Lecture 2 | AI Free Basic Course
Вставка
- Опубліковано 7 лют 2025
- The AI course is entirely free.
Live classes are held every Tuesday and Wednesday on Facebook and UA-cam from 7:30 to 9:30 p.m.
Recorded lectures will be uploaded to the UA-cam channel.
👉 For free online courses, Visit Irfan Malik's channel: / @muhammadirfanmalik
Much more:
Facebook: / iamirfansaeedmalik
Facebook Groups
Freelancing Group: / 2763770613931485
AI Group: / 1721947284991964
Instagram: / iamirfanmalikofficial
LinkedIn: / muhammadirfanmalik
Twitter: / irfan_malikx
Tiktok: / irfansaeedmalikofficial
WhatsApp Community: chat.whatsapp....
#irfanmalik #xevensolutions #xevenai #xevenskills #muhammadirfan #hopetoskills #freecourses #chatgpt #chatgpt3 #freeonlinecourses #technology #ai #artificialintelligence
I am from India and i respect this man who provides quality content to Pakistani people to enhance their skill. Love from india❤
I am from Delhi and i follow Irfan Bhai very closely very valuable person he is sharing pure gold experience
❤
We are same nation with same ancestors ...
We have thousands of years civilization....
Proud to be soth asian...🇵🇰🇮🇳🇧🇩
@@HaiDeR...sheikh-256 ♥♥♥♥♥
Love you too 💖
To both these teachers: I cannot thank you guys enough for the hard work that you are putting in training the students. It is not just that we have started loving you, it is actually the case that we respect you in a way a teacher should really be respected. Amazing teachers you both are!!!
Me from india but irfan bhai is boss of best examples in the world
Yess, you're right
Yes,
Ma Shaa Allah
yeah that's true
He simplifies concepts in a beautifull and easy way
May allah bless you all for your remarkable hard work 😊😊😊
Sir I am 12 Year Old Child and I saw 1st lecture and I know many thing from lecture 1 thank you sir irfan
No doubt Mr.Irfan is the great educator for common Pakistanis,Dr.Sahab is the great expert.❤
❤
Exactly.,..
00:11 Supervised learning is a type of AI learning that requires labeled inputs and corresponding outputs.
03:17 Language models are the basic building blocks of machine learning.
08:04 Understanding AI through classification and segmentation
10:40 Teaching generative models to generate synthetic data
16:38 Language models and generative models are sparks of fire in the field of AI.
19:18 The model operates in training and prediction modes.
25:56 The importance of providing accurate data to AI models
28:42 Training and validation data are important in AI model training.
33:42 Developing a hobby is crucial for success.
36:01 Supervised learning is the preferred approach for machine learning.
42:14 Clustering algorithm can be used to group similar documents based on topics.
44:52 Understanding the concept of applying clustering algorithm in real-life problems
48:46 Supervised learning is about finding patterns in data.
51:21 Dictation is an unsupervised learning technique
57:40 Accuracy is an important measure in classification and training a model.
1:00:57 Reinforcement learning is a type of learning where actions are influenced by rewards or punishments.
1:07:00 Artificial neurons in neural networks are connected to create a powerful network.
1:09:54 Artificial Neural Network Band
1:14:53 Machine learning involves training software or individuals based on certain qualities and criteria.
1:17:04 Identification and processing involved in neural networks
1:22:32 Neural networks are powerful for processing large amounts of data
1:26:05 Understanding Neural Networks and Technology Behind It
1:34:03 Introduction to AI and the importance of basic knowledge
1:37:23 Delegation of tasks is important for efficient work
1:43:01 Tips for building a professional mindset
1:46:25 Effective communication techniques
1:53:21 Next Lecture Inside AP Integration
Crafted by Merlin AI.
