كم عدد دروس كورس AI and Machine Learning with Google Cloud من الصفر؟

الكورس يحتوي على 200 درس بإجمالي مدة 31 ساعة 18 دقيقة.

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كورس AI and Machine Learning with Google Cloud من الصفر - كورسات وشهادات معتمدة - مصر 24

Name: كورس AI and Machine Learning with Google Cloud من الصفر 2026
Availability: InStock
Author: Google Cloud Tech

How Gemini and LangChain can supercharge your time

📋 محتوى الكورس 200 درس · 31 ساعة 18 دقيقة

1 How Gemini and LangChain can supercharge your time series data 8:46
2 Data security with AI powered agents 11:48
3 Modern Edge AI experiences 12:57
4 Build your own LLM on Google Cloud 4:37
5 Accelerate AI adoption 2:48
6 How generative AI can support medical staff 5:10
7 How businesses can leverage Google Gemini AI 7:33
8 Extract value with assisted analytics 6:43
9 AI solutions for healthcare 2:33
10 What is Google Gemini Code Assist? 1:48
11 Using Gemini AI workflows for productivity | Google Cloud 1:52
12 Unlocking Document Automation with Vertex AI Platform 7:38
13 Accelerating AI inference workloads 13:39
14 Using Google Gemini AI for managing infrastructure and apps 7:04
15 Building LLM Apps on Google Cloud App Engine 5:44
16 Google Cloud’s approach to generative AI 11:02
17 Analyze audio in BigQuery with Speech-to-Text 9:53
18 Analyze documents in BigQuery with Document AI 7:41
19 Intro to Google Gemini AI and Data Analytics In BigQuery 3:42
20 Build generative apps faster with Vertex AI 8:37
21 Can AI analytics be used with sports other than golf? 0:16
22 How does AI for sports work? 0:22
23 What's the idea behind Golf with Gemini? 0:14
24 How Gen Digital uses CCAI for their enterprise 4:58
25 How Telus transformed data analysis with Contact Center AI 4:12
26 Get Started with Vector Search using Vertex AI 9:22
27 Build AI-powered apps on Vertex AI with LangChain 5:36
28 Generate image captions and ask questions with Imagen on Vertex AI 3:24
29 Prototype apps with Generative AI Studio 0:22
30 The future of security on Google Cloud 7:03
31 How Recommendations AI for Media can boost customer retention 9:12
32 How Hawaii's Department of Human Services scaled with CCAI 4:04
33 How Loveholidays scaled with Contact Center AI 4:03
34 Analyzing unstructured data in BigQuery with Vertex AI 6:02
35 Introduction to Vertex AI Model Garden 5:59
36 Generate and edit images with Generative AI Studio 3:55
37 Enterprise Search via Generative AI App Builder 4:31
38 Introducing Duet AI for code assistance 4:18
39 Introduction to foundation models on Google Cloud 5:30
40 Introduction to large language models 15:46
41 Introduction to Generative AI 22:08
42 How to tune LLMs in Generative AI Studio 4:35
43 Prototyping language apps with Generative AI Studio 5:13
44 Introduction to Machine Learning on Vertex AI 2:35
45 Is AlloyDB compatible with PostgreSQL? 2:37
46 Which AI/ML solution on Vertex AI is right for me? 7:42
47 Machine Learning Accelerator with Looker and BigQuery ML 9:56
48 How to run ML Inference with Apache Beam 7:15
49 Using ML to predict the weather and climate risk 12:41
50 What is Explainable AI? 5:02
51 What is Document AI Warehouse? 3:51
52 What is Human-in-the-Loop? 5:27
53 What is Document AI Workbench? 5:57
54 PyTorch on Vertex AI 4:18
55 Detecting fraud with Cloud Bigtable 4:13
56 Unlock biology & medicine potential with AlphaFold on Google Cloud 7:33
57 Understand text faster with Vertex AI's AutoML NLP 6:05
58 Boosting Speech-to-Text API accuracy 7:15
59 Tuning and scaling your ML models 8:24
60 How to create a great AI Assistant 20:38
61 How to get predictions from an ML model 6:27
62 How to export data from Looker to BigQuery 7:17
63 Training custom models on Vertex AI 8:52
64 Build a restaurant edge solution with Google Cloud | demo 11:16
65 Specialized Processors in Document AI 6:09
66 Storing data for machine learning 5:44
67 Using machine learning to transform finance with Google Cloud and Digits 15:34
68 General Processors in Document AI 4:24
69 Getting started with Notebooks for machine learning 4:27
70 How to use Document AI 7:17
71 What is Document AI? 4:37
72 How to analyze your data in Automatic DLP 7:22
73 Building land cover maps with AI (Dynamic World) 10:01
74 How do I add AI into my apps? 4:18
75 Making sustainability profitable in agriculture with Ambrook and Google Cloud 22:13
76 The future of electric vehicles with Ather Energy and Google Cloud 22:10
77 How are data centers powered sustainably? 6:02
78 How MLB analyzes data with Google Cloud 6:31
79 How airlines forecast demand with FLYR and Google Cloud 16:32
80 Building a better customer service platform with Thrio and Google Cloud 21:48
81 Mapping carbon pollution globally with satellites 8:46
82 Introduction to Vertex AI Feature Store 7:02
83 Illuminating the global fishing fleet through machine learning 8:03
84 Hyperparameter Tuning on Vertex AI 11:01
85 End-to-end MLOps with Vertex AI 8:29
86 Transformers, explained: Understand the model behind GPT, BERT, and T5 9:11
87 Introduction to MLOps and Vertex Pipelines 8:03
88 How to build forecasting models with Vertex AI 10:32
89 Introduction to Vertex AI SDK 9:19
90 How to build an image classification model in Vertex AI 4:34
91 Build a custom ML model with Vertex AI 10:55
92 Introduction to Tensorflow Cloud 7:10
93 Build a pet tracking camera app 7:17
94 Building and training ML models with Vertex AI 12:36
