ElhosseiniAcademy

Understanding Iterables and Iterators - Continued | OOP

COA |Chapter 05 Internal Memory Part 03 | DRAM - SRAM بالعربي

Numpy Part 01 |Packages بالعربي

Vectorization Broadcasting in Python Numpy

COA |Chapter 10 Instruction sets | Machine instruction / Opcode / Number of addresses بالعربي

Pandas 03 | DataFrame - Sorting and Subsetting

Pandas 03 | DataFrame - Sorting and Subsetting بالعربي

Lecture 59 Java | Array [ Example - Reverse an Array ] بالعربي

Class Attribute | OOP

Numpy Part 02 |Packages بالعربي

Python calculation code in Latex | Handcalcs

Pandas 05 | DataFrame - Summary Statistics بالعربي

Pandas 05 | DataFrame - Summary Statistics E

COA |Chapter 04 Cache Memory Part 02 | Hit Ratio بالعربي

Lecture 38: Confusion Matrix

Lecture 61: Logistic Regression

Lecture 56: Early Stopping

Pandas 10 |Explicit Index بالعربي

Color Grading | Blackboard

Mastering Generator Functions | OOP

List Comprehension & Generators | OOP

Lecture 20 Java | Triangle Area بالعربي

Lecture 18 Java | BMI Calculator بالعربي

Lecture 53 Java | Loop [ Break - Continue ] بالعربي

Pandas 11 |Slicing using loc accessor بالعربي

Lecture 04: Machine Learning Workflow Simplified

World Bank Data Analysis with Python: Use Case | OOP

Understanding Iterables and Iterators - Continued | OOP

Uploading Files & Making Announcements | Blackboard

Lecture 51 Java | Loop [ Nested Loop - Example 01 ] بالعربي

Building The First Deep Neural Network using Keras

Lecture 44: MultiLabel Classification

Lecture 45: Multioutput Classification

Lecture 53: Introduction to Regularization

Lecture 52: Cross Validation - Learning Curves

Lecture 26: Beyond Pre-Built: Crafting Custom Transformers for Machine Learning Excellence

Lecture 22: Missing Data Mastery: Cleaning and Imputation for Better Models

Lecture 06: Hands-On Fuzzy Logic Design: Solving the Tipping Problem in MATLAB

Lecture 62: Logistic Regression Applications [Iris - Diabetes]

Lecture 59: Perceptron Algorithm Implementation

Lecture 60: Perceptron Applications - Spam Email Filter

Lecture 36: Binary Classification through MNIST Dataset

Lecture 39: Precision vs. Recall: Finding the Perfect Balance 📊

Lecture 04: From Crisp to Fuzzy: Taming Uncertainty with Fuzzy Logic

Lecture 25: Mastering Scikit-learn: Design for Machine Learning Success 🚀

Lecture 50: Gradient Descent (Batch - Stochastic - MiniBatch) | Linear Regression

Lecture 35: From Code to Cloud: Mastering System Deployment and Monitoring

Lecture 24: 📏 Feature Scaling & Transformation: Taming Your Data for ML Success 🚀

Lecture 34: Confidence Intervals & the Empirical Rule, and Your Pre-Deployment Checklist

Lecture 27: Transformation Pipelines Masterclass 🚀- Pipelines, ColumnTransformer, and More!

Lecture 40: 📈 Precision/Recall Curves Decoded: Visualizing the Impact of Threshold Adjustments 📊