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!!!
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.
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.
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
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.
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 ❤
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
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
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
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
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.
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
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.
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
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.
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
19:00 - 36:00 Modes of Model . Learning & Prediction or Inference mode. training data 70% test data 30% . Overtrain/Hardcode if 100% accuracy. Training accuracy and validation accuracy should be nearly similar. For example :- a person using a software if gets a new software to use, he will shiver and tremble.
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)
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.
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 ❤
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
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.
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
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.
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.
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....
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.
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 .
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.
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
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
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.
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
18:25 - Structured Data has a particular structure that is if you see if you write a Maths equation and tend to solve it then it has a proper structure to be followed that is step 1 we should do like this and the steps go until u have the desired result
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
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.
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 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
1- a table contain some data if it can't define correct purpose it known as unlabel, if it define this column is contain age data and this column contains marks is known as label 2- speech is unstructure, step by step information is structure
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.
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
Labeled data woh hota hy jis me clearly defined ho k input or output kya hy. organized data is structured data while unorganized data is unstructured data
Label data is classified data in which we organize our data into a group like age, name, blood group ita a example of label data and unlabeled data is that in which we don't classified our data into a group we just keep them in module
Labeled (Excel Sheet Row define age,marks and so so aur ap na sab se phel top per define kara de ta hai labeled aur row per define nahi karta wo unlabeled hota hai) Structure Data (Database ma ap table define karta hai jo scheme khalta hai ) Aur UNStructure Data (speach audio freequency)
Labeled Data : data with title (label). Unlabeled Data : data without title (label). Structured Data : Data like excel or DB data. Unstrutured data : Data unlike excel or DB data.
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 .
🎯 Key Takeaways for quick navigation: 04:27 🏗️ *Understanding Object Detection in AI* - Object Detection is a fundamental concept in AI, identifying objects like cars or humans. 05:28 🌐 *Applications of Object and Movement Detection* - Integration of object and movement detection in applications like traffic control and security systems. 06:49 🧠 *Building Big Ideas in AI* - Encouragement to focus on building upon existing AI concepts rather than reinventing the wheel. - Discussion on classification in AI, grouping similar things together for easier analysis. 08:12 🧠 *Understanding Object Entities in AI* - AI classes discuss the power of object entities within a class. 