Course Content
How and Why to Register
Dear, to register for the 6 months AI and Data Science Mentorship Program, click this link and fill the form give there: https://shorturl.at/fuMX6
0/2
Day-17: Complete EDA on Google PlayStore Apps
0/1
Day-25: Quiz Time, Data Visualization-4
0/1
Day-27: Data Scaling/Normalization/standardization and Encoding
0/2
Day-30: NumPy (Part-3)
0/1
Day-31: NumPy (Part-4)
0/1
Day-32a: NumPy (Part-5)
0/1
Day-32b: Data Preprocessing / Data Wrangling
0/1
Day-37: Algebra in Data Science
0/1
Day-56: Statistics for Data Science (Part-5)
0/1
Day-69: Machine Learning (Part-3)
0/1
Day-75: Machine Learning (Part-9)
0/1
Day-81: Machine Learning (Part-15)-Evaluation Metrics
0/2
Day-82: Machine Learning (Part-16)-Metrics for Classification
0/1
Day-85: Machine Learning (Part-19)
0/1
Day-89: Machine Learning (Part-23)
0/1
Day-91: Machine Learning (Part-25)
0/1
Day-93: Machine Learning (Part-27)
0/1
Day-117: Deep Learning (Part-14)-Complete CNN Project
0/1
Day-119: Deep Learning (Part-16)-Natural Language Processing (NLP)
0/2
Day-121: Time Series Analysis (Part-1)
0/1
Day-123: Time Series Analysis (Part-3)
0/1
Day-128: Time Series Analysis (Part-8): Complete Project
0/1
Day-129: git & GitHub Crash Course
0/1
Day-131: Improving Machine/Deep Learning Model’s Performance
0/2
Day-133: Transfer Learning and Pre-trained Models (Part-2)
0/1
Day-134 Transfer Learning and Pre-trained Models (Part-3)
0/1
Day-137: Generative AI (Part-3)
0/1
Day-139: Generative AI (Part-5)-Tensorboard
0/1
Day-145: Streamlit for webapp development and deployment (Part-1)
0/3
Day-146: Streamlit for webapp development and deployment (Part-2)
0/1
Day-147: Streamlit for webapp development and deployment (Part-3)
0/1
Day-148: Streamlit for webapp development and deployment (Part-4)
0/2
Day-149: Streamlit for webapp development and deployment (Part-5)
0/1
Day-150: Streamlit for webapp development and deployment (Part-6)
0/1
Day-151: Streamlit for webapp development and deployment (Part-7)
0/1
Day-152: Streamlit for webapp development and deployment (Part-8)
0/1
Day-153: Streamlit for webapp development and deployment (Part-9)
0/1
Day-154: Streamlit for webapp development and deployment (Part-10)
0/1
Day-155: Streamlit for webapp development and deployment (Part-11)
0/1
Day-156: Streamlit for webapp development and deployment (Part-12)
0/1
Day-157: Streamlit for webapp development and deployment (Part-13)
0/1
How to Earn using Data Science and AI skills
0/1
Day-160: Flask for web app development (Part-3)
0/1
Day-161: Flask for web app development (Part-4)
0/1
Day-162: Flask for web app development (Part-5)
0/1
Day-163: Flask for web app development (Part-6)
0/1
Day-164: Flask for web app development (Part-7)
0/2
Day-165: Flask for web app deployment (Part-8)
0/1
Day-167: FastAPI (Part-2)
0/1
Day-168: FastAPI (Part-3)
0/1
Day-169: FastAPI (Part-4)
0/1
Day-170: FastAPI (Part-5)
0/1
Day-171: FastAPI (Part-6)
0/1
Day-174: FastAPI (Part-9)
0/1
Six months of AI and Data Science Mentorship Program
    Join the conversation
    Rana Anjum Sharif 2 weeks ago
    Done
    Reply
    tayyab Ali 4 months ago
    I have done this lecture.
    Reply
    Sibtain Ali 4 months ago
    Done this video.
    Reply
    Javed Ali 6 months ago
    AOA, I learned in this lecture about the Naïve Bayes algorithm (NB) for best model selection, which is used for classification tasks ( spam filtering, basic image classification, text-based sentiment analysis), which isNAIVE BAYES (NB) ALGORITHM: What is it? Simple but surprisingly powerful algorithm for predictive modeling and machine learning. Based on Bayes’ Theorem. Particularly useful in classification tasks.Bayes’ TheoremAt its core, Bayes’ Theorem provides a way to calculate the probability of a hypothesis given our prior knowledge. Mathematically. It is expressed as:P(A|B) = P(B|A)xP(A) / P(B)Where:P(A|B) is the probability of hypothesis A given the data B. P(B|A) is the probability of the data B given that hypothesis A is given the data. P(A) is the probability of hypothesis A being true ( regardless of the data ). P(B) is the probability of the data ( regardless of the hypothesis)Bayes’ Theorem Example:Imagine you’re a teacher with a class of students, and you know the following information: 60% of the students own a bicycle. You also know that of those students who own a bicycle, 30% bring their bicycle to school. Of those students who do not own a bicycle, 10% bring a bicycle to school (maybe they borrow one).Now, if you see a student with a bicycle at school, what is the probability that the student owns a bicycle?Here, we apply Bayes’ Theorem. Let’s denote:A as the event “Student owns a bicycle.” B as the event “student brings a bicycle to school.”We know:P(A) = 0.60 ( probability that a student owns a bicycle ) P(B|A) = 0.30 ( probability that a student brings a bicycle to school given that they own one ) P(B|-A) = 0.10 ( probability that a student brings a bicycle to school given that they do not own one )We want to find P(A|B), the probability that a student owns a bicycle given that they brought one to school.Bayes’ Theorem states:P(A|B) = P(B|A)xP(A) / P(B)The tricky part is calculating P(B), the probability that a student brings a bicycle to school. We can compute it using the Law of Total probability:P(B) = P(B|A) x P(A) + P(B|-A) x P(-A) Plugging in the known values:P(B) = 0.30 x 0.60 + 0.10 x 0.40 = 0.18 + 0.04 = 0.22Now we can find P(A|B):P(A|B) = 0.30 x 0.60 / 0.22 = 0.82so, if you see a student with a bicycle at school, there’s approximately an 82% chance that they own the bicycle.ALLAH PAK aap ko sahat o aafiyat wali lambi umar ata kray aor ap ko dono jahan ki bhalian naseeb farmaey aur aap ke walid-e-mohtram ko karwat karwat jannat ata farmaye,Ameen.
    Reply
    Shahid Umar 6 months ago
    The new algorithm discussed is Naive Bayes. I learned the definition, formula, and a mathematical example solution of the Naive Bayes Algorithm.
    Reply
    0% Complete