95 How to manage ML datasets with Vertex AI 7:40
96 What is Vertex AI? 7:16
97 Manage a production ML pipeline with TFX 7:24
98 Recovering global wildlife populations using ML 8:32
99 How to build an ML pipeline with TFX 7:29
100 7 best practices for Dialogflow CX 5:56
101 IVR platform & 1 click integrations 5:37
102 Build sound classification model with no code 4:42
103 How to automatically transcribe your video or audio into text 4:39
104 What is a pre-built agent in Dialogflow CX? 6:09
105 Creating a multi-flow agent with Dialogflow CX 6:03
106 Creating a single-flow conversational agent 6:43
107 What is Federated Learning? 5:36
108 Introducing pages and transitions in Dialogflow CX 6:10
109 How to dub a video with AI 5:11
110 What is Dialogflow CX? 5:37
111 What is AutoML Translation? 5:40
112 What is the Translation API? 5:24
113 Can AI make a good baking recipe? 7:06
114 Making a smart closet with ML 5:27
115 Integrate Dialogflow with Google Chat 10:33
116 Machine learning without code in the browser 2:36
117 Cloud AI Platform Pipelines overview 4:02
118 How to convert PDFs to audiobooks with machine learning 4:29
119 How to build a Kubeflow Pipeline 4:59
120 Training an ML model with Kubeflow 3:18
121 Persistent Disk for productive data science 5:50
122 Building smarter games with machine learning 5:03
123 What is KFserving? 5:28
124 AI Platform Optimizer 4:11
125 Can AI make me a better athlete? | Using machine learning to analyze penalty kicks 6:33
126 Metadata management 3:38
127 Setting up AI Platform Pipelines 7:24
128 I built an AI-powered moderation bot for Discord 6:45
129 Hyperparameter Tuning with Katib 4:29
130 I created an AI-powered video archive for searching family videos 6:21
131 Intro to Kubeflow Pipelines 4:21
132 Introduction to JAX 7:05
133 Jupyter Notebooks 3:58
134 Cloud AI Data labeling service 7:30
135 Kubeflow Components 3:52
136 Intro to Explanations for AI Platform 4:12
137 Getting Started with Kubeflow 3:51
138 Using the What-If Tool for explainability 4:49
139 Introduction to Kubeflow 3:47
140 Training models with custom containers on Cloud AI Platform 4:25
141 AI Platform Training with built-in algorithms 6:15
142 Diving into the TPU v2 and v3 6:01
143 Using Tensorflow with Docker (Google Cloud AI Huddle) 1:23:12
144 Tensor Processing Units: History and hardware 5:35
145 Train, and deploy models in Cloud and on-prem from notebooks (Google Cloud AI Huddle) 58:41
146 Using AutoML Natural Language for custom text classification 5:56
147 Getting started with Natural Language Processing: Bag of words 6:27
148 Understanding image models and predictions using an Activation Atlas 7:50
149 Visualizing Convolutional Neural Networks using Lucid 8:48
150 TensorFlow Privacy 6:39
151 AutoML Tables 5:00
152 Production ready chatbots with SpringML 10:44
153 PyTorch on GCP 3:45
154 Conversation design best practices 8:07
155 BigQuery ML: Machine Learning with Standard SQL 6:08
156 Kubeflow: Machine Learning on Kubernetes 4:19
157 How to integrate Dialogflow with Google cloud ML APIs 5:44
158 How to Upgrade Colab with More Compute 4:11
159 Create Frontend Django client for Dialogflow 6:24
160 Create FAQ Chatbot with Dialogflow 3:40
161 Fulfillment: Integrating Dialogflow with BigQuery 5:20
162 Reusable Execution in Production Using Papermill (Google Cloud AI Huddle) 1:00:11
163 Fulfillment: How to Integrate Dialogflow with Google Calendar 5:50
164 Integrating Dialogflow with Twilio Messaging Service 3:38
165 Jupyter on the web with Colab 4:08
166 AI Huddle (Google Cloud AI Huddle) 44:57
167 Understanding Entities in Dialogflow 5:48
168 Integrate Dialogflow with Telephony Gateway 2:47
169 Optimizing TensorFlow Models for Serving (Google Cloud AI Huddle) 58:45
170 Integrate Dialogflow with Actions on Google 4:25
171 How to Build an Appointment Scheduler with Dialogflow 4:40
172 Getting Started with Dialogflow 5:28
173 Deconstructing Chatbots - An Overview 3:38
174 Kubeflow - Tools and Framework (Google Cloud AI Huddle) 32:56
175 TensorFlow Eager Mode 3:20
176 AutoML Vision and Designing Product Experiences at Google (Google Cloud AI Huddle) 41:51
177 Using TensorFlow Hub for more productive machine learning 4:39
178 Training Image & Text Classification Models Faster with TPUs on Cloud ML Engine (Cloud AI Huddle) 56:57
179 Deep Learning VM Images 6:49
180 How to Import a Keras model into TensorFlow.js 4:29
181 Getting Started with TensorFlow.js 4:07
182 Scaling up Keras with Estimators 9:10
183 Deep Learning Images for Google Compute Engine (Google Cloud AI Huddle) 27:33
184 Training Keras with GPUs & Serving Predictions with Cloud ML Engine (Google Cloud AI Huddle) 1:22:17
185 Getting Started with Keras 8:11
186 Serving Scikit-learn Models at Scale 4:13
187 Learning Scikit-Learn 4:26
188 What Cloud AI can do for you (Google Cloud AI Huddle) 36:01
189 AutoML Vision - Part 2 3:37
190 AutoML Vision - Part 1 8:01
191 How to Make a Data Science Project with Kaggle 21:00
192 BigQuery and Open Datasets 3:30
193 AI Adventures: art, science, and tools of machine learning (Google I/O '18) 35:00
194 Quick Draw: the biggest doodle dataset 4:14
195 Visualize your Data with Facets 6:30
196 TensorFlow Object Detection on iOS 13:07
197 Print Statements in TensorFlow 4:22
198 Which Python Package Manager Should You Use? 5:06
199 Jupyter Tips and Tricks 6:10
200 Introduction to Kaggle Kernels 4:22

