Machine Learning with Python
Master the entire Machine Learning workflow using Python, from data preprocessing to building and deploying predictive models in real-world scenarios.
Modules
- What is ML? Types of ML
- Supervised vs Unsupervised Learning
- ML Workflow and Life Cycle
- Python Ecosystem for ML
- Pandas, NumPy, Matplotlib Recap
- Data Cleaning & Feature Engineering
- Exploratory Data Analysis (EDA)
- Data Scaling and Normalization
- Linear & Logistic Regression
- K-Nearest Neighbors (KNN)
- Naive Bayes
- Decision Trees & Random Forest
- Confusion Matrix, Accuracy, Precision, Recall
- ROC-AUC, F1-Score
- Cross Validation
- Hyperparameter Tuning: GridSearch & RandomizedSearch
- K-Means Clustering
- DBSCAN & Hierarchical Clustering
- Principal Component Analysis (PCA)
- Feature Reduction Techniques
- Gradient Boosting: XGBoost, LightGBM
- Support Vector Machines (SVM)
- Ensemble Techniques
- Model Stacking & Blending
- House Price Prediction
- Customer Churn Prediction
- Credit Card Fraud Detection
- Retail Sales Forecasting
- Pickle & Joblib for Model Saving
- Deploying ML Models with Streamlit & Flask
- Creating REST APIs for Models
- Intro to MLOps and Cloud Deployment
Industry Insights
92%
Industry Relevance
High
Market Demand
11 LPA+
Avg. Salary
Ready to start learning?
Your Learning Roadmap
Follow this path to mastery. Our AI guide leads the way.
ML Foundations
8 hrsIntro to ML types, workflow, and Python ecosystem
Python & Data Prep
12 hrsEDA, cleaning, feature engineering, scaling
Supervised Learning
14 hrsRegression, KNN, Naive Bayes, trees
Model Evaluation & Tuning
10 hrsMetrics, validation, hyperparameter tuning
Unsupervised Learning
12 hrsClustering, PCA, feature reduction
Advanced Algorithms
14 hrsXGBoost, SVM, ensembles, stacking
ML Projects
10 hrsEnd-to-end case studies with real datasets
Model Deployment
10 hrsFlask/Streamlit apps, APIs, cloud deploy
Why take Machine Learning with Python?
- Machine Learning is the backbone of AI, used in finance, health, marketing, and tech.
- Python is the #1 language for building ML models due to libraries like Scikit-learn, Pandas, and TensorFlow.
- Learn to solve real-world problems: customer churn, fraud detection, demand forecasting, and more.
- Prepares you for top ML roles, internships, and research positions with hands-on project work.
- End-to-end journey: from data cleaning to model deployment, all in Python.