Intro to AI & ML
Get an overview of Artificial Intelligence and Machine Learning concepts and their real-world applications.
Python for Data Science
Learn the essentials of NumPy, Pandas, and Matplotlib for data manipulation and visualization.
Data Wrangling
Master techniques for cleaning and preparing datasets for analysis.
Exploratory Data Analysis
Visualize and summarize data to uncover meaningful insights.
Probability & Statistics
Understand distributions and hypothesis testing for data-driven decisions.
Math for ML
Explore linear algebra and calculus concepts essential for machine learning.
ML Fundamentals
Learn about training, testing, and avoiding overfitting in machine learning models.
Regression Models
Dive into linear regression and key evaluation metrics for predictive modeling.
Classification Models
Understand logistic regression, k-NN, and SVM for classification tasks.
Clustering & Unsupervised Learning
Explore K-means, PCA, and dimensionality reduction techniques.
Feature Engineering
Learn to create and select the best features for machine learning models.
Deep Learning Basics
Get started with neural networks, CNNs, and RNNs for advanced AI applications.
NLP Basics
Master text preprocessing and word embeddings for natural language processing.
Computer Vision Basics
Learn image classification and object detection techniques.
Deployment & Ethics
Understand MLOps, model monitoring, and ethical considerations in AI.