This comment should be pinned ❤
Hello ap k pas in lectures k notices hn ? Agr hain to plz mere pe email kr den . I need it
Hello well defined 👌
19:01
جزاک اللہ سر ۔۔۔آپ کے اس کورس سے ہم بہت کچھ سیکھ رہے ہیں ۔۔۔ جاب کی وجہ سے ہم یونی نہیں جاسکتے آپ ہمارے لیے ایک امید کی کرن ہیں ۔۔
I am proud of you that i have such a hero personality in Pakistan who is awesome.Allah give this nation real heros like you.❤
Jazakallah sir maza aya ❤❤❤❤
Allah ap ko dunya or akhirat dono dy ameen
This course deserve millions of views and prayers for our respected sir ❤😊
Indeed
Why ?😂
@@winter2740😂
No its reach will be limited to mostly urdu and hindi speakers unfortunately.
Yes of course
really interesting ...your real life example
Labeled data is data that has been tagged with one or more labels or categories, making it clear what the data represents. For example, in a set of labeled images, each image is accompanied by a tag indicating what is depicted in the image, such as "cat" or "dog." Labeled data is used to train supervised machine learning models.
Unlabeled data, on the other hand, does not have any explicit tags or categories associated with it. This type of data requires the use of unsupervised learning techniques to find patterns or structures within the data. Unlabeled data is often used for tasks such as clustering, dimensionality reduction, and generative modeling.
Wow
I'm started learning now qu k mjy yeh bht achi field r ap ka learning method jo ap basics sy starting hai yeh mre jaise bndy k lye bht helpful method h Allah apko or apki team ko rizk dy r izat b dy ameeen ❤
बहुत बढ़िया वीडियो! 🤩 आपने AI के कांसेप्ट्स को बहुत ही सिंपल और क्लियर तरीके से समझाया है। beginners के लिए बहुत हेल्पफुल लगा! 👍🔥
Label data is a set of which is defined and easy to classify accordingly whereas unlabeled data is a group of undefined data which is hard to classified
Ma Sha Allah, amazing course. May Allah Pak bless them all
MashAllah 💯 Great Effort and Good Course
This Course is Very Important.
Sir Irfan and Sir Sheraz Great Job 👍
The data in which Input and output is clearly defined is known as labeled data
While the data in which input and out put is not defined ia known as unlabelled data
Highly organized data is known as supervised data
While not organised data is known as unsupervised data
Me ek Mechanical Engineer hun or ye lecture le raha hu or zyada tar batain sir k upper se guzar rhi hn but sir Irfan jb example de k samjhaty hain tou bat samjh ajati h
Irfan Malik Sir, You are a real hero for everyone who wants to self-learn. I am a big fan of you.
Irfan Malik is truly a gem. Really appreciated! May Allah protect you from the evils of our country, ameen!
Irfan malik is great in giving examples, that we understand in very good way.......... Great work. Keep it up.
AOA.
Really hats off to you both and your team . really easy to understand and practical based way of learning you people are working... that really remarkable... thanks alot
such a massive starting teaching way to learn AI Basics to easily for the AI learner.. jazak ALLAH Sir Irfan Sb..
ANSWER NO.1:
Labelled Data: The data which is pre-defined inputs with corresponding outputs/labelled data. For Example, if we give a system with cat image with corresponding text output.
Un-labeled Data: The data with no corresponding outputs is said to be unlabeled data. i.e., random images with no output, unlabeled columns or rows in Excel sheet.
Structured Data: The data which is a structured format like DB Schemas or Tables.
Un-Structured Data: The data which is un-structured like voice notes.
From kashmir ... thanks so much sir for providing these lectures free of cost ..May Allaah reward you with goodness.
Masha Allah itna acha course main ny you tube pe phli bar dekha hai JazakAllah xeven soulition
Structured data is organized and formatted in a specific way, often within databases, using a predefined data model. It's easily searchable and can be processed using query languages like SQL. Unstructured data, on the other hand, lacks a specific format and organization, often found in emails, social media posts, images, videos, etc. It's not easily searchable or analyzed without special tools such as natural language processing or machine learning algorithms.
