Question: I tried to run the code below, taken from CS50's AI course: import csv import tensorflow as tf from sklearn.model_selection import train_test_split # Read data in from file with open("banknotes.csv") as f: reader = csv.reader(f) next(reader) data = [] for row in reader: data.append( { "evidence": [float(cell) for cell in row[:4]], "label": 1 if row[4] == "0" else 0, } ) # Separate data into training and testing groups evidence = [row["evidence"] for row in data] labels = [row["label"] for row in data] X_training, X_testing, y_training, y_testing = train_test_split( evidence, labels, test_size=0.4 ) # Create a neural network model = tf.keras.models.Sequential() # Add a hidden layer with 8 units, with ReLU activation model.add(tf.keras.layers.Dense(8, input_shape=(4,), activation="relu")) # Add output layer with 1 unit, with sigmoid activation model.add(tf.keras.layers.Dense(1, activation="sigmoid")) # Train neural network model.compile( optimizer="adam", loss="binary_crossentropy", metrics=["accuracy"] ) model.fit(X_training, y_training, epochs=20) # Evaluate how well model performs model.evaluate(X_testing, y_testing, verbose=2)  However, I get the following error: Traceback (most recent call last): File "C:\Users\Eric\Desktop\coding\cs50\ai\lectures\lecture5\banknotes\banknotes.py", line 41, in <module> model.fit(X_training, y_training, epochs=20) File "C:\Users\Eric\Desktop\coding\cs50\ai\.venv\Lib\site-packages\keras\src\utils\traceback_utils.py", line 122, in error_handler raise e.with_traceback(filtered_tb) from None File "C:\Users\Eric\Desktop\coding\cs50\ai\.venv\Lib\site-packages\keras\src\trainers\data_adapters\__init__.py", line 113, in get_data_adapter raise ValueError(f"Unrecognized data type: x={x} (of type {type(x)})") ValueError: Unrecognized data type: x=[...] (of type <class 'list'>)  where "..." is the training data. Any idea what went wrong? I'm using Python version 3.11.8 and TensorFlow version 2.16.1 on a Windows computer. I tried running the same code in a Google Colab notebook, and it works: the problem only occurs on my local machine. This is the output I'm expecting: Epoch 1/20 26/26 [==============================] - 1s 2ms/step - loss: 1.1008 - accuracy: 0.5055 Epoch 2/20 26/26 [==============================] - 0s 2ms/step - loss: 0.8588 - accuracy: 0.5334 Epoch 3/20 26/26 [==============================] - 0s 2ms/step - loss: 0.6946 - accuracy: 0.5917 Epoch 4/20 26/26 [==============================] - 0s 2ms/step - loss: 0.5970 - accuracy: 0.6683 Epoch 5/20 26/26 [==============================] - 0s 2ms/step - loss: 0.5265 - accuracy: 0.7120 Epoch 6/20 26/26 [==============================] - 0s 2ms/step - loss: 0.4717 - accuracy: 0.7655 Epoch 7/20 26/26 [==============================] - 0s 2ms/step - loss: 0.4258 - accuracy: 0.8177 Epoch 8/20 26/26 [==============================] - 0s 2ms/step - loss: 0.3861 - accuracy: 0.8433 Epoch 9/20 26/26 [==============================] - 0s 2ms/step - loss: 0.3521 - accuracy: 0.8615 Epoch 10/20 26/26 [==============================] - 0s 2ms/step - loss: 0.3226 - accuracy: 0.8870 Epoch 11/20 26/26 [==============================] - 0s 2ms/step - loss: 0.2960 - accuracy: 0.9028 Epoch 12/20 26/26 [==============================] - 0s 2ms/step - loss: 0.2722 - accuracy: 0.9125 Epoch 13/20 26/26 [==============================] - 0s 2ms/step - loss: 0.2506 - accuracy: 0.9283 Epoch 14/20 26/26 [==============================] - 0s 2ms/step - loss: 0.2306 - accuracy: 0.9514 Epoch 15/20 26/26 [==============================] - 0s 3ms/step - loss: 0.2124 - accuracy: 0.9660 Epoch 16/20 26/26 [==============================] - 0s 2ms/step - loss: 0.1961 - accuracy: 0.9769 Epoch 17/20 26/26 [==============================] - 0s 2ms/step - loss: 0.1813 - accuracy: 0.9781 Epoch 18/20 26/26 [==============================] - 0s 2ms/step - loss: 0.1681 - accuracy: 0.9793 Epoch 19/20 26/26 [==============================] - 0s 2ms/step - loss: 0.1562 - accuracy: 0.9793 Epoch 20/20 26/26 [==============================] - 0s 2ms/step - loss: 0.1452 - accuracy: 0.9830 18/18 - 0s - loss: 0.1407 - accuracy: 0.9891 - 187ms/epoch - 10ms/step [0.14066053926944733, 0.9890710115432739] 

Answer: https://www.tensorflow.org/api_docs/python/tf/keras/Model#fit It appears you're giving Model.fit([X], [y]) the wrong type. What I almost always do before handing off data to train_test_split is converting my features and labels to np arrays. So you can either convert them before handing them off to train_test_split or do it before the model.fit(...) NOTE: Don't forget to add import numpy as np So in your case you'd do: X_training_np = np.array(X_training) y_training_np = np.array(y_training) model.fit(X_training_np, y_training_np, epochs=...)