About This Course
This Machine Learning course takes you from fundamentals to real-world deployment. You'll explore core ML types—Supervised, Unsupervised, and Reinforcement Learning—and build end-to-end ML pipelines using Python, Jupyter, and Scikit-learn. Gain hands-on skills in data preprocessing, feature scaling, dimensionality reduction (PCA, t-SNE), and implement key algorithms like Regression, Decision Trees, Random Forest, SVM, and Clustering.
Advance into Autoencoders, Time Series (ARIMA, SARIMA), and hyperparameter tuning (GridSearchCV, Bayesian Optimization). Finally, learn to deploy models with Flask, FastAPI, Django, MLflow, CI/CD, and AutoML tools.
Course Benefits
- 35+ hrs of Training
- Industry-based assessments
- Outcome-based learning
- Hands-on projects
- Lifetime LMS Access
- Industry certification
Course Curriculum
1. Introduction to Machine Learning
- What is ML: Machines learning from data to predict and decide
- Types of ML: Supervised, Unsupervised & Reinforcement Learning
- Applications: Recommendations, fraud detection, automation, and more
- Impact: Driving smarter solutions across industries
2. ML Workflow & Tools
- ML Pipeline: Learn the workflow from data to deployment
- Setup: Work with Python, Jupyter, Colab & Scikit-learn
- Hands-On: Build, train & evaluate real ML models
- Practical Learning: Apply concepts in coding environments
3. Data Preprocessing
- Data Cleaning: Handle missing or incomplete data
- Categorical Encoding: Convert text into numerical format
- Data Preparation: Make datasets ready for ML models
- Quality Input: Ensure clean, reliable data for better results
4. Feature Engineering
- Feature Scaling: Normalize and standardize data
- Outlier Detection: Spot unusual data points
- Outlier Treatment: Minimize their impact on models
- Better Accuracy: Boost model performance with clean data
5. Feature Selection & Dimensionality Reduction
- Feature Selection: Identify key features for modeling
- Dimensionality Reduction: Simplify data with PCA & t-SNE
- Noise Reduction: Remove irrelevant inputs
- Boost Accuracy: Improve model efficiency and performance
6. Linear Regression
- Linear Regression: Apply simple & multiple regression
- Polynomial Regression: Capture non-linear patterns
- Predictive Modeling: Build models for accurate forecasts
- Data Insights: Understand relationships between variables
7. Regularized Regression
- Ridge & Lasso: Apply regularization to avoid overfitting
- Model Evaluation: Measure with MSE, RMSE & R²
- Better Accuracy: Improve generalization and predictions
- Robust Models: Build reliable regression models
8. Advanced Classification
- Ensemble Learning: Enhance accuracy with Random Forest & more
- SVM Models: Classify complex data effectively
- Advanced Algorithms: Apply for classification & regression
- High Performance: Build stronger, reliable models
9. Clustering
- K-Means: Cluster data based on similarity
- Hierarchical Clustering: Create nested groupings for deeper insights
- Unsupervised Learning: Find hidden patterns in unlabeled data
- Data Insights: Reveal structure without predefined labels
10. Anomaly Detection
- Isolation Forest & One-Class SVM: Detect outliers in datasets
- Autoencoders: Use neural networks for anomaly detection
- Fraud & Defect Detection: Spot unusual patterns and risks
- Enhanced Security: Strengthen systems with advanced anomaly detection
11. Hyperparameter Tuning
- GridSearchCV & RandomizedSearchCV: Automate hyperparameter tuning
- Bayesian Optimization: Apply smarter parameter search methods
- Performance Boost: Optimize models for accuracy and reliability
12. Time Series Forecasting
- Time Series Basics: Learn components and patterns in sequential data
- ARIMA Modeling: Build forecasting models for time-dependent data
- SARIMA Modeling: Handle seasonality for more accurate predictions
13. Deep Learning Basics
- Neural Network Basics: Understand the foundation of deep learning
- Network Architecture: Explore layers and connections in neural networks
- Activation Functions & Optimizers: Learn how models learn and improve accuracy
14. Model Deployment & MLOps
- Model Deployment: Deploy ML models using Flask, FastAPI, and Django
- MLOps Pipelines: Manage workflows with MLflow
- CI/CD for ML: Automate and streamline machine learning projects
Requirements
- Basic Python programming knowledge
- Understanding of mathematics & statistics
- Computer with internet connection
- Determination to learn and build ML applications
Material Includes
- 35+ Hours of Video Lectures by MNC Certified Trainer
- Lifetime LMS Access
- Section Quizzes & Assessments
- Industry-Based Hands-on Projects
- Certificate of Completion
Why Choose This Course
Expert Trainers
Learn from experienced MNC-certified professionals with real-world ML expertise
Hands-On Projects
Build real ML applications and strengthen your portfolio with practical projects
Industry Certification
Receive recognized certification to boost your career in machine learning
Complete ML Pipeline
Master the entire workflow from data preprocessing to model deployment
Self-Paced Learning
Learn at your own pace with lifetime access to course materials and updates
MLOps & Deployment
Learn to deploy models with Flask, FastAPI, MLflow, and CI/CD pipelines
Ready to Master Machine Learning?
Join 1,200+ students already learning ML. Start your journey today and build intelligent systems from data to deployment.