08:27 📊 *Application in Health Care* - Health Care system example showcasing how AI can predict and classify diseases. 08:40 🖼️ *Image Segmentation* - Explores image segmentation, where objects in an image are segmented and classified separately. 08:53 🤖 *AI in Object Recognition* - Discusses AI's role in object recognition, using the example of distinguishing between animals and humans. 09:20 🧠 *Developing AI Understanding* - Emphasizes the difference between human thinking and AI's ability to learn and understand patterns. 10:01 🔄 *AI Model Training Process* - Explains the process of training an AI model by feeding it data, teaching it to see, hear, and test its abilities. 10:46 🌐 *Future of AI and Hardware Combination* - Discusses the future of AI, mentioning the importance of hardware combinations. 11:16 ⚙️ *Generative Models and Synthetic Data* - Introduces generative models creating synthetic data resembling real data. 11:44 🎮 *Presentation Control with AI* - Explains using AI for controlling presentations, interpreting gestures, and predicting actions. 12:00 🤖 *Model Information Points and Generation* - Describes the process of generating model information points. 12:13 🧠 *Differentiation in AI Responses* - Talks about the uniqueness and differentiation in AI responses to questions. 12:39 🧠 *Understanding Information Generation in AI* - Exploring why clouds form and how information generation is like dreaming. 13:08 🌐 *Applications of Image Generation in AI* - Extracting data points from generative models for image generation. 14:15 💼 *AI Applications Beyond Image Generation* - Connecting AI concepts to freelancing opportunities. 15:16 🎓 *Overview of AI Branches: Explicit Programming vs. AI Learning* - Differentiating explicit programming from AI learning methods. 16:38 🚀 *Advancements in AI: Sparking the Fire* - Overview of advanced AI, including ANI (Artificial Narrow Intelligence) and AGI (Artificial General Intelligence). 16:53 🤖 *Introduction to Machine Learning* - Overview of the value of AI in generating insights. 18:00 📊 *Supervised Learning and Labeled Data* - Significance of labeled data in Supervised Learning. 19:12 🔄 *Training and Inference Modes in Machine Learning* - Explanation of the two modes: Training and Inference. 20:08 🔄 *Feedback in Supervised Learning* 21:04 🧠 *Learning Modes and Input-Output Pairs* - Need for input-output pairs in both learning modes. 21:29 🧠 *Understanding Model Output* - Discussing the conversion of non-input to output in machine learning models. 22:54 📚 *Importance of Learning and Confidence* - Encouraging not to fear wrong answers as it helps in learning and improvement. 24:24 💡 *Stock and Demand in Business* - Drawing parallels between stock production in the market and machine learning. 25:46 🔍 *Accuracy and Overfitting in Machine Learning* - Discussing the concept of overfitting in machine learning models. 26:54 🤔 *Addressing Doubts and Self-Reflection* 27:56 🚂 *Understanding the Importance of Training Data* - The significance of training data in machine learning, 28:51 📊 *Balancing Training and Validation Data* - Allocating a portion (70%) of available data for training and the rest (30%) for validation, 30:01 🧪 *Evaluating Model Performance* - The rule of thumb for balancing training and validation data, 31:51 🔄 *Learning from a Real-world Analogy* - Drawing parallels between model training and following a step-by-step process, 32:19 🤔 *Reflecting on the Learning Journey* - Addressing the challenges and decisions in the learning process, 32:34 🚀 *Career Progression after Course Completion* - Individuals start at different points in their learning journey. 33:01 🛤️ *Navigating Your Career Path* - Success may not follow a linear trajectory; realistic expectations are crucial. 33:29 🎯 *Focusing on Self-Improvement* - Encourages setting personal goals and continuously improving. 33:58 🌐 *Popularizing AI* - Stresses the importance of popularizing AI to generate public interest. 34:22 ⚙️ *The Power of AI in Education* - AI can be a powerful tool in education, especially in underserved areas. 34:50 🏏 *Connecting Passion to Opportunities* 35:18 🤖 *Model Training and Inference* 35:57 📊 *Prediction with Anonymized Data* 36:13 🔧 *Technical Challenges and Humor* 36:39 🧠 *Understanding Classical Machine Learning Basics* 37:35 🌐 *Why Choose Supervised Learning?