Real-World Machine Learning with Python

 

This course is designed to provide a comprehensive understanding of Machine Learning (ML) using Python. It covers Supervised and Unsupervised Learning, feature engineering, model evaluation, hyperparameter tuning, and real-world ML project implementation. Students will gain hands-on experience with Python libraries like NumPy, Pandas, Matplotlib, Scikit-Learn, and TensorFlow.

Course Modules & Syllabus
Module 1: Introduction to Machine Learning
What is Machine Learning?
Types of ML: Supervised, Unsupervised, Reinforcement Learning
AI vs ML vs Deep Learning vs Data Science
Applications of Machine Learning
Module 2: Python for Machine Learning
Python Basics: Data Types, Loops, Functions
NumPy: Arrays, Operations, Linear Algebra
Pandas: DataFrames, Data Manipulation
Matplotlib & Seaborn: Data Visualization
Module 3: Data Preprocessing & Feature Engineering
Handling Missing Data
Encoding Categorical Data
Feature Scaling (Normalization & Standardization)
Feature Selection & Feature Extraction
Module 4: Supervised Learning Algorithms
Regression Models:
Linear Regression, Multiple Regression
Polynomial Regression
Ridge & Lasso Regression
Classification Models:
Logistic Regression
Decision Trees & Random Forest
Support Vector Machines (SVM)
K-Nearest Neighbors (KNN)
Naïve Bayes Algorithm
Module 5: Model Evaluation & Optimization
Train-Test Split & Cross-Validation
Evaluation Metrics: Accuracy, Precision, Recall, F1 Score
Confusion Matrix & ROC-AUC Curve
Hyperparameter Tuning (Grid Search & Random Search)
Module 6: Unsupervised Learning Algorithms
Clustering Algorithms:
K-Means Clustering
Hierarchical Clustering
DBSCAN
Dimensionality Reduction Techniques:
Principal Component Analysis (PCA)
t-SNE
Module 7: Ensemble Learning & Advanced ML Techniques
Bagging & Boosting (XGBoost, AdaBoost, Gradient Boosting)
Stacking & Blending Models
Module 8: Machine Learning Model Deployment
Saving & Loading ML Models
Deploying ML Models using Flask & FastAPI
ML Model Deployment on Cloud (AWS, Azure, or Google Cloud)
Module 9: Real-World ML Projects
Predictive Analytics (Sales Forecasting, Stock Price Prediction)
Customer Segmentation using Clustering
Sentiment Analysis with NLP
Fraud Detection System
Image Classification using ML
Who Should Join?
Beginners who want to enter the field of Machine Learning
Software Engineers & Data Analysts looking to upskill
Students & Researchers in AI, ML, and Data Science
Course Highlights
✅ Hands-on Learning with Real Projects
✅ 100% Practical with Python Coding Exercises
✅ Expert Trainers with Industry Experience
✅ Certification upon Completion
✅ Placement Assistance & Career Guidance

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