Machine Learning

Master supervised and unsupervised learning algorithms with hands-on projects and real-world applications.

Course Details

Course Highlights

Comprehensive ML Training

From basic concepts to advanced algorithms with focus on practical applications.

Hands-on Projects

Build and deploy machine learning models on real-world datasets.

Industry Tools

Master scikit-learn, TensorFlow, and other essential ML libraries.

Capstone Project

Complete an end-to-end ML project to showcase your skills.

Learning Outcomes

By the end of this course, you will be able to:

  • Implement various ML algorithms from scratch
  • Preprocess and visualize data effectively
  • Train, evaluate and optimize ML models
  • Deploy models in production environments
  • Work with both structured and unstructured data
  • Understand model interpretability techniques

Course Curriculum

Module 1: ML Foundations
  • Introduction to Machine Learning
  • Python for ML (NumPy, Pandas, Matplotlib)
  • Data Preprocessing Techniques
  • Exploratory Data Analysis
  • Feature Engineering
Module 2: Supervised Learning
  • Linear & Logistic Regression
  • Decision Trees & Random Forests
  • SVM & Kernel Methods
  • Model Evaluation Metrics
  • Hyperparameter Tuning
Module 3: Unsupervised Learning
  • Clustering Algorithms (K-Means, DBSCAN)
  • Dimensionality Reduction (PCA, t-SNE)
  • Anomaly Detection
  • Association Rule Learning
Module 4: Advanced Topics
  • Ensemble Methods
  • Neural Networks Basics
  • Model Deployment
  • ML in Production
Module 5: Capstone Project
  • Problem Definition
  • Data Collection & Cleaning
  • Model Building & Evaluation
  • Final Deployment

Who Should Attend?

Python Developers

Programmers who want to transition into machine learning roles.

Data Analysts

Analysts looking to upgrade their skills to predictive modeling.

Tech Graduates

Recent graduates wanting to start a career in AI/ML.

AI Enthusiasts

Professionals interested in applying ML to their domain.