* 39:07 🖼️ *Labeling Data for Supervised Learning* 41:00 🤖 *Developing Learning Algorithms with Grouping* 42:22 🧩 *Understanding Unsupervised Learning in AI* 43:13 🗃️ *Application of Unsupervised Learning in Document Clustering* 44:07 🌐 *Challenges and Considerations in Clustering* 45:08 🤔 *Why Concepts Understanding Matters* 45:45 🚑 *Applying AI Concepts to Real-Life Problems* 46:28 🧩 *Clustering Applications and Problem Solving* 47:08 🧠 *Generating Ideas through Problem-Solving* 47:51 🌐 *Connecting Concepts and Building Stories* 48:46 🌍 *AI Concepts Becoming Familiar* 49:29 🚀 *Transitioning to AI World with Confidence* 50:08 🧠 *Explaining Supervised Learning with Clarity* 50:23 🧠 *Introduction to Unsupervised Learning* 51:13 🤖 *Two Techniques in Supervised Learning* 52:32 📊 *Application of Normal Detection in Manufacturing* 53:28 🌐 *Extending Anomaly Detection to Various Applications* 54:00 🤝 *Graph Model for Connection Analysis* 55:35 🔄 *Function Learning in Machine Learning Models* 56:01 🤖 *Image Detection Techniques and Model Learning* 57:02 🎓 *Understanding Machine Learning Models* 58:00 📊 *Model Evaluation and Overfitting* 59:11 🌐 *Supervised Learning Applications and Dimensionality Reduction* 01:00:38 🧠 *Clustering Algorithm and Decision Making* 01:01:16 🚴 *Reinforcement Learning Analogy* 01:01:50 🚴 *Reinforcement Learning and Bike Riding* 01:03:03 🌟 *Rewards and Penalties in Reinforcement Learning* 01:05:41 🤖 *Types of Learning in Machine Learning* 01:06:22 🧠 *Inspiration from Human Brain in Machine Learning* 01:07:20 🧠 *Understanding Artificial Neurons and Neural Networks* 01:08:58 📈 *The Role of Max Function in Neural Networks* 01:09:33 🤖 *Building Artificial Neural Networks* 01:10:50 🌐 *Structuring Departments Analogously to Neural Networks* 01:11:47 🎨 *Integrating Information to Form a Network* 01:12:00 🧩 *Decision-Making and Value Assignment in Networks* 01:12:14 🧠 *Understanding Neural Networks and Functions* 01:12:44 🤖 *Notable Neuron Functions* 01:13:34 📊 *Applying Functions in Neural Networks* 01:14:46 🧩 *Neural Network in Real-world Decision Making* 01:15:34 🚀 *The Complexity of Hiring Through Layers* 01:16:13 🧠 *Understanding Model Training Process* 01:16:41 🖼️ *Image Recognition and Deep Learning* 01:17:10 🧠 *Neural Network Layers and Information Flow* 01:17:56 📊 *Results Evaluation in Neural Networks* 01:18:37 🤖 *Technical Aspects and Decision Making* 01:19:33 🌐 *Collaboration and Problem Solving in Neural Networks* 01:20:15 🧠 *Efficient Neuron Usage in Problem Solving* 01:20:35 🛠️ *Building an Engine Analogy* 01:21:00 🔄 *Function in Machine Learning* 01:21:45 🧠 *Evolution of Neural Networks* 01:23:29 📈 *Rise of Data and Neural Networks* 01:24:53 🤖 *Deep Learning Essentials* 01:26:17 🧠 *Understanding Neural Networks and Research* 01:26:55 🤖 *Implementing Concepts into Code* 01:27:43 🌐 *Exploring Generative Models in AI* 01:30:13 🤖 *Types of Neural Networks* 01:31:40 🤖 *Importance of Representations in AI* 01:32:44 🤔 *Questions on Generative Transformer* 01:33:55 🚀 *Understanding the Beginning of ChatGPT* 01:34:22 🌐 *Evolution of Technology* 01:35:11 🚶♂️ *Career Paths and Opportunities* 01:35:59 🔄 *Delegation and Time Management* 01:36:42 💡 *Problem-Solving and Learning* 01:37:27 📈 *Value of Time* 01:38:15 🌐 *Learning from ChatGPT* 01:39:05 🤖 *Overview of Task Delegation* 01:40:01 🌐 *Applying Task Delegation in Different Fields* 01:41:18 🚀 *Converting Ideas into Action* 01:42:43 🤝 *Client Interaction and Understanding* 01:44:08 📝 *Freelancing Strategies* 01:45:06 🤝 *Building Client Relationships* 01:46:13 🤔 *Handling Rejections and Negative Feedback* 01:47:25 📅 *Ensuring Timely Payments* 01:49:25 🧠 *Effective Communication Techniques* 01:50:41 🧠 *The Power of Content Creation* 01:51:12 🌐 *Evolving Concepts in Technology* 01:51:51 📚 *Progress in Self-Learning* Made with HARPA AI
Sir, Labeled means model ko train krte hoe jb hum model ko kisi object k bare mn bata te hn to us object ka aik naam model ko de dete hn. for example: agar hum model ko cat k bare mn bata rhe hn to usko cat ki photo dete hn aur saath usko name dete hn k ye cat h. aur unlabeled mn iska reverse hota h.
label data woh hota hai jo machine lerning learn kar ka bata sakay yeh structure dogs and cat ka hai unstructure unlabel data hota hai jo machine learn kar kay and discover kay bata deti hai kay dog and cat hai
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.❤❤❤
Unsupervised learning in artificial intelligence is a type of machine learning that learns from data without human supervision. Unlike supervised learning, unsupervised machine learning models are given unlabeled data and allowed to discover patterns and insights without any explicit guidance or instruction.
