diff --git a/src/CharRNN/index.js b/src/CharRNN/index.js
index d4b26a913..af65e235d 100644
--- a/src/CharRNN/index.js
+++ b/src/CharRNN/index.js
@@ -11,7 +11,7 @@ A LSTM Generator: Run inference mode for a pre-trained LSTM.
 
 import * as tf from "@tensorflow/tfjs";
 import axios from "axios";
-import sampleFromDistribution from "./../utils/sample";
+import sampleFromDistribution from "../utils/sample";
 import CheckpointLoader from "../utils/checkpointLoader";
 import callCallback from "../utils/callcallback";
 
@@ -309,6 +309,6 @@ class CharRNN {
   }
 }
 
-const charRNN = (modelPath = "./", callback) => new CharRNN(modelPath, callback);
+const charRNN = (modelPath, callback) => new CharRNN(modelPath, callback);
 
 export default charRNN;
diff --git a/src/DBSCAN/index.js b/src/DBSCAN/index.js
index af99840cc..d24d73ead 100644
--- a/src/DBSCAN/index.js
+++ b/src/DBSCAN/index.js
@@ -33,9 +33,7 @@ async function loadDataset(inputData) {
   } else {
     data = inputData;
   }
-  const dataFlat = data.map(d => {
-    return Object.values(d);
-  });
+  const dataFlat = data.map(d => Object.values(d));
   return dataFlat;
 }
 
@@ -138,6 +136,7 @@ class DBSCAN {
   getClusterId() {
     return this.lastClusterId;
   }
+
   /**
    * increment cluster id
    */
@@ -159,9 +158,7 @@ class DBSCAN {
         return tf
           .stack([values.asType("float32"), indices.asType("float32")], 1)
           .arraySync()
-          .filter(v => {
-            return v[0] <= this.config.eps;
-          })
+          .filter(v => v[0] <= this.config.eps)
           .reduce((prev, cur) => {
             prev.push(cur[1]);
             return prev;
diff --git a/src/FaceApi/index.js b/src/FaceApi/index.js
index f6a69e373..e1fdc0907 100644
--- a/src/FaceApi/index.js
+++ b/src/FaceApi/index.js
@@ -268,7 +268,7 @@ class FaceApiBase {
    * @param {Object} faceApiOptions
    */
   setReturnOptions(faceApiOptions) {
-    const output = Object.assign({}, this.config);
+    const output = { ...this.config};
     const options = ["withLandmarks", "withDescriptors"];
 
     options.forEach(prop => {
@@ -306,7 +306,7 @@ class FaceApiBase {
     if (Array.isArray(result) === true) {
       output = result.map(item => {
         // if landmarks exist return parts
-        const newItem = Object.assign({}, item);
+        const newItem = { ...item};
         if (newItem.landmarks) {
           const { landmarks } = newItem;
           newItem.parts = {
@@ -333,7 +333,7 @@ class FaceApiBase {
       });
       // single detection is an object
     } else {
-      output = Object.assign({}, result);
+      output = { ...result};
       if (output.landmarks) {
         const { landmarks } = result;
         output.parts = {
diff --git a/src/FeatureExtractor/Mobilenet.js b/src/FeatureExtractor/Mobilenet.js
index f4ccabdd5..a1907201a 100644
--- a/src/FeatureExtractor/Mobilenet.js
+++ b/src/FeatureExtractor/Mobilenet.js
@@ -10,7 +10,7 @@ A class that extract features from Mobilenet
 import * as tf from "@tensorflow/tfjs";
 import axios from "axios";
 import handleArguments from "../utils/handleArguments";
-import Video from "./../utils/Video";
+import Video from "../utils/Video";
 import { imgToTensor } from "../utils/imageUtilities";
 import { saveBlob } from "../utils/io";
 import callCallback from "../utils/callcallback";
@@ -127,7 +127,7 @@ class Mobilenet {
    *    the video is ready. If no callback is provided, it will return a
    *    promise that will be resolved once the video element has loaded.
    */
-  classification(video, objOrCallback = null, callback) {
+  classification(video, objOrCallback, callback) {
     const { options, callback: cb } = handleArguments(objOrCallback, callback);
 
     this.usageType = "classifier";
@@ -367,7 +367,7 @@ class Mobilenet {
     return { value: prediction[0] };
   }
 
