Se ha denunciado esta presentación.
Se está descargando tu SlideShare. ×

لموعد الإثنين 03 يناير 2022 143 مبادرة #تواصل_تطوير المحاضرة ال 143 من المبادرة المهندس / محمد الرافعي طرباي نقيب المبرمجين بالدقهلية بعنوان "IT INDUSTRY" How To Getting Into IT With Zero Experience

Anuncio
Anuncio
Anuncio
Anuncio
Anuncio
Anuncio
Anuncio
Anuncio
Anuncio
Anuncio
Anuncio
Anuncio
IT Industry
How to Getting Into IT With Zero Experience?
Eng. Rafie Tarabay
eng_rafie@mans.edu.eg
• Head of Students' scientific society 1997 to
2000
• Head of Programmers Syndicate DK Branch
from 2011 till now.
• Work o...
Where to go?
1. Help desk position: ICDL, ITIL
2. Tester: ISTQB
3. SystemAdministrator:
1. MCSE
2. CCNA
3. Linux
4. DevOPs...
Anuncio
Anuncio
Anuncio
Anuncio
Anuncio
Anuncio
Próximo SlideShare
Unit IV Software Engineering
Unit IV Software Engineering
Cargando en…3
×

Eche un vistazo a continuación

1 de 31 Anuncio

لموعد الإثنين 03 يناير 2022 143 مبادرة #تواصل_تطوير المحاضرة ال 143 من المبادرة المهندس / محمد الرافعي طرباي نقيب المبرمجين بالدقهلية بعنوان "IT INDUSTRY" How To Getting Into IT With Zero Experience

Descargar para leer sin conexión

الموعد الإثنين 03 يناير 2022
143
مبادرة
#تواصل_تطوير
المحاضرة ال 143 من المبادرة
المهندس / محمد الرافعي طرباي
نقيب المبرمجين بالدقهلية
بعنوان
"IT INDUSTRY"
How To Getting Into IT With Zero Experience

وذلك يوم الإثنين 03 يناير2022
السابعة مساء توقيت القاهرة
الثامنة مساء توقيت مكة المكرمة

و الحضور من تطبيق زووم
https://us02web.zoom.us/meeting/register/tZUpf-GsrD4jH9N9AxO39J013c1D4bqJNTcu

علما ان هناك بث مباشر للمحاضرة على القنوات الخاصة بجمعية المهندسين المصريين
ونأمل أن نوفق في تقديم ما ينفع المهندس ومهمة الهندسة في عالمنا العربي
والله الموفق

للتواصل مع إدارة المبادرة عبر قناة التليجرام
https://t.me/EEAKSA

ومتابعة المبادرة والبث المباشر عبر نوافذنا المختلفة

رابط اللينكدان والمكتبة الالكترونية
https://www.linkedin.com/company/eeaksa-egyptian-engineers-association/

رابط قناة التويتر
https://twitter.com/eeaksa

رابط قناة الفيسبوك
https://www.facebook.com/EEAKSA

رابط قناة اليوتيوب
https://www.youtube.com/user/EEAchannal

رابط التسجيل العام للمحاضرات
https://forms.gle/vVmw7L187tiATRPw9

ملحوظة : توجد شهادات حضور مجانية لمن يسجل فى رابط التقيم اخر المحاضرة

الموعد الإثنين 03 يناير 2022
143
مبادرة
#تواصل_تطوير
المحاضرة ال 143 من المبادرة
المهندس / محمد الرافعي طرباي
نقيب المبرمجين بالدقهلية
بعنوان
"IT INDUSTRY"
How To Getting Into IT With Zero Experience

وذلك يوم الإثنين 03 يناير2022
السابعة مساء توقيت القاهرة
الثامنة مساء توقيت مكة المكرمة

و الحضور من تطبيق زووم
https://us02web.zoom.us/meeting/register/tZUpf-GsrD4jH9N9AxO39J013c1D4bqJNTcu

علما ان هناك بث مباشر للمحاضرة على القنوات الخاصة بجمعية المهندسين المصريين
ونأمل أن نوفق في تقديم ما ينفع المهندس ومهمة الهندسة في عالمنا العربي
والله الموفق

للتواصل مع إدارة المبادرة عبر قناة التليجرام
https://t.me/EEAKSA

ومتابعة المبادرة والبث المباشر عبر نوافذنا المختلفة

رابط اللينكدان والمكتبة الالكترونية
https://www.linkedin.com/company/eeaksa-egyptian-engineers-association/

رابط قناة التويتر
https://twitter.com/eeaksa

رابط قناة الفيسبوك
https://www.facebook.com/EEAKSA

رابط قناة اليوتيوب
https://www.youtube.com/user/EEAchannal

رابط التسجيل العام للمحاضرات
https://forms.gle/vVmw7L187tiATRPw9

ملحوظة : توجد شهادات حضور مجانية لمن يسجل فى رابط التقيم اخر المحاضرة

Anuncio
Anuncio

Más Contenido Relacionado

Presentaciones para usted (20)

