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Development Data Science Machine Learning

Machine Learning Nanodegree

أكبر كورس عربي لتعليم الألة والذكاء الأصطناعي لتأهيل مليون عالم بيانات عربي
Rating: 4.4 out of 54.4 (2,627 ratings)
62,149 students
Created by Dr. Tamer Eid, Loaii abdalslam, Ligency Team
Last updated 9/2020
Arabic
30-Day Money-Back Guarantee

What you'll learn

  • استخدم بايثون لعلوم البيانات والتعلم الآلي
  • تنفيذ خوارزميات تعلم الآلة
  • ريادة الأعمال بأستخدام الذكاء الاصطناعي
  • كيفية صنع بيئة عمل أفتراضية للتأهيل لسوق العمل
  • العقلية التحليلة
  • NumPy for Numerical Data تعلم ال
  • Pandas for Data Analysis تعلم ال
  • SciKit-Learn for Machine Learning Tasks استخدام ال
  • K-Means Clustering
  • Logistic Regression
  • Linear Regression
  • Random Forest and Decision Trees
  • Neural Networks
  • Support Vector Machines

Course content

36 sections • 191 lectures • 31h 15m total length

  • Preview03:27
  • Preview07:11
  • مقدمة الكورس - هام جدا
    09:43
  • ملاحظات مهمة للاستفادة من هذا الكورس
    00:03
  • This PDF resource will help you a lot
    01:04
  • Anaconda و ال Python تثبيت ال
    10:28
  • Jupyter Notebooks
    28:06

  • NumPy Arrays
    14:45
  • Preview00:05
  • NumPy Array Indexing
    13:14
  • NumPy Arrays Indexing Notice
    00:06
  • NumPy Operation
    05:03
  • NumPy Operation Notice
    00:06
  • ملحوظة قبل البدء في حل التمرين
    00:06
  • NumPy Excercise Overview
    04:40
  • NumPy Exercise Solution
    06:30
  • Pandas Series
    10:10
  • Pandas Series Notice
    00:06
  • Panadas DataFrames - Part 1
    17:33
  • Panadas DataFrames - Part 1 Notice
    00:06
  • Panadas DataFrames - Part 2
    18:34
  • Panadas DataFrames - Part 2 Notice
    00:06
  • Panadas DataFrames - Part 3
    10:52
  • Pandas Missing Data
    05:36
  • Panadas Group-by
    07:00
  • Panadas Group-by Notice
    00:06
  • Panadas Merging Joining and Con
    13:23
  • Panadas Operations
    13:41
  • Panadas Data Input and Output
    14:17

  • Preview02:40
  • How to get the Dataset
    09:03
  • How to Import The Libraries and Data Set
    13:51
  • How to Import The Libraries and Data Set Notice
    00:04
  • Preview17:16
  • Missing Data Notice
    00:00
  • Categorical Data
    14:20
  • Categorical Data Notice
    00:00
  • Splitting the data
    16:35
  • Splitting the data Notice
    00:00
  • Preview13:24
  • preprocessing Data Template
    06:40
  • Quiz 1 : Data Preprocessing
    4 questions

  • Welcome to Regression
    00:21

  • business problem description
    02:58
  • Preview08:30
  • Simple linear Regression Intuition - Step 2
    03:58
  • Simple linear Regression in python - Step 1
    07:49
  • Simple linear Regression in python - Step 2
    06:30
  • Simple linear Regression in python - Step 3
    05:01
  • Simple Linear Regression in Python - Step 4
    11:43
  • Dataset + Business Problem Description
    05:42
  • Quiz 2: Simple Linear Regression
    5 questions

  • Multiple Linear Regression Intuition - Step 1
    01:17
  • Multiple Linear Regression Intuition - Step 2
    01:13
  • Multiple Linear Regression Intuition - Step 3
    08:16
  • Multiple Linear Regression Intuition - Step 4
    03:31
  • 036 Prerequisites: What is the P-Value?
    00:01
  • Multiple Linear Regression Intuition - Step 5
    26:54
  • Multiple Linear Regression in Python - Step 1
    12:06
  • Multiple Linear Regression in Python - Step 2
    03:03
  • Multiple Linear Regression in Python - Step 3
    04:35
  • Multiple Linear Regression in Python - Backward Elimination - Preparation
    12:17
  • Multiple Linear Regression in Python - Backward Elimination - Homework +Solution
    17:55
  • Multiple Linear Regression in Python - Automatic Backward Elimination
    00:40
  • Quiz 3: Multiple Linear Regression
    4 questions

  • Preview04:41
  • Polynomial Regression in Python - Step 1
    10:09
  • Polynomial Regression in Python - Step 2
    11:41
  • Polynomial Regression in Python - Step 3
    12:25
  • Polynomial Regression in Python - Step 3 Notice
    00:00
  • Polynomial Regression in Python - Step 4
    06:10
  • Python Regression Template
    06:59

  • Preview24:51
  • SVR in Python
    13:13

  • Preview08:37
  • Decision Tree Regression Intuition Notice
    00:00
  • Decision Tree Regression in Python
    14:08