-  async load(filesOrPath = null, callback) {
+  async load(filesOrPath, callback) {
     if (typeof filesOrPath !== "string") {
       let model = null;
       let weights = null;
diff --git a/src/KMeans/index.js b/src/KMeans/index.js
index 959f0d736..bdc0e040a 100644
--- a/src/KMeans/index.js
+++ b/src/KMeans/index.js
@@ -42,9 +42,7 @@ async function loadDataset(inputData) {
   } else {
     data = inputData;
   }
-  const dataFlat = data.map((d) => {
-    return Object.values(d)
-  });
+  const dataFlat = data.map(d => Object.values(d));
   return dataFlat;
 }
 
@@ -158,7 +156,7 @@ class KMeans {
         const centroidKTensor = centroidK.map(d => d.tensor);
         if (centroidKTensor.length === 0) {
           return centroid;
-        } else if (centroidKTensor.length === 1) {
+        } if (centroidKTensor.length === 1) {
           return centroidKTensor[0];
         }
         // grab mean for for cluster
@@ -186,4 +184,4 @@ class KMeans {
 
 const kmeans = (dataset, options, callback) => new KMeans(dataset, options, callback);
 
-export default kmeans;
\ No newline at end of file
+export default kmeans;
diff --git a/src/NeuralNetwork/NeuralNetwork.js b/src/NeuralNetwork/NeuralNetwork.js
index 078d9b965..c23eb6b79 100644
--- a/src/NeuralNetwork/NeuralNetwork.js
+++ b/src/NeuralNetwork/NeuralNetwork.js
@@ -129,9 +129,7 @@ class NeuralNetwork {
    * @param {*} _inputs
    */
   predictSync(_inputs) {
-    const output = tf.tidy(() => {
-      return this.model.predict(_inputs);
-    });
+    const output = tf.tidy(() => this.model.predict(_inputs));
     const result = output.arraySync();
 
     output.dispose();
@@ -145,9 +143,7 @@ class NeuralNetwork {
    * @param {*} _inputs
    */
   async predict(_inputs) {
-    const output = tf.tidy(() => {
-      return this.model.predict(_inputs);
-    });
+    const output = tf.tidy(() => this.model.predict(_inputs));
     const result = await output.array();
 
     output.dispose();
@@ -211,16 +207,16 @@ class NeuralNetwork {
    * @param {*} filesOrPath
    * @param {*} callback
    */
-  async load(filesOrPath = null, callback) {
+  async load(filesOrPath, callback) {
     if (filesOrPath instanceof FileList) {
       const files = await Promise.all(
         Array.from(filesOrPath).map(async file => {
           if (file.name.includes('.json') && !file.name.includes('_meta')) {
             return { name: 'model', file };
-          } else if (file.name.includes('.json') && file.name.includes('_meta.json')) {
+          } if (file.name.includes('.json') && file.name.includes('_meta.json')) {
             const modelMetadata = await file.text();
             return { name: 'metadata', file: modelMetadata };
-          } else if (file.name.includes('.bin')) {
+          } if (file.name.includes('.bin')) {
             return { name: 'weights', file };
           }
           return { name: null, file: null };
diff --git a/src/NeuralNetwork/NeuralNetworkData.js b/src/NeuralNetwork/NeuralNetworkData.js
index 84aaf279b..acabb54d4 100644
--- a/src/NeuralNetwork/NeuralNetworkData.js
+++ b/src/NeuralNetwork/NeuralNetworkData.js
@@ -100,7 +100,7 @@ class NeuralNetworkData {
    * @param {*} dataRaw
    */
   getDataStats(dataRaw) {
-    const meta = Object.assign({}, this.meta);
+    const meta = { ...this.meta};
 
     const inputMeta = this.getInputMetaStats(dataRaw, meta.inputs, 'xs');
     const outputMeta = this.getInputMetaStats(dataRaw, meta.outputs, 'ys');
@@ -125,7 +125,7 @@ class NeuralNetworkData {
    */
   // eslint-disable-next-line no-unused-vars, class-methods-use-this
   getInputMetaStats(dataRaw, inputOrOutputMeta, xsOrYs) {
-    const inputMeta = Object.assign({}, inputOrOutputMeta);
+    const inputMeta = { ...inputOrOutputMeta};
 