▶ يُشغَّل الآن

How Gemini and LangChain can supercharge your time series data

📘 نظرة عامة على الكورس

يُقدّم هذا الكورس المجاني شرحاً شاملاً لـ AI and Machine Learning with Google Cloud من الصفر في مجال DevOps و Cloud، ويستهدف المبتدئين الراغبين في بناء مهاراتهم العملية واحترافية في 2026. يُقدّمه Google Cloud Tech باللغة العربية بأسلوب مبسّط ومنظّم.

يضم الكورس 200 درساً بمدة إجمالية 31 ساعة 18 دقيقة، تأخذك من الصفر حتى الاحتراف خطوةً بخطوة.

لماذا تتعلم AI and Machine Learning with Google Cloud من الصفر؟

الذكاء الاصطناعي من أسرع المجالات نمواً في سوق العمل، مع زيادة الطلب على المتخصصين بنسبة 300% خلال السنوات الأخيرة.
مجاني بالكامل — ابدأ الآن دون أي رسوم
شهادة إتمام معتمدة — أضفها لـ CV وLinkedIn
تعلّم في أي وقت — بالسرعة التي تناسبك
مجالات الذكاء الاصطناعي: رؤية الحاسب، معالجة اللغة الطبيعية، الروبوتيات، الألعاب، التنبؤ والتوصية.

