Advanced Machine Learning Course + Microsoft Azure AI fundamentals | Arabic
This course cover :Selected topic in Machine learning Selected topics in AI Artificial Neural NetworkDeep LearningNLPMLflowHugging FaceRNNMicrosoft Azure AI fundamentals https://drive.google.com/drive/folders/1iQc5u0KDN5Z7kRVbblHY-ytorHBvqyvY
Curated by: Dr. Ahmed Yousry (113 videos)
Tracks in this Playlist
- #1 : Introduction to Machine learning | Arabic
- #2 : How Machine learning work and what are it's issues | Arabic
- #3 : Types of Machine learning | Arabic
- #4 : Data Preprocessing techniques and Anomalies | Arabic
- # 5 : Anomaly / Outlier detection techniques and how to mitigate | Arabic
- # 6 : Statistics for Data Science part 1 | Arabic
- # 7 : Statistics for Data Science, Part 2
- # 8 : Statistics for Data Science (Bayes theorem, vectors, magnitude, matrices, Cosine similarity)
- # 9 : Feature Engineering (Feature Extraction & Selection Techniques) | Arabic
- #10 : Python with advanced examples 1: (Data types, functions, operators, sets, Lists, ...)
- # 11: Python with advanced examples 2 : Loops, Dictionaries, sets, and Tuples, define modules
- # 12 : Advanced examples with Random, args and kwargs, Pickle, files, lambda, OOP
- #13 : Object Oriented Programming in python Part 2
- #14 : Exception handling & NumPy Part 1
- #15 : NumPy Part 2
- #16 : Matplotlib Library
- #17: Pandas library
- Scaling and Outlier Techniques
- Data Integration Transformation and Encoding
- #18: Linear regression with one variable Part 1
- #19: Linear regression with one variable Part 2
- Evaluation Metrics (Classification , Regression , Errors Metrics For Deep Larning )
- #20 : Linear regression with multiple variables & scaling techniques & overfitting and underfiting
- #21 : Practical examples on linear regression
- #22 : Logistic regression Mathematics
- # 23 : EDA (Exploratory Data Analysis) with Complete Project Part 1
- #24 : EDA with Complete project Part 2
- #25 : EDA with Complete project Part 3
- #26 : EDA with Complete project Part 4
- #29 : SVM Complete project tutorial with EDA
- #30 : Breast Cancer classification with correlation feature selection and EDA
- #31 : Decision Tree Algorithm
- #32 : KNN and Naive Bayes Algorithms
- #33 : Car evaluation classification using Decision Tree Project and EDA
- #34 : Naive bayes Complete Project Tutorial & EDA Gaussian, Multinomial, Bernoulli NB
- #35 : Complete KNN Project with EDA
- #36 : Complete Project using RF and DT with EDA
- #37 : Clustering concepts and Kmeans Algorithm with Example
- #38 : Hierarchical clustering AGNES and DIANA
- # 39 : DBSCAN clustering Algorithm Explanation
- #40 : silhouette Score for calculating Clustering performance
- #27 : Classification metrics (Accuracy, precision, recall, ROC AUC, Macro, Micro, and weighted)
- Lecture 5: Support Vector Machine Part 1
- Lecture 5 : Support Vector Machine Part 2
- Lecture 5 : Support Vector Machine Part 3
- #28 : Support Vector Machine and it's parameters
- #41: K-means complete project
- #42: Clustering Customers Income using (DBSCAN, Hierarchical, K-means )
- Random forest Algorithm understanding
- #43: Ensemble ML Algorithms (Voting, Bagging, Boosting, Stacking )
- #44: Complete ML Algorithms with Student Attrition dataset
- #45: Feature selection (Kbest, select percentile, ch2) with complete project
- #46: Feature selection (RFE, RFECF, BFS, FFS, select from model) Part 2
- #47: Outlier detection techniques (IQR, Z-Score, Isolation Forest, LOF , Elliptic Envelope)
- #48: Dimension reduction techniques (PCA, LDA, TSNE)
- Balancing Algorithms for Data Science : Oversampling & Undersampling Techniques شرح
- #49: Complete Machine learning Project with many Algorithms Student Attrition dataset
- #50: Intrusion detection full project
- #51: Cancer types classification
- #52: Glioma Grading classification
- NLP and Preprocessing steps (Tokenization, Lemmatization, stemming, ..)
- Bag of words and N grams technique
- NLP POS , Syntactic Parsing and TFIDF
- Fake new Classification Complete Project using NLP
- What is Neural Networks
- Activations functions for ANN
- Fead forward Neural Network (FFNN) and Single layer perceptron (SLP)
- Back propagation theory for MLP and Numerical Example
- Role of Activation function in backpropagation
- Multiclassification Project using DNN for Mnist dataset
- Binary Breast cancer classification Project using ANN
- Introduction to Deep Learning , Batch normalization Learning rate Dropout Augmentation
- Batches, Epochs, Gradient problems, Optimizers
- Simple Regression Project using DNN
- MPG Car regression complete project using DNN
- Understanding Convolutional Neural Networks (CNN)
- Complete CNN Project using Mnist Dataset (70 k images)
- Transfer Learning Understanding
- DNN optimization Concepts (Weight initialization, regularization, Callbacks)
- DNN optimization (Lamda , Concatinate layers, Adavanced TL models, Depthwise separable convolution)
- Brain Stroke Classification with tuning Transfer, Ensemble Learning
- Knee Roughness Complete project using DNN, Random splitting data
- Chest-x-ray pneumonia Classification, Receive Dataset from Kaggle
- XAI Explainable AI Techniques (Grad-CAM , LIMe)
- Implementation of XAI (Grad CAM and LIME) with explanation
- Understanding Autoencoders
- Denoising projects Dentists and Brain tumor using AutoEncoders
- Generative Adversarial Network (GAN) Complete Project
- Understanding RNN , LSTM, GRU
- Complete project using RNN
- LSTM and GRU Mutiple projects
- Mlflow Basics Part 1
- Mlflow Basic Part 2
- Mlflow Complete Project ( Tracking )
- Mlflow Complete Project ( Packaging )
- MLflow Complete Project (Register and Models)
- Introduction to Hugging face Hub - Models - Datasets ...
- Hugging face transformers importing and custom modeling
- Importing multiple Hugging face NLP tasks
- Retrieval Augmented Generation (RAG) on Microsoft Azure