This course offers a practical and theoretical foundation in Artificial Intelligence and Machine Learning, teaching students how to build intelligent systems using real-world data. It covers algorithms, data processing, model training, and deployment, preparing learners for emerging tech-driven careers. 🛠️ Tools & Technologies Used Python – Primary programming language for AI/ML Jupyter Notebook – Interactive coding environment TensorFlow – Deep learning framework by Google Keras – Simplified neural network library PyTorch – Flexible ML library by Facebook Scikit-learn – Classical machine learning algorithms Pandas & NumPy – Data manipulation and analysis Matplotlib & Seaborn – Data visualization Google Colab – Cloud-based coding and model training OpenCV – Image processing and computer vision 🎯 Career Opportunities Machine Learning Engineer AI Developer Data Scientist Computer Vision Engineer NLP Engineer (Natural Language Processing) Business Intelligence Analy

Master the science of extracting meaningful insights from data. This course covers data analysis, statistics, machine learning, data visualization, and real-world problem-solving using modern tools and techniques. Perfect for those aiming to build a career in data-driven fields. 🛠️ Tools Used in Data Science Python – Core programming for data manipulation and modeling R – Statistical computing and visualization SQL – Data querying and database management Jupyter Notebook – Interactive coding and data exploration Pandas & NumPy – Data manipulation libraries Matplotlib & Seaborn – Data visualization Scikit-learn – Machine learning models Tableau / Power BI – Business data visualization tools TensorFlow / PyTorch – Deep learning frameworks 🎯 Career Opportunities in Data Science Data Scientist Data Analyst Machine Learning Engineer AI Specialist Business Intelligence Analyst Data Engineer Research Scientist Quantitative Analyst Big Data Analyst