مسار التعلم في هذا الكورس

يسير الكورس وفق مسار منطقي متدرّج: الرياضيات والإحصاء → Python → ML → Deep Learning → Specialization

المتطلبات السابقة

Python + رياضيات (إحصاء وجبر خطي) — يمكن لأي شخص البدء في هذا الكورس.

الأدوات والبرامج المستخدمة

Python
TensorFlow
PyTorch
Scikit-learn
Jupyter
Keras
Hugging Face

متطلبات الجهاز

RAM 16GB فأكثر
GPU (NVIDIA RTX مُوصى)
SSD سريع
تخزين 500GB+

الوظائف المتاحة بعد إتمام الكورس

AI Engineer
ML Engineer
Data Scientist
AI Researcher
NLP Engineer

متوسط رواتب AI and Machine Learning with Google Cloud من الصفر 2026

مصر: 12,000-40,000 ج
السعودية: 18,000-50,000 ر
أمريكا: $120,000-$200,000

كم من الوقت تحتاج لإتقان AI and Machine Learning with Google Cloud من الصفر؟

أساسيات ML: 40-60 ساعة
Deep Learning: 60-100 ساعة
تخصص كامل: 200-400 ساعة

الشهادات المعتمدة في مجال DevOps و Cloud

Google ML Engineer
AWS ML Specialty
Coursera ML Specialization (Andrew Ng)
Deep Learning Specialization

مشاريع عملية ستتمكن من بناؤها

Image Classifier
Chatbot
Recommendation System
Object Detection
Sentiment Analysis

ماذا بعد هذا الكورس؟

بعد إتمام الكورس، يُوصى بالتعمق في: LLMs، Computer Vision، NLP، MLOps، Generative AI.

الأقسام الجامعية ذات الصلة

يُناسب هذا الكورس طلاب: كلية حاسبات ومعلومات، كلية هندسة (ذكاء اصطناعي)، جامعة القاهرة، جامعة عين شمس.

سجّل في كورس AI and Machine Learning with Google Cloud من الصفر الآن، ابدأ رحلتك نحو الاحتراف في DevOps و Cloud مجاناً. تذكّر: أفضل وقت للتعلم هو الآن.

المدرّس / القناة

Google Cloud Tech

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عدد الدروس

200 درس

⏱️

المدة الإجمالية

31 ساعة 18 دقيقة

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المستوى

مبتدئ

🌍

اللغة

العربية

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الشهادة

مجانية عند الإكمال

✅

المتطلبات

لا توجد متطلبات مسبقة

📂

التصنيف

DevOps و Cloud

🎯 ماذا ستتعلم في هذا الكورس

✓

How Gemini and LangChain can supercharge your time series data

✓

Data security with AI powered agents

✓

Modern Edge AI experiences

✓

Build your own LLM on Google Cloud

✓

Accelerate AI adoption

✓

How generative AI can support medical staff

✓

How businesses can leverage Google Gemini AI

✓

Extract value with assisted analytics

✓

+ 192 موضوع آخر...

💼 الوظائف التي يؤهلك لها الكورس

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متعلق بـ DevOps و Cloud

❓ أسئلة شائعة عن الكورس

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يحتوي الكورس على 200 درس بإجمالي مدة 31 ساعة 18 دقيقة.

هل الكورس مجاني بالكامل؟ +

نعم، الكورس مجاني تماماً ومتاح بالكامل على منصة مصر 24 بدون أي رسوم.

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الكورس موجّه لمستوى مبتدئ، وهو مناسب تماماً للمبتدئين من الصفر.

ما لغة الشرح في الكورس؟ +

الكورس مشروح باللغة العربية بأسلوب واضح ومبسّط.

هل يمكن تعلم الكورس بدون خبرة سابقة؟ +

نعم بالتأكيد. هذا الكورس مصمم للمبتدئين من الصفر.

كم يستغرق إتمام هذا الكورس؟ +

المدة الإجمالية 31 ساعة 18 دقيقة. يُنصح بتخصيص ساعة يومياً.

هل أحصل على شهادة عند الإتمام؟ +

نعم، تحصل على شهادة إتمام مجانية من مصر 24 بعد مشاهدة جميع الدروس.

من يقدم هذا الكورس؟ +

الكورس مقدم من Google Cloud Tech.

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شهادة إتمام

كورس AI and Machine Learning with Google Cloud من الصفر

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