Aoa, sir apboth bht acha samjha rahy hn,
Sir main primary teacher hn aur computer,web develping aur computer ki apps ka ziada knowledge ni rakhta...mgr main ap sy kuch sekhna zaroor chahta hn, ap k lectures 50 to 60 percent samaj aa rhy hn, bar bar suon rha hn..
Bs ap ki mohbbat,shafqat aur guidance chahiye
Thanks and dua to both teachers
Irfan bhai ke examples .. love the way he deliver the knowledge ..❤❤
I have watched class 1 and now on class 2 . Many new things I learned me a chartered accountant is getting things .... really thankful to your efforts. Both have good knowledge and experience ماشاءالله لاحول ولا قوة إلا بالله
I m 60 year old. I m fond of Irfan Malik for learning AI. He is my mentor under Digiskills. Weldone.
Labeled Data: Data in which we have proper input-output pairs.
Unlabeled Data: Data without outputs/labels.
Structured Data: Data arrange in the form of tables, rows and columns or data in a database.
Unstructured Data: Data that can't be arranged in tables such as pictures, videos and voice etc
Man you got guts ❤ I love your teaching methods. More power to you. 💕 Much much appreciated . Your are absolutely a dedicated and hard working man.
Assalam o Alaikum
Kya ap kindly mjhe thora guide kr skti hain ...mjhe is course k bary main bht dair pra chala jin logon ny phle enrollment krwa the kya un logon ko emails ati hain transcripts ki .kya jo b prhaya gya hota h hs k notes milty hain?????
Kindly answer me and help i need notes yun sb info rakhna its difficult even if i make notes they are not covering whole info.I am a non tech person so quite hard for me regarding this.Jazakillah
@@anonymous25800 drive.google.com/drive/u/0/mobile/folders/16ePoT1BdXTwTdWMzWeeIRkJD48mNVFK-?usp=drive_link&pli=1
Is drive main pichly lectures ki slides . Notebooks . Assignments wagera sb available hai. Quiz ka mjhy b ni pta. Main b late join kia tha. Ab enrollment krwayi lekin koi notification ni ata quiz wagera ka .
@@anonymous25800 Baki tamam lectures recorded form main playlist bna k inky channel pr pary hain. Quizes k liye live ky end main I guess koi link wagera dety hongy. Main live km hi attend kr pati.
Study of Artificial Neural Network is Deep Learning . It is doing ML with Artificial Neural Networks. Example of Hiring. If we are hiring a person with just consulting HR, we are using ML and if we do the same process using different departments to hire a few person. It is called Deep Learning.
I love Irfan Malik and the way he speak,teach everything..Just love it
Amazing course.
Allah bless you Sir in every field of life and here after.
Every lecture become more interesting.REALY like how both of you are teaching us.concept cleared
Lable data: define input with specific name and data called labled data..
unlabelled data: Not defined With specific name
structure data; define data in organised format
unstructure data: define data in unorganised format
Sorry first I want to share something about Irfan Sir and Dr sahib you both are great human being who really want to help people without any cost you are amazing and the style of teaching is also amazing in this course i learn a lot and want to do more courses in this field thank you so much really appreciate.
Alhamdulillah
Second lecture is completed and
Mjhy kafi time lag Raha hy samjhny man kun k meri field Nahi hy magar mjhy yahan tak sab samjh aagaya hy thank you so much sir.
بہت زبردست طریقے سے سمجھایا آپ دونوں نے ، اللہ تعالی ہمیشہ آباد رکھیں۔۔آمین
Amazing Way of teaching.
JazakAllah Sir g.
Sir Assalam-O-Alikum My Name is Muhammad Habib From Rawalpindi. I am having this lecture that my friend shared with me. I am proud of myself because I am giving my time to this lecture, I am trying to understand and learn every thing I can learn from this lecture. But I have some troubles with some terms used in this lecture. I am unable to understand, but I am hoping that I will understand and learn these terms in future.
