Posts

Showing posts with the label ibm watson tutorial

Featured Post

Top Questions People Ask About Pandas, NumPy, Matplotlib & Scikit-learn — Answered!

Image
 Whether you're a beginner or brushing up on your skills, these are the real-world questions Python learners ask most about key libraries in data science. Let’s dive in! 🐍 🐼 Pandas: Data Manipulation Made Easy 1. How do I handle missing data in a DataFrame? df.fillna( 0 ) # Replace NaNs with 0 df.dropna() # Remove rows with NaNs df.isna(). sum () # Count missing values per column 2. How can I merge or join two DataFrames? pd.merge(df1, df2, on= 'id' , how= 'inner' ) # inner, left, right, outer 3. What is the difference between loc[] and iloc[] ? loc[] uses labels (e.g., column names) iloc[] uses integer positions df.loc[ 0 , 'name' ] # label-based df.iloc[ 0 , 1 ] # index-based 4. How do I group data and perform aggregation? df.groupby( 'category' )[ 'sales' ]. sum () 5. How can I convert a column to datetime format? df[ 'date' ] = pd.to_datetime(df[ 'date' ]) ...

Complete Videos of IBM Watson IoT

Image
Watson IoT is a set of capabilities that learn from, and infuse intelligence into, the physical world. The Internet of Things-generated data is growing twice as fast as social and computer-generated data, and it is extremely varied, noisy, time-sensitive and often confidential. You can learn quickly IBM watson for IoT quickly. Complexity grows as billions of devices interact in a moving world. This presents a growing challenge that will test the limits of programmable computing.  What is Cognitive IoT Cognitive IoT is not explicitly programmed. It learns from experiences with the environment and interactions with people.  It brings true machine learning to systems and processes so they can understand your goals, then integrate and analyze the relevant data to help you achieve them. References IBM Watson IoT videos 5 Challenges in internet of things Follow us on social media Facebook Twitter