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
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 ♥♥♥♥♥
Me from india but irfan bhai is boss of best examples in the world
Yess, you're right
Yes,
Ma Shaa Allah
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!!!
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
No doubt Mr.Irfan is the great educator for common Pakistanis,Dr.Sahab is the great expert.❤
❤
Exactly.,..
Sir I am 12 Year Old Child and I saw 1st lecture and I know many thing from lecture 1 thank you sir irfan
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
May allah bless you all for your remarkable hard work 😊😊😊
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.
really interesting ...your real life example
Jazakallah sir maza aya ❤❤❤❤
Allah ap ko dunya or akhirat dono dy ameen
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
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 ۔۔
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.
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 ❤
Ma Sha Allah, amazing course. May Allah Pak bless them all
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.❤
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
1:00 Reinforcement learning. Example of bicycle. Agent, Reward, Penalty & Negative Reward
1:05:27 Deep Learning
MashAllah 💯 Great Effort and Good Course
This Course is Very Important.
Sir Irfan and Sir Sheraz Great Job 👍
🎯 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
Irfan Malik is truly a gem. Really appreciated! May Allah protect you from the evils of our country, ameen!
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
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
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
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.
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 great in giving examples, that we understand in very good way.......... Great work. Keep it up.
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
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.
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
Amazing Way of teaching.
JazakAllah Sir g.
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
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
such a massive starting teaching way to learn AI Basics to easily for the AI learner.. jazak ALLAH Sir Irfan Sb..
Amazing course.
Allah bless you Sir in every field of life and here after.
19:00 - 36:00 Modes of Model . Learning & Prediction or Inference mode. training data 70% test data 30% . Overtrain/Hardcode if 100% accuracy. Training accuracy and validation accuracy should be nearly similar. For example :- a person using a software if gets a new software to use, he will shiver and tremble.
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)
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.
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 ❤
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
I m 60 year old. I m fond of Irfan Malik for learning AI. He is my mentor under Digiskills. Weldone.
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.
Amazing lecture. Thanks of sharing knowledge.
Every lecture become more interesting.REALY like how both of you are teaching us.concept cleared
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
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.
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.
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....
From kashmir ... thanks so much sir for providing these lectures free of cost ..May Allaah reward you with goodness.
بہت زبردست طریقے سے سمجھایا آپ دونوں نے ، اللہ تعالی ہمیشہ آباد رکھیں۔۔آمین
MashAllah 💯 Great Effort and Good Course
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.
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 .
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.
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
I love Irfan Malik and the way he speak,teach everything..Just love it
Every lecture become more interesting.REALY like how both of you are teaching us.concept cleared.💕
37:50 Unsupervised Learning . A person is know by the company he keeps.
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
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.
Next level and practical examples by irfan sir❤
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
MashaAllah amazing lesson. Dr Sheraz explains things well! Mr Irfan gives such interesting examples
Irfan bhai ke examples .. love the way he deliver the knowledge ..❤❤
18:25 - Structured Data has a particular structure that is if you see if you write a Maths equation and tend to solve it then it has a proper structure to be followed that is step 1 we should do like this and the steps go until u have the desired result
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
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.
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.
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
Mahsahallah ❤ Finally 2nd Video Successfully completed 🎉 Alhamdullha ✨
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
1- a table contain some data if it can't define correct purpose it known as unlabel, if it define this column is contain age data and this column contains marks is known as label
2- speech is unstructure, step by step information is structure
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.