At the End, I want to thank All of your team who have worked hard and made sure that it will be available for people like me
Thank you especially Dr Saab And My dear Sir Irfan
Sir Irfan I am extreme level fan of you
I just got a freelancing course from digiskill
I like your way of talk specially your example and voice frequency ❤
1:00 Reinforcement learning. Example of bicycle. Agent, Reward, Penalty & Negative Reward
1:05:27 Deep Learning
MashAllah 💯 Great Effort and Good Course
Every lecture become more interesting.REALY like how both of you are teaching us.concept cleared.💕
thanks to give me a deep concept about AI.👍👍👍
Assalam O Alaikum
Sir, mind blowing efforts made by you and your team. I am just thinking why I haven't join your class 1 year before... Really amazing sir God bless you
Hats off to you people... Really appreciated. A comprehensive course for both Field relevant and non field people
sir i have no words to appreciate the level of quality that you are providing, in sha allah we will expand this learning culture in our nation
Very good lecture as i was searching since many days learning AI .I am from India and and am very much satisfied with the kind of lecture in AI like this.
79K views 4 months ago AI Free Basic Course
The AI course is entirely free.
Live classes are held every Tuesday and Wednesday on Facebook and UA-cam from 7:30 to 9:30 p.m.
Recorded lectures will be uploaded to the UA-cam channel. …
Unstructured data is information that is not arranged according to a preset data model or schema, and therefore cannot be stored in a traditional relational database or RDBMS. Text and multimedia are two common types of unstructured content.
MashaAllah amazing lesson. Dr Sheraz explains things well! Mr Irfan gives such interesting examples
Solute you sir , your lectures with example is sooo super , example feels like Allah blessings you knowledge that giving the ability how can lister easily understand.its realize that you really want people grow up with power of knowledge.Sir you are not earning the money,you are really earning the people,their heart and love.May Allah give you more power and his rewards.❤❤❤
Jazak Allah. Its upto our new generation to pay attention and learn to enhance their livings.
Note: There are multiple ways/techniques to solve a problem and which technique is best, it depends of problem. Clustering or Classification.
Detecting Duplicate Images in Phone Gallery is also Clustering. It's not necessar that if i can solve a problem with USML but can't SML.
Algorithm decide which thing will fall under similar cluster. It is called K-means cluster.
Anomaly - Anything weird.
In Anamoly Detection, you tell the model that certain things are normal. If something deviates from the defined normals, Model will declare it anomaly. Anomaly could be hundreds of types but Model Don't know.
Applications :
Manifacturer making Engines of Bikes based on specific normal parameters. Healthy is Normal. Anything that's rotten is abnormal.
Every ML model learns a function, maps input to output.
ہم ماڈل کو کتنا ٹرین کریں؟
There are some ways to measure the Classification Algorithm and the famous one is accuracy measurement. Model's Training accuracy is directly proportional to test accuracy. If training model's accuracy is not going above 60%, then it needs to be more complex, use different algorithms kr more neurons in neural networks.
3. Dimensionality Reduction
In USML, it projects three-dimensional room to Two-dimensional Room. 3D input to 2D output.
Reinforcement Learning
Learning Bicyle Riding in Childhood. You fell again and again but you did it. There comes a time when your muscles are trained by practice to ride bicycle.
We stidy this practice element in Reinforcement ML.
It has an AGENT, the bicycle rider who is learning to ride bicycle.
SET OF ACTION: Paddle and keep the staring straight. It will give you REWARD.
اگر گر گئے تو یہ Penalty/Negative Reward ہو گا۔
اس میں ایجنٹ ایک غیر مانوس ماحول میں ایک نئے طریقے سے کچھ خاص بتائے گئے عمل سے اسے کھنگالیں گے، ریانفورسمنٹ لرننگ کہلائے گا۔
Don't have commercial aspects but it's exciting how we learn.
Deep Learning
How human brain works. It works by Neurons.
Scientist said can we artificially create neurons.
Idea is just like human neurons combine to do so many things, we also create such neurons to perform multiple tasks.
