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 1 month ago
    Done
    Reply
    Mr. Arshad 5 months ago
    i understand thank jazakumulah kharn
    Reply
    Mr. Arshad 5 months ago
    from sklearn.preprocessing import Normalizer data = [[1, 1, 1], [1, 1, 0], [1, 0, 0]] normalizer = Normalizer(norm='12') print(normalizer.fit_transform(data)) showing error plz help meFile c:UsersdeLLminiconda3envspython_mlLibsite-packagessklearnutils_set_output.py:157, in _wrap_method_output..wrapped(self, X, *args, **kwargs) 155 @wraps(f) 156 def wrapped(self, X, *args, **kwargs): --> 157 data_to_wrap = f(self, X, *args, **kwargs) 158 if isinstance(data_to_wrap, tuple): 159 # only wrap the first output for cross decomposition 160 return_tuple = ( 161 _wrap_data_with_container(method, data_to_wrap[0], X, self), 162 *data_to_wrap[1:], 163 )File c:UsersdeLLminiconda3envspython_mlLibsite-packagessklearnbase.py:916, in TransformerMixin.fit_transform(self, X, y, **fit_params) 912 # non-optimized default implementation; override when a better 913 # method is possible for a given clustering algorithm 914 if y is None: 915 # fit method of arity 1 (unsupervised transformation) --> 916 return self.fit(X, **fit_params).transform(X) 917 else:
    Reply
    tayyab Ali 6 months ago
    I have done this lecture with 100% practice.
    Reply
    Sibtain Ali 6 months ago
    I have learned L1 and L2 with 100% practice.
    Reply
    Shahid Umar 6 months ago
    L1 is used for the sum of the absolute values is 1 in each row. L2 is used for text data normalization.
    Reply
    0% Complete