Python for ML

About Course

Learn how Python is used for Machine Learning, from programming fundamentals and data handling to building and evaluating basic ML models. This beginner-friendly course provides practical experience with Python, NumPy, Pandas, Matplotlib, and Scikit-learn while introducing real-world Machine Learning workflows.

Understand the Python fundamentals required for Machine Learning
Learn how to work with data using NumPy and Pandas
Clean, transform, and prepare datasets for Machine Learning
Create visualizations using Matplotlib to understand data patterns
Build basic Machine Learning models using Scikit-learn
Understand model training, testing, and performance evaluation
Apply regression and classification techniques to practical datasets
Develop a complete beginner-level Machine Learning workflow using Python

Course Content

Module 1: Python Fundamentals for Machine Learning
Introduction to Python for Machine Learning Variables, Data Types, and Operators Lists, Tuples, Dictionaries, and Sets Conditional Statements and Loops Functions and Basic Error Handling Working with Python Libraries

Module 2: Data Analysis with NumPy and Pandas
Introduction to NumPy Arrays Numerical Operations with NumPy Introduction to Pandas Working with DataFrames and Series Loading and Exploring Datasets Data Cleaning and Preprocessing

Module 3: Data Visualization and Machine Learning with Scikit-learn
Introduction to Data Visualization Creating Charts with Matplotlib Understanding Data Patterns and Relationships Introduction to Scikit-learn Preparing Data for Machine Learning Splitting Data into Training and Testing Sets

Module 4: Building and Evaluating Machine Learning Models
Building Regression Models Building Classification Models Training and Testing Machine Learning Models Evaluating Model Performance Understanding Overfitting and Underfitting Creating a Complete Python-Based ML Workflow

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Python for ML

About Course

What Will You Learn?

Course Content

Module 1: Python Fundamentals for Machine Learning
Introduction to Python for Machine Learning Variables, Data Types, and Operators Lists, Tuples, Dictionaries, and Sets Conditional Statements and Loops Functions and Basic Error Handling Working with Python Libraries

Module 2: Data Analysis with NumPy and Pandas
Introduction to NumPy Arrays Numerical Operations with NumPy Introduction to Pandas Working with DataFrames and Series Loading and Exploring Datasets Data Cleaning and Preprocessing

Module 3: Data Visualization and Machine Learning with Scikit-learn
Introduction to Data Visualization Creating Charts with Matplotlib Understanding Data Patterns and Relationships Introduction to Scikit-learn Preparing Data for Machine Learning Splitting Data into Training and Testing Sets

Module 4: Building and Evaluating Machine Learning Models
Building Regression Models Building Classification Models Training and Testing Machine Learning Models Evaluating Model Performance Understanding Overfitting and Underfitting Creating a Complete Python-Based ML Workflow

Please Rotate Your Device

About Course

What Will You Learn?

Course Content

Module 1: Python Fundamentals for Machine Learning Introduction to Python for Machine Learning Variables, Data Types, and Operators Lists, Tuples, Dictionaries, and Sets Conditional Statements and Loops Functions and Basic Error Handling Working with Python Libraries

Module 2: Data Analysis with NumPy and Pandas Introduction to NumPy Arrays Numerical Operations with NumPy Introduction to Pandas Working with DataFrames and Series Loading and Exploring Datasets Data Cleaning and Preprocessing

Module 3: Data Visualization and Machine Learning with Scikit-learn Introduction to Data Visualization Creating Charts with Matplotlib Understanding Data Patterns and Relationships Introduction to Scikit-learn Preparing Data for Machine Learning Splitting Data into Training and Testing Sets

Module 4: Building and Evaluating Machine Learning Models Building Regression Models Building Classification Models Training and Testing Machine Learning Models Evaluating Model Performance Understanding Overfitting and Underfitting Creating a Complete Python-Based ML Workflow

Student Ratings & Reviews

Please Rotate Your Device

Module 1: Python Fundamentals for Machine Learning
Introduction to Python for Machine Learning Variables, Data Types, and Operators Lists, Tuples, Dictionaries, and Sets Conditional Statements and Loops Functions and Basic Error Handling Working with Python Libraries

Module 2: Data Analysis with NumPy and Pandas
Introduction to NumPy Arrays Numerical Operations with NumPy Introduction to Pandas Working with DataFrames and Series Loading and Exploring Datasets Data Cleaning and Preprocessing

Module 3: Data Visualization and Machine Learning with Scikit-learn
Introduction to Data Visualization Creating Charts with Matplotlib Understanding Data Patterns and Relationships Introduction to Scikit-learn Preparing Data for Machine Learning Splitting Data into Training and Testing Sets

Module 4: Building and Evaluating Machine Learning Models
Building Regression Models Building Classification Models Training and Testing Machine Learning Models Evaluating Model Performance Understanding Overfitting and Underfitting Creating a Complete Python-Based ML Workflow