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
Labeled data woh hota hy jis me clearly defined ho k input or output kya hy. organized data is structured data while unorganized data is unstructured data
Label data is classified data in which we organize our data into a group like age, name, blood group ita a example of label data and unlabeled data is that in which we don't classified our data into a group we just keep them in module
Labeled (Excel Sheet Row define age,marks and so so aur ap na sab se phel top per define kara de ta hai labeled aur row per define nahi karta wo unlabeled hota hai) Structure Data (Database ma ap table define karta hai jo scheme khalta hai ) Aur UNStructure Data (speach audio freequency)
You are all word field mentors and helpers
Annotate means the process of labelling the pictures/data points/ data samples is called Annotation.
Labeled Data : data with title (label).
Unlabeled Data : data without title (label).
Structured Data : Data like excel or DB data.
Unstrutured data : Data unlike excel or DB data.
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 .
labeled data is data which is given some name or headings while the un-labeled data is without labels or names.
🎯 Key Takeaways for quick navigation:
04:27 🏗️ *Understanding Object Detection in AI*
- Object Detection is a fundamental concept in AI, identifying objects like cars or humans.
05:28 🌐 *Applications of Object and Movement Detection*
- Integration of object and movement detection in applications like traffic control and security systems.
06:49 🧠 *Building Big Ideas in AI*
- Encouragement to focus on building upon existing AI concepts rather than reinventing the wheel.
- Discussion on classification in AI, grouping similar things together for easier analysis.
08:12 🧠 *Understanding Object Entities in AI*
- AI classes discuss the power of object entities within a class.
08:27 📊 *Application in Health Care*
- Health Care system example showcasing how AI can predict and classify diseases.
08:40 🖼️ *Image Segmentation*
- Explores image segmentation, where objects in an image are segmented and classified separately.
08:53 🤖 *AI in Object Recognition*
- Discusses AI's role in object recognition, using the example of distinguishing between animals and humans.
09:20 🧠 *Developing AI Understanding*
- Emphasizes the difference between human thinking and AI's ability to learn and understand patterns.
10:01 🔄 *AI Model Training Process*
- Explains the process of training an AI model by feeding it data, teaching it to see, hear, and test its abilities.
10:46 🌐 *Future of AI and Hardware Combination*
- Discusses the future of AI, mentioning the importance of hardware combinations.
11:16 ⚙️ *Generative Models and Synthetic Data*
- Introduces generative models creating synthetic data resembling real data.
11:44 🎮 *Presentation Control with AI*
- Explains using AI for controlling presentations, interpreting gestures, and predicting actions.
12:00 🤖 *Model Information Points and Generation*
- Describes the process of generating model information points.
12:13 🧠 *Differentiation in AI Responses*
- Talks about the uniqueness and differentiation in AI responses to questions.
12:39 🧠 *Understanding Information Generation in AI*
- Exploring why clouds form and how information generation is like dreaming.
13:08 🌐 *Applications of Image Generation in AI*
- Extracting data points from generative models for image generation.
14:15 💼 *AI Applications Beyond Image Generation*
- Connecting AI concepts to freelancing opportunities.
15:16 🎓 *Overview of AI Branches: Explicit Programming vs. AI Learning*
- Differentiating explicit programming from AI learning methods.
16:38 🚀 *Advancements in AI: Sparking the Fire*
- Overview of advanced AI, including ANI (Artificial Narrow Intelligence) and AGI (Artificial General Intelligence).
16:53 🤖 *Introduction to Machine Learning*
- Overview of the value of AI in generating insights.
18:00 📊 *Supervised Learning and Labeled Data*
- Significance of labeled data in Supervised Learning.
19:12 🔄 *Training and Inference Modes in Machine Learning*
- Explanation of the two modes: Training and Inference.
20:08 🔄 *Feedback in Supervised Learning*
21:04 🧠 *Learning Modes and Input-Output Pairs*
- Need for input-output pairs in both learning modes.
21:29 🧠 *Understanding Model Output*
- Discussing the conversion of non-input to output in machine learning models.
22:54 📚 *Importance of Learning and Confidence*
- Encouraging not to fear wrong answers as it helps in learning and improvement.
24:24 💡 *Stock and Demand in Business*
- Drawing parallels between stock production in the market and machine learning.
25:46 🔍 *Accuracy and Overfitting in Machine Learning*
- Discussing the concept of overfitting in machine learning models.
