Machine Learning & Deep Learning with Python: with Projects

Master Machine Learning and Deep Learning from scratch and build real-world Artificial Intelligence applications using Python.       This comprehensive course is designed for students, developers, data scientists, and AI enthusiasts who want to learn Machine Learning and Deep Learning through a practical, hands-on approach. Whether you are a complete beginner or have some programming experience, this course will guide you step-by-step from fundamental concepts to advanced neural network architectures.Throughout the course, you will learn how Machine Learning algorithms work, how to prepare and analyze data, how to build predictive models, and how to create powerful Deep Learning systems using modern Python libraries.What You'll Learn      Understand the fundamentals of Artificial Intelligence, Machine Learning, and Deep Learning       ● Master data preprocessing, feature engineering, and model evaluation techniques       ● Build Machine Learning models for classification, regression, clustering, and dimensionality reduction       ● Work with popular algorithms such as Linear Regression, Logistic Regression, Decision Trees, Random Forests, K-Nearest          Neighbors, Naive Bayes, Support Vector Machines, and Ensemble Methods       ● Learn the mathematics and intuition behind Machine Learning models       ● Understand neural networks, activation functions, loss functions, backpropagation, and gradient descent        ● Build Deep Learning models using PyTorch         ● Develop Convolutional Neural Networks (CNNs) for image classification tasks          ● Learn techniques to prevent overfitting and improve model performance           ● Train, evaluate, and deploy AI models on real-world datasets ● Complete hands-on projects to strengthen your practical skillsCourse Content      Machine Learning           ● Introduction to AI and Machine Learning             ● Data Analysis and Visualization             ● Data Cleaning and Preprocessing             ● Supervised Learning             ● Unsupervised Learning              ● Model Evaluation and Performance Metrics              ● Feature Engineering              ● Ensemble LearningDeep Learning           ● Neural Network Fundamentals            ● Forward and Backward Propagation           ● Activation Functions            ● Loss Functions           ● Optimizers and Gradient Descent            ● PyTorch Fundamentals            ● Building Neural Networks with PyTorch            ● Convolutional Neural Networks (CNNs)            ● Image Classification Projects Hands-On Projects           ● House Price Prediction            ● Customer Churn Prediction           ● Spam Detection           ● Image Classification           ● Multiple Real-World AI Applications Requirements           ● Basic knowledge of Python is recommended            ● No prior Machine Learning or Deep Learning experience is required           ● A computer with internet access            ● Passion to learn Artificial Intelligence Who This Course Is For            ● Students interested in Artificial Intelligence and Data Science           ● Python developers looking to enter AI and Machine Learning            ● Beginners who want a structured learning path            ● Software engineers seeking practical Machine Learning skills            ● Anyone who wants to build real-world AI projectsBy the end of this course, you will have a strong foundation in Machine Learning and Deep Learning, practical experience building AI applications, and the confidence to continue your journey toward becoming a Machine Learning Engineer, AI Engineer, or Data Scientist.