There is no commonality between Biological Neuron and Artificial Neuron expect both are called Neuron.
دو نمبر میں سے بڑا نمبر ڈھونڈنا ایک نیورون ہے۔ یہ ڈیپ لرننگ نیورون ہے۔ یہ میکسٹ فنکشن ہے۔ میکسیمم سے یہ لفظ نکلا ہے۔ دو نمبروں میں سے بڑا نمبر دے گا۔ یہ ایک مشہور آرٹیفیشل نیورون ہے۔
بہت سارے آرٹیفیشل نیورون لیں اور وہ آپس میں اسطرح کام کریں کہ وہ ان پٹ لیں اور اس پر کام کر کر کے آؤپٹ دیں۔ اسکو آرٹیفیشک نیورل نیٹ ورک کہیں گے۔
مثلا :- ایک دفتر ہے اس میں ایچ آر، اکاؤنٹ، بزنس ڈیویلپمنٹ کے ہیڈ ہیں۔ اگر ایچ آر ایک فیصلہ لیتا ہے تو
Neuron Vs Fuction
Every Neuron is a function but every Neuron can't work as a Function. Different Neurons.
RELU -Mext Function
Sigmoid Function. دنیا کا کوئی بھی نمبر دیں جواب صفر سے ایک کے درمیان آئے گا۔
ایک بندہ آیا، مختلف لیئر اور فلٹر سے وہ گزرا تو اچھی ہائرنگ ہو گی۔ نیورل نیٹ ورک کہیں گے۔
رشتہ ڈھونڈتے ہیں تو پہلے چچا سے، پھر تایا اور دوسرے رشتہ دسروں سے ملتے ہیں اور بیچ میں جتنے لوگ ٹیسٹ کر کے آؤٹ پٹ دے رہے ہیں وہ نیورونز ہیں۔
Applications of NN In Image detection where there are billions of pixels and NN works to sort out images.
Multiple Functions are helping out each other to solve a problem.
It is said, if you have sufficient number of neurons, you can make function of any problem in the world. This concept is called Universal Approximator.
If you want to invent an engine, you need different experts under one roof and name it a company. Same like above. In fact, it could not consume more data not it had capacity.
Deep Learning or study of ANN's, the more we have neurons, the more we feed data. LLM Model like GPT has 1.76 billion neurons. Deep learning is ML with ANN.
If we hire by training HR department, it's ML but if we hire through layers of other departments, it's ML with ANN.
Also watch Animated UA-cam Videos to clearn your concepts.
When we are coding all this, till then everything must be clear in your mind.
ہمارے پاس مختلف قسم کے نیورل نیٹ ورکس ہیں اور ہر نیورل نیٹ ورک مختلف کسم کرتا ہے۔
Standard Neural Network.
Deep Learning :
Rebranding/Study of Artificial Neural Network. Neural Networks were invented in 1965. There is a problem with ANN's that they need a lot of Computation Power. From 1965 till 2010, we had not sufficient Computational nor sufficient data to process. The best thing about Neural Networks is that the more we feed them data, the more it will learn contrary to Comventional ML, because it doesn't learn after feeding a certain amount of data.
MashaAllah great job .May Allah give u rewards for sharing our know
Next level and practical examples by irfan sir❤
Labelled Data: The data which is pre-defined inputs with corresponding outputs/labelled data. For Example, if we give a system with cat image with corresponding text output.
Un-labeled Data: The data with no corresponding outputs is said to be unlabeled data. i.e., random images with no output, unlabeled columns or rows in Excel sheet.
Structured Data: The data which is a structured format like DB Schemas or Tables.
Un-Structured Data: The data which is un-structured like voice notes.
Malik Sahabgreat, Sir very nice and easy to understand.
Amazing lecture. Thanks of sharing knowledge.
1. labeled data is data in which input and output is defined while in case of unlabelled data output is not defined.