26:54 🤔 *Addressing Doubts and Self-Reflection*
27:56 🚂 *Understanding the Importance of Training Data*
- The significance of training data in machine learning,
28:51 📊 *Balancing Training and Validation Data*
- Allocating a portion (70%) of available data for training and the rest (30%) for validation,
30:01 🧪 *Evaluating Model Performance*
- The rule of thumb for balancing training and validation data,
31:51 🔄 *Learning from a Real-world Analogy*
- Drawing parallels between model training and following a step-by-step process,
32:19 🤔 *Reflecting on the Learning Journey*
- Addressing the challenges and decisions in the learning process,
32:34 🚀 *Career Progression after Course Completion*
- Individuals start at different points in their learning journey.
33:01 🛤️ *Navigating Your Career Path*
- Success may not follow a linear trajectory; realistic expectations are crucial.
33:29 🎯 *Focusing on Self-Improvement*
- Encourages setting personal goals and continuously improving.
33:58 🌐 *Popularizing AI*
- Stresses the importance of popularizing AI to generate public interest.
34:22 ⚙️ *The Power of AI in Education*
- AI can be a powerful tool in education, especially in underserved areas.
34:50 🏏 *Connecting Passion to Opportunities*
35:18 🤖 *Model Training and Inference*
35:57 📊 *Prediction with Anonymized Data*
36:13 🔧 *Technical Challenges and Humor*
36:39 🧠 *Understanding Classical Machine Learning Basics*
37:35 🌐 *Why Choose Supervised Learning?*
39:07 🖼️ *Labeling Data for Supervised Learning*
41:00 🤖 *Developing Learning Algorithms with Grouping*
42:22 🧩 *Understanding Unsupervised Learning in AI*
43:13 🗃️ *Application of Unsupervised Learning in Document Clustering*
44:07 🌐 *Challenges and Considerations in Clustering*
45:08 🤔 *Why Concepts Understanding Matters*
45:45 🚑 *Applying AI Concepts to Real-Life Problems*
46:28 🧩 *Clustering Applications and Problem Solving*
47:08 🧠 *Generating Ideas through Problem-Solving*
47:51 🌐 *Connecting Concepts and Building Stories*
48:46 🌍 *AI Concepts Becoming Familiar*
49:29 🚀 *Transitioning to AI World with Confidence*
50:08 🧠 *Explaining Supervised Learning with Clarity*
50:23 🧠 *Introduction to Unsupervised Learning*
51:13 🤖 *Two Techniques in Supervised Learning*
52:32 📊 *Application of Normal Detection in Manufacturing*
53:28 🌐 *Extending Anomaly Detection to Various Applications*
54:00 🤝 *Graph Model for Connection Analysis*
55:35 🔄 *Function Learning in Machine Learning Models*
56:01 🤖 *Image Detection Techniques and Model Learning*
57:02 🎓 *Understanding Machine Learning Models*
58:00 📊 *Model Evaluation and Overfitting*
59:11 🌐 *Supervised Learning Applications and Dimensionality Reduction*
01:00:38 🧠 *Clustering Algorithm and Decision Making*
01:01:16 🚴 *Reinforcement Learning Analogy*
01:01:50 🚴 *Reinforcement Learning and Bike Riding*
01:03:03 🌟 *Rewards and Penalties in Reinforcement Learning*
01:05:41 🤖 *Types of Learning in Machine Learning*
01:06:22 🧠 *Inspiration from Human Brain in Machine Learning*
01:07:20 🧠 *Understanding Artificial Neurons and Neural Networks*
01:08:58 📈 *The Role of Max Function in Neural Networks*
01:09:33 🤖 *Building Artificial Neural Networks*
01:10:50 🌐 *Structuring Departments Analogously to Neural Networks*
01:11:47 🎨 *Integrating Information to Form a Network*
01:12:00 🧩 *Decision-Making and Value Assignment in Networks*
01:12:14 🧠 *Understanding Neural Networks and Functions*
01:12:44 🤖 *Notable Neuron Functions*
