Machine Learning Masterclass with Python – Hands-On Course

About Course

This hands-on course is designed for students and professionals who want to build a solid foundation in machine learning using Python. You’ll work on real datasets, build supervised and unsupervised models, and learn to apply machine learning in practical scenarios like classification, prediction, and clustering.

No prior AI experience is required. This course takes you from Python basics to advanced model evaluation techniques step by step.

Understand core concepts of machine learning and its real-world applications
Differentiate between supervised, unsupervised, and reinforcement learning
Use Python libraries like Pandas, NumPy, Matplotlib, and Scikit-learn
Clean, preprocess, and prepare datasets for model building
Build predictive models using Linear Regression, Logistic Regression, Decision Trees, and SVM
Perform clustering with K-Means and Hierarchical algorithms
Apply dimensionality reduction using PCA
Evaluate model performance with accuracy, precision, recall, and F1-score
Visualize data insights and confusion matrices
Complete an end-to-end ML project using real datasets
Present and interpret machine learning results confidently
Get ready for real-world data science roles and interviews

Course Content

Module 1: Introduction to Machine Learning
What is Machine Learning? Types of ML: Supervised, Unsupervised, Reinforcement Applications in the real world ML workflow & pipeline overview

Module 2: Python Essentials for ML
Python fundamentals for data analysis Jupyter Notebook setup Numpy, Pandas, and Matplotlib basics

Module 3: Data Preprocessing
Data cleaning and missing value handling Encoding categorical variables Feature scaling techniques Splitting data into train-test sets

Module 4: Supervised Learning Algorithms
Linear Regression and Multiple Regression Logistic Regression for binary classification Decision Trees and Random Forests Support Vector Machines (SVM)

Module 5: Unsupervised Learning Algorithms
Clustering with K-Means Hierarchical Clustering Principal Component Analysis (PCA)

Module 6: Model Evaluation & Deployment
Accuracy, Precision, Recall, F1-score Confusion Matrix, ROC Curve Introduction to model deployment with Streamlit

Machine Learning Masterclass with Python – Hands-On Course

About Course

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What Will You Learn?

Course Content

Module 1: Introduction to Machine Learning
What is Machine Learning? Types of ML: Supervised, Unsupervised, Reinforcement Applications in the real world ML workflow & pipeline overview

Module 2: Python Essentials for ML
Python fundamentals for data analysis Jupyter Notebook setup Numpy, Pandas, and Matplotlib basics

Module 3: Data Preprocessing
Data cleaning and missing value handling Encoding categorical variables Feature scaling techniques Splitting data into train-test sets

Module 4: Supervised Learning Algorithms
Linear Regression and Multiple Regression Logistic Regression for binary classification Decision Trees and Random Forests Support Vector Machines (SVM)

Module 5: Unsupervised Learning Algorithms
Clustering with K-Means Hierarchical Clustering Principal Component Analysis (PCA)

Module 6: Model Evaluation & Deployment
Accuracy, Precision, Recall, F1-score Confusion Matrix, ROC Curve Introduction to model deployment with Streamlit

Module 7: Final Project
End-to-end project: Build a model, evaluate, and interpret results Project presentation and feedback

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About Course

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What Will You Learn?

Course Content

Module 1: Introduction to Machine Learning What is Machine Learning? Types of ML: Supervised, Unsupervised, Reinforcement Applications in the real world ML workflow & pipeline overview

Module 2: Python Essentials for ML Python fundamentals for data analysis Jupyter Notebook setup Numpy, Pandas, and Matplotlib basics

Module 3: Data Preprocessing Data cleaning and missing value handling Encoding categorical variables Feature scaling techniques Splitting data into train-test sets

Module 4: Supervised Learning Algorithms Linear Regression and Multiple Regression Logistic Regression for binary classification Decision Trees and Random Forests Support Vector Machines (SVM)

Module 5: Unsupervised Learning Algorithms Clustering with K-Means Hierarchical Clustering Principal Component Analysis (PCA)

Module 6: Model Evaluation & Deployment Accuracy, Precision, Recall, F1-score Confusion Matrix, ROC Curve Introduction to model deployment with Streamlit

Module 7: Final Project End-to-end project: Build a model, evaluate, and interpret results Project presentation and feedback

Trending Courses

Trending College Workshops

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Categories

Module 1: Introduction to Machine Learning
What is Machine Learning? Types of ML: Supervised, Unsupervised, Reinforcement Applications in the real world ML workflow & pipeline overview

Module 2: Python Essentials for ML
Python fundamentals for data analysis Jupyter Notebook setup Numpy, Pandas, and Matplotlib basics

Module 3: Data Preprocessing
Data cleaning and missing value handling Encoding categorical variables Feature scaling techniques Splitting data into train-test sets

Module 4: Supervised Learning Algorithms
Linear Regression and Multiple Regression Logistic Regression for binary classification Decision Trees and Random Forests Support Vector Machines (SVM)

Module 5: Unsupervised Learning Algorithms
Clustering with K-Means Hierarchical Clustering Principal Component Analysis (PCA)

Module 6: Model Evaluation & Deployment
Accuracy, Precision, Recall, F1-score Confusion Matrix, ROC Curve Introduction to model deployment with Streamlit

Module 7: Final Project
End-to-end project: Build a model, evaluate, and interpret results Project presentation and feedback