2. structured data is in the form of rows and columns while unstructured data is in the form of video,image,audio.
labelled data jis main data ko koi Naam diya jata hai or in labelled main us main name ni diya jata.stuctured data Jo he chez ko differentiate kr k us ki classifications kr di jati hai like Excel ka table and un stucherd ki speech ko kehyn gy
Mahsahallah ❤ Finally 2nd Video Successfully completed 🎉 Alhamdullha ✨
I have been joining this course on 01 August 2023.
The data which have clearly mentioned or defined with inputs and their corresponding outputs is called labeled data.
The data which have not clearly mentioned or defined with inputs and their corresponding outputs is called unlabeled data.
The data which is in proper format, Easy to understand for others is known as structured data. i.e in MS Excel and in data bases the data is formatted in rows and columns.
The data which is not in proper format is known as unstructured data. i.e Frequency, Speech or audio.
Sir great.Its unbelievable that such genuis people are found in Pakistan .If practicle works related industry and freelance market are practed and learn then it would be outstanding .
ma sha Allah ...Sir you both are really amazing mentors.May Allah SWT bless you more ameen.Stay blessed.
Structure data:
Like table form data in which everything is defined for example row of age , row of salary etc.
Unstructured data:
Which is not defined like a speech and ups and down the voice
predicted output wo hota ha jo model generate krta ha or actual output woo hota ha jo input ka perfect jawab hota ha or user ko pehly sy hi pta hota ha...user bs model ka generated output or actual output ko campare krta ha or model ko feedback deta ha...or model kudh ko update krta ha
🎯 Key points for quick navigation:
00:15 *Define applications, review previous discussion.*
00:33 *Learn unsupervised, reinforcement, deep learning.*
00:48 *Start 4GB prompt engineering.*
01:01 *Discuss cloud tools.*
01:28 *80% value generated by supervised learning.*
01:41 *Example: supervised learning, image filtering.*
01:55 *Input-label terminology.*
02:23 *Expected output after filtering.*
03:01 *Introduction to machine learning components.*
03:15 *Human vs. machine intelligence.*
04:00 *Health care AI system examples.*
04:41 *Object detection basics*
04:59 *CCTV cameras detect vehicles*
05:12 *Applications of object detection*
05:28 *System detects dangerous movements*
05:42 *Sensors for unseen objects*
08:40 *Classification and prediction.*
08:53 *Image segmentation.*
09:20 *AI models understand.*
10:01 *Training AI models.*
10:46 *Hardware combinations.*
11:31 *Generating synthetic data.*
13:34 *Impossible to give*
13:57 *Session on stable diffusion*
14:15 *Learn prompting better*
14:30 *Improve communication skills*
14:43 *Discussing sector concepts*
18:25 *Labeled vs. Unlabeled Data*
18:44 *Supervised Learning Basics*
18:58 *Model Training Misconceptions*
19:12 *Modes of Model Operation*
19:27 *Feedback Loop in Supervised Learning*
25:03 *Stock market risky.*
25:16 *Overfitting risks.*
25:30 *Supervised learning importance.*
25:56 *Exam analogy clarity.*
26:54 *Missing lecture content.*
28:11 *Model training insights.*
29:04 *Training data split.*
30:01 *Train on 70% data.*
30:31 *Aim for high accuracy.*
30:42 *Validate model accuracy.*
30:55 *Avoid overfitting issues.*
31:27 *Follow systematic learning.*
32:19 *Real-world application readiness.*
33:01 *Set realistic expectations.*
34:50 *Basic AI enthusiasm.*
35:05 *Sporting events inspire passions.*
35:39 *Model predictions vs. learning.*
36:13 *Supervised learning overview.*
37:35 *Labeling challenges in AI.*
40:16 *Data points for learning.*
40:37 *Labelled data learning.*
41:00 *Group similar data.*
41:22 *Supervised learning application.*
41:45 *Image example usage.*
42:11 *Data type identification.*
42:36 *Unsupervised learning clusters.*
43:13 *Document clustering.*
43:54 *Classifying documents.*