01:13:34 📊 *Applying Functions in Neural Networks*
01:14:46 🧩 *Neural Network in Real-world Decision Making*
01:15:34 🚀 *The Complexity of Hiring Through Layers*
01:16:13 🧠 *Understanding Model Training Process*
01:16:41 🖼️ *Image Recognition and Deep Learning*
01:17:10 🧠 *Neural Network Layers and Information Flow*
01:17:56 📊 *Results Evaluation in Neural Networks*
01:18:37 🤖 *Technical Aspects and Decision Making*
01:19:33 🌐 *Collaboration and Problem Solving in Neural Networks*
01:20:15 🧠 *Efficient Neuron Usage in Problem Solving*
01:20:35 🛠️ *Building an Engine Analogy*
01:21:00 🔄 *Function in Machine Learning*
01:21:45 🧠 *Evolution of Neural Networks*
01:23:29 📈 *Rise of Data and Neural Networks*
01:24:53 🤖 *Deep Learning Essentials*
01:26:17 🧠 *Understanding Neural Networks and Research*
01:26:55 🤖 *Implementing Concepts into Code*
01:27:43 🌐 *Exploring Generative Models in AI*
01:30:13 🤖 *Types of Neural Networks*
01:31:40 🤖 *Importance of Representations in AI*
01:32:44 🤔 *Questions on Generative Transformer*
01:33:55 🚀 *Understanding the Beginning of ChatGPT*
01:34:22 🌐 *Evolution of Technology*
01:35:11 🚶♂️ *Career Paths and Opportunities*
01:35:59 🔄 *Delegation and Time Management*
01:36:42 💡 *Problem-Solving and Learning*
01:37:27 📈 *Value of Time*
01:38:15 🌐 *Learning from ChatGPT*
01:39:05 🤖 *Overview of Task Delegation*
01:40:01 🌐 *Applying Task Delegation in Different Fields*
01:41:18 🚀 *Converting Ideas into Action*
01:42:43 🤝 *Client Interaction and Understanding*
01:44:08 📝 *Freelancing Strategies*
01:45:06 🤝 *Building Client Relationships*
01:46:13 🤔 *Handling Rejections and Negative Feedback*
01:47:25 📅 *Ensuring Timely Payments*
01:49:25 🧠 *Effective Communication Techniques*
01:50:41 🧠 *The Power of Content Creation*
01:51:12 🌐 *Evolving Concepts in Technology*
01:51:51 📚 *Progress in Self-Learning*
Made with HARPA AI
Sir, Labeled means model ko train krte hoe jb hum model ko kisi object k bare mn bata te hn to us object ka aik naam model ko de dete hn. for example: agar hum model ko cat k bare mn bata rhe hn to usko cat ki photo dete hn aur saath usko name dete hn k ye cat h. aur unlabeled mn iska reverse hota h.
MashaAllah great job .May Allah give u rewards for sharing our know
Outstanding Lecture Sir Thank you so much
i have tell you in a short line
Structured data: Organized and predefined format.
Unstructured data: No fixed structure, more flexible.
label data woh hota hai jo machine lerning learn kar ka bata sakay yeh structure dogs and cat ka hai unstructure unlabel data hota hai jo machine learn kar kay and discover kay bata deti hai kay dog and cat hai
like tarining
Real asset of Pakistan....... Respect for you Sir ❤
Jazak Allah. Its upto our new generation to pay attention and learn to enhance their livings.
Main to apki video watch karne se pehle hi like kar dyti hun because I know yaha se bht kch sekhne ko milega mjhe ❤
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.❤❤❤
Unsupervised learning in artificial intelligence is a type of machine learning that learns from data without human supervision. Unlike supervised learning, unsupervised machine learning models are given unlabeled data and allowed to discover patterns and insights without any explicit guidance or instruction.
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
Sir Irfan, You and Your Team is doing really a great job. Godspeed!!!
thanks to give me a deep concept about AI.👍👍👍
Very informative thanks sir Allah apko khush rakhy