  • Preview08:49
  • Random Forest Regression in Python
    15:50

Requirements

  • اللغة الأنجليزية
  • لغة البرمجة بايثون (Python)
  • أساسيات الجبر الخطي والمصفوفات
  • أساسيات الأحصاء
  • اساسيات التفاضل والتكامل
  • Algorithms and data structures
  • ****..............Intermediate statistical knowledge:
  • Populations, samples
  • Mean, median, mode
  • Standard error
  • Variation, standard deviations
  • Normal distribution
  • Precision and accuracy
  • Hypothesis testing
  • Problem solving
  • Confidence Interval, P-values, T-test, Statistical Significance
  • Intermediate calculus and linear algebra mastery:
  • Derivatives
  • Integrals
  • Series expansions
  • Matrix operations through eigenvectors and eigenvalues

Description

 الجزء - 1

 Numpy و Pandas  معالجة البيانات و :

                   Advanced Numpy                                      

                   Advanced Pandas

                   Data Preprocessing                

الجزء - 2 

Regression:                  

                  Simple Linear Regression

                  Multiple Linear Regression Intuition

                  Polynomial Regression

                  Support Vector Regression (SVR)

                  Decision Tree  Regression,

                  Random Forest Regression   

            

  الجزء - 3

Classification :

                الانحدار اللوجستي

                K-NN

               SVM                

               Kernel SVM                

               Naive Bayes                

               Decision Tree Classification                

               Random Forest Classification                

الجزء - 4  

Clustering  :

                K-Means                

               Hierarchical Clustering                

الجزء - 5   

Association Rule Learning::

               Apriori                

               Eclat                

الجزء - 6

Reinforcement Learning :

                Upper Confidence Bound                

               Thompson Sampling               

الجزء - 7   

Natural Language Processing (NLP) :

                Bag-of-words model               

                algorithms for NLP               

الجزء - 8   

Deep Learning :

                 الشبكات العصبية الاصطناعية، الشبكات العصبية التلافيفية 

                (Deep Learning: Artificial Neural Networks, Convolutional Neural Networks)

الجزء - 9

Dimensionality Reduction  :

               PCA               

               Kernel PCA               

               LDA               

الجزء - 10 

Model Selection & Boosting :

                k-fold Cross Validation               

                Parameter Tuning,                

                Grid Search               

                XGBoost               

فضلا على ذلك، فإن الدورة مليئة بالتمارين العملية التي تستند إلى أمثلة واقعية. لذا لن تتعلم النظرية فحسب، بل ستحصل أيضًا على بعض التدريب العملي  و تتعلم كيفية بناء النماذج الخاصة بك.

أيضا ستشتمل هذه الدورة التدريبية على قوالب بالبايثون و التي يمكنك تنزيلها واستخدامها في مشروعاتك الخاصة.

سيكون لديك فهم أساسي للعديد من نماذج تعلم الألة 

عمل تحليل قوي وتوقعات دقيقة للبيانات

Reinforcement Learning  و NPL و ال Deep Learning التعامل مع موضوعات محددة مثل

 Dimensionality Reduction التعامل مع التقنيات المتقدمة مثل

 سوف تتعلم كيف تختار النموذج الصحيح والمناسب لكل نوع من أنواع تعليم الألة  لكافة المشاكل المختلفة او متطلبات عملك  

  قم ببناء مزيج من عدة نماذج مختلفة لتعليم الألة بالتعلم كيفية دمجها معا لحل المشاكل الصعبة

Who this course is for:

  • الأشخاص المهتمة بتعليم ألة والذكاء الاصطتناعي
  • من عندهم خبره سابقة في تعلم أله ويريدون التعمق فيها
  • خريجي الجامعات او حتى طلاب الثانوي ويريدون العمل بتعليم الألة او مجال الذكاء الأصطتناعي
  • محللي البيانات والمحاسبين المالييين
  • الأشخاص الذين يريدون الترقي في العمل وزيادة قيمة مضافة للشركة التي يعمل بها

Instructors

Dr. Tamer Eid
Chief information Officer ida.gov.eg
Dr. Tamer Eid
  • 4.4 Instructor Rating
  • 2,627 Reviews
  • 64,164 Students
  • 1 Course

obsessed about Machine learning and deep learning , it's a love from first sight  , and i am doing my-best to be a mutual LOVE....!

the first meeting was back in 98 in my Artificial intelligence diploma  Sadaat Academy , and I remember the lecturers always saying 20 to 30 years from now to apply those beautiful concepts, it need huge computational power and millions of tons of data .....Next year i worked with first BI tool Cognos ...then ten years later Artificial intelligence was booming

I told you it's a love story with the intelligent machines and it was from the first sight     :)

Loaii abdalslam
Machine Learning Engineer
Loaii abdalslam
  • 4.4 Instructor Rating
  • 2,627 Reviews
  • 62,149 Students
  • 1 Course

Student at Faculty of Finance and Administrative Sciences, Former Machine Learning engineer at Convertedin, I'm specialized in Economic intelligence and business intelligence, Location intelligence  and student at the development of the military system with artificial intelligence techniques


طالب في  كلية العلوم المالية والإدارية و مهندس تعلم ألة متخصص في الذكاء الاقتصادي وذكاء الأعمال والذكاء الميداني وطالب تكنولوجيا الأنظمة العسكرية والسياسية

Ligency Team
Helping Data Scientists Succeed
Ligency Team
  • 4.5 Instructor Rating
  • 469,134 Reviews
  • 1,685,674 Students
  • 109 Courses

Hi there,

We are the Ligency PR and Marketing team. You will be hearing from us when new courses are released, when we publish new podcasts, blogs, share cheatsheets and more!

We are here to help you stay on the cutting edge of Data Science and Technology.

See you in class,

Sincerely,

The Real People at Ligency

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