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)

Currently Playing: Balancing Algorithms for Data Science : Oversampling & Undersampling Techniques شرح

Oversampling , Undersampling SMOTE , SMOTE ENN, ADASYN, Borderline-SMOTE Tomek Links (Data Cleaning), SMOTE + Tomek (The Power Combo) Class Weight Mechanism XGBoost & LightGBM Scaling

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