6. Python AI Development: Practice

Question : How would you create a scatter plot with Plotly.express?

fig = px.scatter(df, x='...', y='...', color='...') fig.show()

Question : Which command trains the deep learning model?

model.fit_generator(...) Selected

Question : Which of these are benefits of using an Anaconda Distribution?

1. Anaconda provides extensive enterprise support 2. It is easy to manage library dependencies with Anaconda 3. Anaconda is an open source GUI driven tool which supports both Python & R

Question : List the steps needed for pilot distraction prediction in the correct order.

1. Conduct data analysis 2. Understand input data and make sure it is synchronized 3. Create Gradient Boosting model 4. Train the created model on input data 5. Derive feature importance and confusion matrix 6. Tune hyperparameters to improve performance

Question : What is true about deep learning models?

1. Deep learning models perform well in the real world 2. Examples of deep learning models are RNNs, CNNs, and LSTMs Selected. 3. Deep learning models are self-sufficient at extracting features from data

Question : Why is analysis of data before building the model important?

1. Detects data correlation Not selected 2. Removes missing values Selected 3. Understands data distribution

Question : How do we address NaN values in our data?

1. Fill in median value along dataset 2. Fill in mean value along dataset Selected 3. Delete the entries with missing values

Question : List the steps to start developing AI with Python in correct order.

1. Install Anaconda on your machine 2. Create an environment and install all the required packages 3. Activate your AI development Python environment 4. Run jupyter labs command to launch Jupyter Notebooks web application

Question : Which are commonly used machine learning algorithms

1. K-nearest neighbors algorithm 2. Logistic regression 3. Random Forest algorithm

Question : List the steps needed for driver distraction prediction in the correct order.

1. Pre-process data & explore dataset 2. Understand input data and make sure it is synchronized 3. Create CNN model 4. Train the created CNN model on input data 5. Understand the results and collect more data or label more classes 6. Train the model on new data

Question : Which of the following applies to Jupyter Notebook?

Highly extensible development environment which provides easy to share reproducible experiments & visualization

Question : Which applies to Google Colab?

Online development environment which provides free high performance CPU/GPU and pre-installed AI libraries