Machine Learning & Deep Learning Masterclass in One Semester

Introduction Introduction of the Course Introduction to Machine Learning and Deep Learning Introduction to Google Colab Python Crash Course Data Preprocessing Supervised Machine Learning Regression Analysis Logistic RegressionK-Nearest Neighbor (KNN)Bayes Theorem and Naive Bayes Classifier Support Vector Machine (SVM)Decision Trees Random Forest Boosting Methods in Machine Learning Introduction to Neural Networks and Deep Learning Activation Functions Loss Functions Back Propagation Neural Networks for Regression Analysis Neural Networks for Classification Dropout Regularization and Batch Normalization Convolutional Neural Network (CNNs)Recurrent Neural Network (RNNs)Autoencoders Generative Adversarial Network (GANs)Unsupervised Machine LearningK-Means Clustering Hierarchical Clustering Density Based Spatial Clustering Of Applications With Noise (DBSCAN)Gaussian Mixture Model (GMM) Clustering Principal Component Analysis (PCA)What you’ll learn Theory, Maths and Implementation of machine learning and deep learning algorithms.Regression Analysis.Classification Models used in classical Machine Learning such as Logistic Regression, KNN, Support Vector Machines, Decision Trees, Random Forest, and Boosting Methods in Machine Learning.Build Artificial Neural Networks and use them for Regression and Classification Problems.Using GPU with Deep Learning Models.Convolutional Neural Networks Transfer Learning Recurrent Neural Networks Time series forecasting and classification.Autoencoders Generative Adversarial Networks Python from scr