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
    Zohaib Zeeshan 2 days ago
    done
    Reply
    Rana Anjum Sharif 1 month ago
    Done
    Reply
    nazra kazmi 2 months ago
    df = pd.DataFrame(data)# Define a function to standardize the date format def standardize_date(date_str): try: # Try parsing the date with different formats date_obj = pd.to_datetime(date_str, errors='coerce') return date_obj.strftime("%m/%d/%Y") except ValueError: # If parsing fails, return NaN return pd.NaT# Apply the function to the 'date' column df['date'] = df['date'].apply(standardize_date)print(df)
    Reply
    nazra kazmi 2 months ago
    import pandas as pd# Sample DataFrame data = { 'date': ['2021-12-01', '01-12-2022', 'dec-22-2022', '2021/12/12'], 'country': ['USA', 'UK', 'United States of America', 'UK'], 'name': ['nazra', 'nazra', 'Nazar', 'naz'], 'age': [21, 22, 23, 24], 'city': ['lahore', 'karach', 'lahore', 'lahore'], 'sale': [100, None, 300, 400] }df = pd.DataFrame(data)# Define a function to standardize the date format def standardize_date(date_str): try: # Try parsing the date with different formats date_obj = pd.to_datetime(date_str, errors='coerce') return date_obj.strftime("%m/%d/%Y") except ValueError: # If parsing fails, return NaN return pd.NaT# Apply the function to the 'date' column df['date'] = df['date'].apply(standardize_date)print(df)
    Reply
    nazra kazmi 2 months ago
    import pandas as pd# Sample DataFrame data = { 'date': ['2021-12-01', '01-12-2022', 'dec-22-2022', '2021/12/12'], 'country': ['USA', 'UK', 'United States of America', 'UK'], 'name': ['nazra', 'nazra', 'Nazar', 'naz'], 'age': [21, 22, 23, 24], 'city': ['lahore', 'karach', 'lahore', 'lahore'], 'sale': [100, None, 300, 400] }df = pd.DataFrame(data)# Define a function to standardize the date format def standardize_date(date_str): try: # Try parsing the date with different formats date_obj = pd.to_datetime(date_str, errors='coerce') return date_obj.strftime("%m/%d/%Y") except ValueError: # If parsing fails, return NaN return pd.NaT# Apply the function to the 'date' column df['date'] = df['date'].apply(standardize_date)print(df)date country name age city sale 0 12/01/2021 USA nazra 21 lahore 100.0 1 01/12/2022 UK nazra 22 karach NaN 2 12/22/2022 United States of America Nazar 23 lahore 300.0 3 12/12/2021 UK naz 24 lahore 400.0
    Reply
    Liaqat Ali 4 months ago
    How to remove inconsistencies in big data
    Reply
    Liaqat Ali 4 months ago
    Excellent sir
    Reply
    komal Baloch 5 months ago
    done
    Reply
    junaid amin 5 months ago
    done
    Reply
    tayyab Ali 6 months ago
    I have done this lecture with 100% practice and the assignment is done.
    Reply
    0% Complete