44:27 *Document categorization.*
44:54 *Understanding concepts.*
45:32 *Supervised learning provides answers.*
45:45 *Real-life problems improve concepts.*
46:12 *Doctors diagnose using different languages.*
46:28 *Applying clustering algorithms to real-world data.*
46:55 *Classification applies better ideas.*
47:36 *Building stories in your mind.*
49:29 *Conceptual alienation avoided.*
49:52 *Understanding in difficulty.*
50:08 *Supervised learning clusters.*
50:35 *Application of clustering.*
51:13 *Unsupervised technique description.*
52:04 *Types of models.*
52:45 *Supervised learning applications.*
53:28 *Data separation techniques.*
54:00 *Clustering for healthy separation.*
54:18 *Graph model connections.*
55:12 *Function of machine learning models.*
55:35 *Higher function learning*
55:47 *Square function maps*
56:01 *Detecting duplicate images*
56:22 *Image similarity groups*
57:02 *Model training decision*
57:33 *Model accuracy assessment*
01:01:30 *Try handling the handle.*
01:01:50 *Reinforcement learning example.*
01:02:38 *Practice is essential.*
01:07:20 *Biological neurons vs. artificial neurons.*
01:08:09 *ReLU (Rectified Linear Unit) explained.*
01:09:25 *Artificial neurons collaborate to process inputs.*
01:12:27 *Deep learning refines information.*
01:12:44 *Different neuron functions.*
01:12:59 *Activation functions like sigmoid.*
01:13:34 *Flexibility in neuron outputs.*
01:13:47 *Neuron network complexity.*
01:14:18 *Technical versus assessment interviews.*
01:16:41 *Image reduction and pixel identification.*
01:16:56 *Processing involved in image identification.*
01:17:10 *Neural network's role in image understanding.*
01:17:43 *Interpretation of neural network outputs.*
01:17:56 *Neurons and layer management.*
01:18:22 *Multifunctional output processing.*
01:19:17 *Technical and battery communication.*
01:20:01 *Efficient neuron count for problem solving.*
01:21:00 *Engine creation with expert collaboration.*
01:21:31 *Universal approximate neural networks.*
01:21:45 *Types of artificial networks.*
01:22:11 *Evolution of neural networks.*
01:22:46 *Deep learning basics.*
01:23:29 *Data availability impact.*
01:24:53 *Massive language models.*
01:25:24 *Neural network applications.*
01:27:08 *Generative models discussed.*
01:27:22 *Introduction to discriminators.*
01:27:43 *Types of segmentation.*
01:28:24 *Neural network connections.*
01:30:13 *Transformer models discussed.*
01:31:40 *Importance of representations.*
01:32:44 *Introduction to Generative Transformers.*
01:33:55 *Historical background on Transformers.*
01:35:11 *Opportunities in business and technical fields.*
01:36:12 *Properly start coding.*
01:36:42 *Understand image generation.*
01:37:27 *Delegate tasks effectively.*
01:38:39 *Train yourself continually.*
01:39:18 *Generate good proposals.*
01:41:31 *Convert dialog-based model.*
01:41:44 *Care about results.*
01:41:58 *Easy conversation creation.*
01:42:28 *Conversational AI models.*
01:43:05 *Pakistan representation.*
01:44:31 *Simple English usage.*
01:47:25 *Understand developer mindset*
01:47:43 *Commit to problem-solving*
01:48:22 *Comfort and clarity*
01:49:25 *Avoid misunderstandings*
01:50:55 *Think critically*
Made with HARPA AI
48:00 Irfan sir says:- Math me Nalaik tha lekin top kiya = Over fitting
frist time i know about sir irfan in digiskill freelancing course and i very inspired♥
structure data wo hoya ha jo properly organlzed hota ha or unstructured data wo hota h jo aam lsan to samaj sata h magar computer nl
labeled data wo hota h jo dlfferent catagorles ma dlvlded hota h or unlabled data wo hota ha jo varlfled nl hota
labeled data is like the team matches which are arranged in a specific way that team one will play with team three on this date ,,,, so the way they are organized and arranged is called labeling while unlabeled data is like there is no specific calculations , no heading titles or example that farhan or saeed belongs to which team etc....
