Master Machine Learning algorithms and build intelligent models to solve real-world problems.
Types of ML, Applications, ML Workflow
NumPy, Pandas, Matplotlib, Seaborn, Scikit-learn Basics
Data Cleaning, Handling Missing Values, Encoding, Scaling
Univariate, Bivariate, Multivariate Analysis, Visualization
Feature Selection, Extraction, Transformation, Dimensionality Reduction
Linear Regression, Logistic Regression
Decision Trees, Random Forest, SVM, KNN, Naive Bayes
K-Means Clustering, Hierarchical Clustering, DBSCAN, PCA
Train/Test Split, Cross Validation, Confusion Matrix, Metrics
Grid Search, Random Search, Bias-Variance Tradeoff
Gradient Boosting, XGBoost, Ensemble Methods
Model Deployment, Streamlit, Flask, Real-world Projects
Predict house prices using regression algorithms.
Predict customer churn using classification models.
Detect fraudulent transactions using ML models.
Build a movie recommendation system using ML & similarity.
Forecast sales using time series & regression.
* Salaries are indicative. Source: Glassdoor, AmbitionBox, Naukri (2024)
One of the most in-demand skills in the world.
Solve real problems and create intelligent solutions.
Great opportunities in AI, ML & Data Science.
Applicable in multiple industries & domains.
High paying roles with excellent benefits.
Stay ahead with cutting-edge technology and innovation.
Join thousands of students who transformed their careers with our training programs.