions
  • Understand ensemble learning
  • Explore bagging and boosting concepts
  • Learn Random Forest and boosting-based approaches
  • Compare model results on practical datasets
06Module 6: Nearest Neighbours (KNN)Make predictions using nearby data points
  • Understand the K-Nearest Neighbours algorithm
  • Explore distance and neighbour selection
  • Apply KNN to classification and prediction tasks
  • Practice model fitting and prediction in Python
07Module 7: Support Vector Machines (SVM)Understand margins, boundaries, and kernels
  • Understand support vectors and decision boundaries
  • Explore margins and kernel concepts
  • Apply SVM to classification problems
  • Practice with Python and real datasets
08Module 8: Naive BayesApply probability-based classification
  • Understand the intuition behind Naive Bayes
  • Explore probabilistic classification
  • Train and test a Naive Bayes model
  • Apply the algorithm to a practical dataset
09Module 9: Dimensionality ReductionReduce features while retaining useful information
  • Understand why dimensionality reduction is useful
  • Explore feature reduction concepts
  • Learn the role of PCA
  • Use reduced feature sets in a modeling workflow
10Module 10: ClusteringDiscover groups and patterns in unlabeled data
  • Understand unsupervised learning and clustering
  • Explore K-Means clustering
  • Interpret clusters and group structure
  • Practice clustering with Python datasets
11Module 11: Model Evaluation & TuningMeasure model performance and improve results
  • Understand why model evaluation matters
  • Evaluate models using suitable metrics
  • Compare model performance
  • Explore model tuning and optimization
12Module 12: Model DeploymentMove a trained model toward practical use
  • Understand the model deployment workflow
  • Prepare a trained model for use
  • Connect model predictions to an application
  • Review practical deployment considerations
13Module 13: Model StreamlitBuild interactive interfaces for machine learning models
  • Understand how Streamlit can present a model
  • Create a simple interactive ML application
  • Accept user input and display predictions
  • Present an end-to-end model demo

The module descriptions provide a high-level guide to the course structure. Detailed lesson availability may vary within each module.

LEARN WITH ME

Meet Your Instructor, Pankaj Chouhan

I’m a software engineer and educator with 10+ years of experience, passionate about helping learners understand coding and problem-solving. My approach connects theory with practical examples and real-world application.

Our mission: learn, apply, and innovate through structured, engaging, and career-focused learning.

About Pankaj GitHub
START YOUR MACHINE LEARNING JOURNEY

Turn Your Interest in ML into Practical Skills

Explore Machine Learning algorithms, work with Python, and learn the path from model building to deployment. Book a free demo to discuss your goals and the course format.

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