Similares a لموعد الإثنين 03 يناير 2022 143 مبادرة #تواصل_تطوير المحاضرة ال 143 من المبادرة المهندس / محمد الرافعي طرباي نقيب المبرمجين بالدقهلية بعنوان "IT INDUSTRY" How To Getting Into IT With Zero Experience (20)

Anuncio

Más de Egyptian Engineers Association (20)

Más reciente (20)

Anuncio

لموعد الإثنين 03 يناير 2022 143 مبادرة #تواصل_تطوير المحاضرة ال 143 من المبادرة المهندس / محمد الرافعي طرباي نقيب المبرمجين بالدقهلية بعنوان "IT INDUSTRY" How To Getting Into IT With Zero Experience

  1. 1. IT Industry How to Getting Into IT With Zero Experience? Eng. Rafie Tarabay eng_rafie@mans.edu.eg
  2. 2. • Head of Students' scientific society 1997 to 2000 • Head of Programmers Syndicate DK Branch from 2011 till now. • Work on GULF 4 years, and on IBM 10 years • Work as a Senior Engineer on Dubai (Mohammed Bin Rashid Al Maktoum Knowledge Foundation) for 2 years • Develop many enterprise applications that server Egyptian Government and more that 100 Organization/Universities on MEA • Application Development Consultant 21+ years Full stack developer, Certified ITIL • Expert in Bigdata, Machine Learning, Block Chain, and IOT • Have 4 Mastery Award from IBM 1) IBM Artificial Intelligence Analyst Mastery Award 2) IBM Predictive Analytics Mastery Award 3) IBM IOT Mastery Award 4) IBM Blockchain Mastery Award
  3. 3. Where to go? 1. Help desk position: ICDL, ITIL 2. Tester: ISTQB 3. SystemAdministrator: 1. MCSE 2. CCNA 3. Linux 4. DevOPs (Docker , Kubernetes) 5. Cloud 4. Application Developer
  4. 4. Introduction to Development Path About System Analysis Steps PMP vs Agile (PM vs PO, and Scrum Master) Career path: Developer, Senior Developer ,Team Lead, System Analyst, System Architecture, Project Manager Mobile Flutter, React-Native Web-Technology • Backend: PHP, Java, NodeJs, Python, ASP.net core 6.0 • Frontend: Angular, VueJS, React + Bootstrap 5.0+HTML, CSS, JavaScript Database SQL Server, DB2 Oracle MySQL/ MariaDB, MongoDB Advanced Topics MQ/Kafka , Solr 8, Blockchain, Big Data/Hadoop, Redis
  5. 5. In Software Development 1. Start small then extend 2. Change one thing at a time 3. Add error handling early 4. Test the part before whole 5. Before change, first understand the existing code 6. There will always be bugs 7. Ask, ask, and ask 8. Keep learning
  6. 6. Open Source ERP: Odoo, ERP Next eCommerce: NopCommerce, GrandNode Portals: WordPress, Joomla
  7. 7. Cloud 1. MS Azure 2. Amazon AWS 3. Google Cloud 4. IBM Cloud 5. Digital Ocean
  8. 8. AI industry 1. Software Engineer 2. Data Engineer 3. Data Science 4. Data Analysis
  9. 9. Machine learning techniques Machine learning mainly has three types of learning techniques: • Supervised learning • Unsupervised learning • Reinforcement learning
  10. 10. Accuracy vs Precision  Accuracy is how close a measured value is to the actual (true) value.  Precision is how close the measured values are to each other.
  11. 11. Machine Learning tasks categories 1. Classification 2. Regression 3. Clustering 4. Anomaly detection 5. Association 6. Recommendation 7. Dimensionality reduction 8. Computer Vision 9. Text Analytics
  12. 12. Common modeling techniques Supervised Learning • describe and distinguishes classes for future prediction (on new data) based on training data • Classification & Prediction • Common Methods: Decision Trees, Regression, Nearest Neighbours, Neural Networks Unsupervised Learning • Analyses data where labels are unknown to create groups/classes for objects that are similar to each other within the group but dissimilar to objects in other clusters • Cluster • Common Methods: K-means, Hierarchical, Two-Step Association • Analyzing data to detect items that occur together • Association • Common Methods Apriori, CARMA
  13. 13. Classification [supervised learning] • Classification is concerned with building models that separate data into distinct classes. These models are built by inputting a set of training data for which the classes are pre-labelled in order for the algorithm to learn from. The model is then used by inputting a different dataset for which the classes are withheld, allowing the model to predict their class membership based on what it has learned from the training set. Binary classification examples (divide data to two options only) • spam filtering is a classification task • Tumor diagnosis can be treated as a classification problem. • determining credit risk using personal information such as income, outstanding debt • use ROC Curves (Receiver operator characteristics) to summarize & present performance, models distinguish between false & true positives Multi-class classification examples • handwritten recognition each character is a multi-class classification problem • image recognition is a multi-class classification task • Xbox Kinect360, which infers body parts and position
  14. 14. Regression [ supervised learning] • The goal is to predict a numerical label for an unlabeled observation Regression algorithms: Linear regression, Decision trees Examples • home valuation • Asset trading, and forecasting • Sales or inventory forecasting