ایسا ڈیٹا جس کا جواب باخوابی علم ہو اسے labeled data کہتے ہیں۔ cat, dog,classification.
ایسا ڈیٹا جس کا جواب معلوم نہ ہو اسے unlabeled data کہتے ہیں۔ score,numbers
ایسا ڈیٹا جو rows /columns پر مشتمل ہو اسے structure data کہتے ہیں۔ name columns, age columns ,numbers at rows.
ایسا ڈیٹا جو image,speech,audio پر مشتمل ہو اسے unstructured data کہتے ہیں۔ یا ایسا ڈیٹا جو rows or columns پر مشتمل نہ ہو اسے unstructured data کہتے ہیں
Well Explained, excellent
Ml is a branche of AI, has 3 tyes supervised,un supervised and reinforcment , further supervised data has 2 types classification and regression, classification for objects and regression for valued data.more supervised learning can be structured and unstructured.
If anyone has colab notebook then kindly share
37:50 Unsupervised Learning . A person is know by the company he keeps.
Sir u r great ap k liy ilfaz khatam hain u r best &Great parson 💓💓💓💓
1) labelled data is the data which has tags of which type of data it has.
2)structured data is data which is related to other data in a schematical way.
(if im wrong corrrect me)
thank u so mch sir...Allah pak apko kamyaabiyaan ata kry.Ameen
I also started to learn this course .Can you talk me or share your experience of this course with me?
labelled data refers to the data which is classified
unlabelled data refers to the unclassified data
the data which is highly organised according to classes is structured data
the unorganised data is known as unstructured data
Labeled is Text, images and others audios etc.
Unlabeled doesn't contain more information.
Structured data is standardized, clearly and defined while unstructured data is usually store in native format.
Structured data is quantitative
Unstructured data is qualitative data .
labeled data are the data which contains some labels like Name,gender,Age etc and unlabled data does not contains labels whereas Structured data same as like we have employement data which have information about employement in structured form like name, salary,departments all informations are properly defined and unstructure data does not have properly defined informations.
Sir Irfan, You and Your Team is doing really a great job. Godspeed!!!
The data in the form of pics or labeled data is called structured data which can b understand able .
Unstructured data is the voice frequency or waves which can't be seen or which are not in the for of readable data.
1 scnd b skip ni kia m n thanks for contribution this is amazing efforts ❤
Priceless session
grate lecture and mind freshen Sir Irfan are you very humble personality what i say about you i have no words to express my feeling
AOA-labeled data are data types with properly labeled headings, for example. a name column has a heading name. unlabeled data that has not been defined for example columns without heading.
structured data: are the set of data that is properly defined for example the data in Excel sheets, and database tables are structured data. Unstructured data: the data in audio, images, and speech.
In which data we have both input and output is called labeled data and in which data we haven't any specific input or output is called unlabeled data. In which data have specifically structure or body is called structured data i.e., ms excel sheet and in which data we haven't no any structure as well as no any body is called unstructured data i.e., audio, speech etc
Real asset of Pakistan....... Respect for you Sir ❤
Label data is a data in which the things are specified
Unlabel data is a data in which the things are not specified . We specified things by using instruments
label data is the data that is well defined and can be defined and understand easily while unlabled data is totally opposite to it
structured data is the data that is prepared in a specific sequence and has a well organized structure or data skeleton that can be managed easily and effectively
great! Ustaad Gee
supervised learning are the learning in which we give input to machine and also suggest the output of the data which we given. its the input out put paring process .
label data are the data which we define by the name and structure data are the data which define in score form
Sir Ap ka smjhane ka treeka bohat acha he mujhe bohat zaberdast treeke se smj aa raha he