Machine Learning with Python

About Course

Step into the world of Artificial Intelligence with our Machine Learning with Python course – a beginner-friendly, hands-on training designed to turn your coding skills into powerful predictive tools. This course takes you from the basics of Python to building real-world machine learning models using libraries like Scikit-learn, Pandas, NumPy, and Matplotlib.

By the end of this course, you will:
Understand the fundamentals of Machine Learning and the difference between supervised and unsupervised learning.
Master Python libraries like NumPy, Pandas, Matplotlib, and Scikit-learn for building ML models.
Preprocess data to handle missing values, scale features, and prepare datasets for modeling.
Build predictive models using algorithms such as Linear Regression, Logistic Regression, Decision Trees, Random Forest, and Support Vector Machines.
Evaluate your models using metrics like accuracy, precision, recall, and confusion matrix.
Implement unsupervised learning techniques such as K-Means clustering and Principal Component Analysis (PCA).
Optimize model performance with hyperparameter tuning and cross-validation.
Develop real-world projects such as predicting house prices, classifying emails, and customer segmentation.
Deploy ML models for practical use in applications using Flask/FastAPI and Streamlit.
By completing hands-on projects, you will gain the confidence and skills to build machine learning models that solve real-world problems.

Course Content

Module 1: Introduction to Machine Learning
What is Machine Learning? Types of Machine Learning: Supervised, Unsupervised, Reinforcement Learning Key Concepts: Training, Testing, Overfitting, Underfitting Overview of Python for ML Introduction to Python Libraries: NumPy, Pandas, Matplotlib Setting up Jupyter Notebook / Google Colab for hands-on learning

Module 2: Python Basics for Machine Learning
Data Structures in Python Lists, Dictionaries, Tuples, Sets NumPy and Pandas Data Handling and Manipulation Working with Arrays, DataFrames, and Series Data Visualization Matplotlib & Seaborn for Plotting

Module 3: Data Preprocessing & Exploration
Data Cleaning Handling Missing Data, Duplicates, and Outliers Data Transformation Feature Scaling (Normalization, Standardization) Encoding Categorical Variables (One-Hot, Label Encoding) Exploratory Data Analysis (EDA) Visualizing Data Distributions, Correlations

Module 4: Supervised Learning – Regression
Linear Regression Introduction to Regression Problems Training a Linear Model and Model Evaluation Logistic Regression Classification with Logistic Regression Evaluating Classification Performance (Accuracy, Precision, Recall)

Module 5: Supervised Learning – Classification
Decision Trees Building Decision Tree Classifiers Overfitting and Pruning Random Forest Ensemble Learning and Boosting Techniques Support Vector Machines (SVM) Linear and Non-Linear SVM

Module 6: Unsupervised Learning
Clustering K-Means Clustering Hierarchical Clustering Dimensionality Reduction Principal Component Analysis (PCA) Feature Selection Methods

Module 7: Model Evaluation & Hyperparameter Tuning
Evaluating Model Performance Train-Test Split, Cross-Validation Metrics for Classification: Confusion Matrix, Precision, Recall, F1 Score Metrics for Regression: MSE, RMSE, R² Hyperparameter Tuning GridSearchCV and RandomizedSearchCV for Model Optimization

Module 8: Real-World Projects & Case Studies
Project 1: Predicting Housing Prices (Regression) Using a dataset to predict house prices based on various features Project 2: Classifying Emails (Classification) Build a spam email classifier using machine learning algorithms Project 3: Customer Segmentation (Clustering) Segmenting customers using unsupervised learning techniques Project 4: Sentiment Analysis (NLP) Building a sentiment analysis model on movie reviews or Twitter data

Module 9: Advanced Topics (Optional)
Introduction to Deep Learning (TensorFlow/Keras) Building a simple neural network for classification Natural Language Processing (NLP) Basics Text Preprocessing, Tokenization, Word Embeddings

Module 10: Deploying ML Models
Introduction to Model Deployment Deploying Models with Flask or FastAPI Creating Web Applications with Streamlit for Interactive ML Apps

Module 11: Capstone Project
End-to-End Project A real-world project combining data preprocessing, model building, and deployment. You’ll apply everything you’ve learned and showcase your skills.

