Published 8/2023
MP4 | Video: h264, 1280×720 | Audio: AAC, 44.1 KHz
Language: English | Size: 10.17 GB | Duration: 46h 45m
Practical Oriented Explanations by solving more than 80 projects with Numpy, Scikit-learn, Pandas, Matplotlib, Pytorch.
What you’ll learn
Theory, Maths and Implementation of machine learning and deep learning algorithms.
Classification Models used in classical Machine Learning such as Logistic Regression, KNN, Support Vector Machines, Decision Trees, and Random Forest
Build Artificial Neural Networks and use them for Regression and Classification Problems
Using GPU with Neural Networks and Deep Learning Models.
Convolutional Neural Networks
Transfer Learning
Recurrent Neural Networks and LSTM
Time series forecasting and classification.
Autoencoders
Generative Adversarial Networks (GANs)
Python from scratch
Numpy, Matplotlib, Seaborn, Pandas, Pytorch, Scikit-learn and other python libraries.
More than 80 projects solved with Machine Learning and Deep Learning models
Requirements
Some Programming Knowledge is preferable but not necessary
Gmail account ( For Google Colab )
Description
IntroductionIntroduction of the CourseIntroduction to Machine Learning and Deep LearningIntroduction to Google ColabPython Crash CourseData PreprocessingSupervised Machine LearningRegression AnalysisLogistic RegressionK-Nearest Neighbor (KNN)Bayes Theorem and Naive Bayes ClassifierSupport Vector Machine (SVM)Decision TreesRandom ForestBoosting Methods in Machine LearningIntroduction to Neural Networks and Deep LearningActivation FunctionsLoss FunctionsBack PropagationNeural Networks for Regression AnalysisNeural Networks for ClassificationDropout Regularization and Batch NormalizationConvolutional Neural Network (CNN)Recurrent Neural Network (RNN)AutoencodersGenerative Adversarial Network (GAN)Unsupervised Machine LearningK-Means ClusteringHierarchical ClusteringDensity Based Spatial Clustering Of Applications With Noise (DBSCAN)Gaussian Mixture Model (GMM) ClusteringPrincipal Component Analysis (PCA)What you’ll learnTheory, 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 NetworksTransfer LearningRecurrent Neural NetworksTime series forecasting and classification.AutoencodersGenerative Adversarial NetworksPython from scratchNumpy, Matplotlib, seaborn, Pandas, Pytorch, scikit-learn and other python libraries.More than 80 projects solved with Machine Learning and Deep Learning models.Who this course is for:Students in Machine Learning and Deep Learning course.Beginners Who want to Learn Machine Learning and Deep Learning from Scratch.Researchers in Artificial Intelligence.Students and Researchers who want to develop Python Programming skills to solve Machine Learning and Deep Learning Tasks.Want to switch from Matlab and Other Programming Languages to Python.
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