  15. 15. Anomaly Detection [ supervised learning] the goal is to find outliers, noise, deviations in a dataset Anomaly detection applications • In manufacturing, it is used for automatically finding defective products. • In data centers, it is used for detecting bad systems. • Websites use it for fraud detection. • Detecting security attacks. Network traffic associated with a security attack is unlike normal network traffic. Similarly, hacker activity on a machine will be different from a normal user activity.
  16. 16. Clustering [unsupervised learning] • The aim is to split a dataset into a specified number of clusters or segments. • Elements in the same cluster are more similar to each other than to those in other clusters. • Elbow diagram we use to find the best no of clusters Clustering algorithms 1) k-means • The number of clusters (k) must be given explicitly. • Identify the best k cluster centers in an iterative manner • Clusters are assumed to be spherical. 2) OPTICS /DBSCAN • it is a density-based clustering algorithm. represents clusters by its nature Example • creating customer segments, which can be targeted with different marketing programs
  17. 17. Association [unsupervised learning] • Association is most easily explained by introducing market basket analysis, a typical task for which it is well-known. • attempts to identify associations between the various items that have been chosen by a particular shopper and placed in their market basket and assigns support and confidence measures for comparison. • The value of this lies in cross-marketing and customer behavior analysis.
  18. 18. Dimensionality Reduction [unsupervised learning] • The goal in dimensionality reduction is to reduce the number of features in a dataset without significantly impacting the predictive performance of a model and this will reduce the computational complexity and cost of machine learning.
  19. 19. Recommendation [Reinforcement] • The goal of a recommendation system is to recommend a product to a user based on past behavior to determine user preferences. • Unlike Association, Recommendation focus on user behavior and suggest according to this user behavior. • It is reinforcement because we not sure of result until user choose one of our recommendation, if not, then our recommendation was not correct.
  20. 20. Traditional ML Algorithms • can work on low-end machines • break the problem down into different parts, solve them individually and combine them to get the result. • takes much less time to train, ranging from a few seconds to a few hours. • gives us the selection rules, so it is easy to interpret, safe to use in industry for interpretability. Deep learning algorithms • need a large amount of data to work perfect • heavily depend on GPUs • solve the problem end-to-end. • takes a long time to train. (like two weeks) • It is excellent and is near human performance, but no guarantee to be always like this!, because most of operations are hidden and we can’t review selection logic!
  21. 21. Deep Learning Birthdate ImageNet challenge: It is Olympics of computer vision!, Every year, researchers attempt to classify images into one of 200 possible classes given a training dataset of approximately 450,000 images. The goal of the competition is to push the state of the art in computer vision to rival the accuracy of human vision itself (approximately 95– 96%). In 2012, Alex Krizhevsky at the University of Toronto did the unthinkable. Pioneering a deep learning architecture known as a convolutional neural network for the first time on a challenge of this size and complexity, he blew the competition out of the water. The runner up in the competition scored a commendable 26.1% error rate. But AlexNet, over the course of just a few months of work, completely crushed 50 years of traditional computer vision research with an error rate of approximately 16%
  22. 22. Deep learning CNN/RNN CNN is a feed forward neural network that is generally used for Image recognition and object classification. CNN considers only the current input while RNN considers the current input and also the previously received inputs. It can memorize previous inputs due to its internal memory. Use CNNs For: Image data Use RNNs For: Text data, Speech data
  23. 23. Deep Learning Example GFP-GAN is a new free AI model for photo restoration.
  24. 24. Deep Learning Example A new GAN algorithm to composite the 'real' images, MixNMatch, from University of California. MixNMatch learns the background, object pose, shape, and texture from real images with minimal supervision, to mix and generated realistic images. Read more here, MixNMatch: https://arxiv.org/abs/1911.11758 GAN framework: https://arxiv.org/abs/1811.11155
  25. 25. Online Demo How to build a simple CURD How to publish to AWS
  26. 26. Questions & answers Invite questions from the audience.
  27. 27. IT Industry How to Getting Into IT With Zero Experience? Eng. Rafie Tarabay eng_rafie@mans.edu.eg

×