Bonus: Certification & Career Guidance
Project Review & Feedback Detailed feedback on your projects to strengthen your portfolio Resume Building & Interview Preparation Tips for showcasing your skills to potential employers

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Course Content

Module 2: Python Basics for Machine Learning
Data Structures in Python Lists, Dictionaries, Tuples, Sets NumPy and Pandas Data Handling and Manipulation Working with Arrays, DataFrames, and Series Data Visualization Matplotlib & Seaborn for Plotting

Module 4: Supervised Learning – Regression
Linear Regression Introduction to Regression Problems Training a Linear Model and Model Evaluation Logistic Regression Classification with Logistic Regression Evaluating Classification Performance (Accuracy, Precision, Recall)

Module 5: Supervised Learning – Classification
Decision Trees Building Decision Tree Classifiers Overfitting and Pruning Random Forest Ensemble Learning and Boosting Techniques Support Vector Machines (SVM) Linear and Non-Linear SVM

Module 6: Unsupervised Learning
Clustering K-Means Clustering Hierarchical Clustering Dimensionality Reduction Principal Component Analysis (PCA) Feature Selection Methods

Module 9: Advanced Topics (Optional)
Introduction to Deep Learning (TensorFlow/Keras) Building a simple neural network for classification Natural Language Processing (NLP) Basics Text Preprocessing, Tokenization, Word Embeddings

Module 10: Deploying ML Models
Introduction to Model Deployment Deploying Models with Flask or FastAPI Creating Web Applications with Streamlit for Interactive ML Apps

Module 11: Capstone Project
End-to-End Project A real-world project combining data preprocessing, model building, and deployment. You’ll apply everything you’ve learned and showcase your skills.

Bonus: Certification & Career Guidance
Project Review & Feedback Detailed feedback on your projects to strengthen your portfolio Resume Building & Interview Preparation Tips for showcasing your skills to potential employers

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About Course

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Course Content

Module 2: Python Basics for Machine Learning Data Structures in Python Lists, Dictionaries, Tuples, Sets NumPy and Pandas Data Handling and Manipulation Working with Arrays, DataFrames, and Series Data Visualization Matplotlib & Seaborn for Plotting

Module 4: Supervised Learning – Regression Linear Regression Introduction to Regression Problems Training a Linear Model and Model Evaluation Logistic Regression Classification with Logistic Regression Evaluating Classification Performance (Accuracy, Precision, Recall)

Module 5: Supervised Learning – Classification Decision Trees Building Decision Tree Classifiers Overfitting and Pruning Random Forest Ensemble Learning and Boosting Techniques Support Vector Machines (SVM) Linear and Non-Linear SVM

Module 6: Unsupervised Learning Clustering K-Means Clustering Hierarchical Clustering Dimensionality Reduction Principal Component Analysis (PCA) Feature Selection Methods

Module 9: Advanced Topics (Optional) Introduction to Deep Learning (TensorFlow/Keras) Building a simple neural network for classification Natural Language Processing (NLP) Basics Text Preprocessing, Tokenization, Word Embeddings

Module 10: Deploying ML Models Introduction to Model Deployment Deploying Models with Flask or FastAPI Creating Web Applications with Streamlit for Interactive ML Apps

Module 11: Capstone Project End-to-End Project A real-world project combining data preprocessing, model building, and deployment. You’ll apply everything you’ve learned and showcase your skills.

Bonus: Certification & Career Guidance Project Review & Feedback Detailed feedback on your projects to strengthen your portfolio Resume Building & Interview Preparation Tips for showcasing your skills to potential employers

Student Ratings & Reviews

Trending Courses

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Categories

Module 2: Python Basics for Machine Learning
Data Structures in Python Lists, Dictionaries, Tuples, Sets NumPy and Pandas Data Handling and Manipulation Working with Arrays, DataFrames, and Series Data Visualization Matplotlib & Seaborn for Plotting

Module 4: Supervised Learning – Regression
Linear Regression Introduction to Regression Problems Training a Linear Model and Model Evaluation Logistic Regression Classification with Logistic Regression Evaluating Classification Performance (Accuracy, Precision, Recall)

Module 5: Supervised Learning – Classification
Decision Trees Building Decision Tree Classifiers Overfitting and Pruning Random Forest Ensemble Learning and Boosting Techniques Support Vector Machines (SVM) Linear and Non-Linear SVM

Module 6: Unsupervised Learning
Clustering K-Means Clustering Hierarchical Clustering Dimensionality Reduction Principal Component Analysis (PCA) Feature Selection Methods

Module 9: Advanced Topics (Optional)
Introduction to Deep Learning (TensorFlow/Keras) Building a simple neural network for classification Natural Language Processing (NLP) Basics Text Preprocessing, Tokenization, Word Embeddings

Module 10: Deploying ML Models
Introduction to Model Deployment Deploying Models with Flask or FastAPI Creating Web Applications with Streamlit for Interactive ML Apps

Module 11: Capstone Project
End-to-End Project A real-world project combining data preprocessing, model building, and deployment. You’ll apply everything you’ve learned and showcase your skills.

Bonus: Certification & Career Guidance
Project Review & Feedback Detailed feedback on your projects to strengthen your portfolio Resume Building & Interview Preparation Tips for showcasing your skills to potential employers