Machine Learning

Machine Learning is one of the most in-demand fields in Artificial Intelligence. This course provides strong foundations in ML concepts, algorithms, and practical implementation using Python. Learners will gain hands-on experience in building predictive models, analyzing data, and creating AI-driven applications.

Duration: Flexible

Fees: Contact us for details

Syllabus Highlights

Introduction to Artificial Intelligence & Machine Learning
Python for Machine Learning (NumPy, Pandas, Matplotlib, Scikit-Learn)
Data Preprocessing & Feature Engineering
Supervised Learning:

Linear & Logistic Regression
Decision Trees & Random Forests
Naïve Bayes
Support Vector Machines (SVM)

Unsupervised Learning:

K-Means & Hierarchical Clustering
Principal Component Analysis (PCA)

Ensemble Methods (Bagging, Boosting, XGBoost)
Neural Networks & Deep Learning Basics
Model Evaluation & Hyperparameter Tuning
Natural Language Processing (NLP) Basics
Real-World Applications of ML (Finance, Healthcare, E-commerce, etc.)

Projects

House Price Prediction Model
Sentiment Analysis of Tweets/Reviews
Customer Segmentation using Clustering
Handwritten Digit Recognition (MNIST Dataset)
Recommendation System (Big Project)

Outcomes

Strong foundation in ML algorithms & techniques
Hands-on experience with Python & ML libraries
Ability to build predictive models & analyze real-world data
Readiness for advanced fields like Deep Learning, AI, and Data Science