Learn machine learning algorithms, data preprocessing, model building, evaluation, and predictive analytics to solve real-world business problems.
- Introduction to Machine Learning and AI
- Understanding Data and Problem Types
- Python for Machine Learning
- Data Collection and Data Preprocessing
- Data Cleaning and Feature Engineering
- Exploratory Data Analysis (EDA)
- Supervised Learning Concepts
- Linear Regression
- Logistic Regression
- Decision Trees and Random Forests
- Support Vector Machines (SVM)
- K-Nearest Neighbors (KNN)
- Unsupervised Learning Concepts
- Clustering Algorithms (K-Means, Hierarchical Clustering)
- Dimensionality Reduction Techniques
- Model Training and Validation
- Performance Evaluation Metrics
- Hyperparameter Tuning
- Introduction to Deep Learning
- Neural Networks Fundamentals
- Working with Scikit-learn, NumPy, and Pandas
- Model Deployment Basics
- Real-world Machine Learning Use Cases
- Mini Projects and Case Studies
- Final Capstone Project and Best Practices
Talk to our team about your goals, schedule and the right batch for you.