     Object.keys(inputMeta).forEach(k => {
       if (inputMeta[k].dtype === 'string') {
@@ -151,7 +151,7 @@ class NeuralNetworkData {
    */
   getDataUnits(dataRaw, _arrayShape = null) {
     const arrayShape = _arrayShape !== null ? _arrayShape : undefined;
-    const meta = Object.assign({}, this.meta);
+    const meta = { ...this.meta};
 
     // if the data has a shape pass it in
     let inputShape;
@@ -182,7 +182,7 @@ class NeuralNetworkData {
   // eslint-disable-next-line class-methods-use-this, no-unused-vars
   getInputMetaUnits(_dataRaw, _inputsMeta) {
     let units = 0;
-    const inputsMeta = Object.assign({}, _inputsMeta);
+    const inputsMeta = { ..._inputsMeta};
 
     Object.entries(inputsMeta).forEach(arr => {
       const { dtype } = arr[1];
@@ -271,7 +271,7 @@ class NeuralNetworkData {
    */
   // eslint-disable-next-line class-methods-use-this, no-unused-vars
   convertRawToTensors(dataRaw) {
-    const meta = Object.assign({}, this.meta);
+    const meta = { ...this.meta};
     const dataLength = dataRaw.length;
 
     return tf.tidy(() => {
@@ -281,18 +281,14 @@ class NeuralNetworkData {
       dataRaw.forEach(row => {
         // get xs
         const xs = Object.keys(meta.inputs)
-          .map(k => {
-            return row.xs[k];
-          })
+          .map(k => row.xs[k])
           .flat();
 
         inputArr.push(xs);
 
         // get ys
         const ys = Object.keys(meta.outputs)
-          .map(k => {
-            return row.ys[k];
-          })
+          .map(k => row.ys[k])
           .flat();
 
         outputArr.push(ys);
@@ -319,7 +315,7 @@ class NeuralNetworkData {
    * @param {*} dataRaw
    */
   normalizeDataRaw(dataRaw) {
-    const meta = Object.assign({}, this.meta);
+    const meta = { ...this.meta};
 
     const normXs = this.normalizeInputData(dataRaw, meta.inputs, 'xs');
     const normYs = this.normalizeInputData(dataRaw, meta.outputs, 'ys');
@@ -340,7 +336,7 @@ class NeuralNetworkData {
     // the data length
     const dataLength = dataRaw.length;
     // the copy of the inputs.meta[inputOrOutput]
-    const inputMeta = Object.assign({}, inputOrOutputMeta);
+    const inputMeta = { ...inputOrOutputMeta};
 
     // normalized output object
     const normalized = {};
@@ -392,9 +388,7 @@ class NeuralNetworkData {
     // value with the onehot array
     // if none exists, return the given value
     if (options.legend) {
-      const normalized = inputArray.map(v => {
-        return options.legend[v] ? options.legend[v] : v;
-      });
+      const normalized = inputArray.map(v => options.legend[v] ? options.legend[v] : v);
       return normalized;
     }
 
@@ -460,7 +454,7 @@ class NeuralNetworkData {
    * @param {*} _meta
    */
   applyOneHotEncodingsToDataRaw(dataRaw) {
-    const meta = Object.assign({}, this.meta);
+    const meta = { ...this.meta};
 
     const output = dataRaw.map(row => {
       const xs = {
@@ -498,7 +492,7 @@ class NeuralNetworkData {
    * @param {*} dataRaw
    */
   getDataOneHot(dataRaw) {
-    const meta = Object.assign({}, this.meta);
+    const meta = { ...this.meta};
 
     const inputMeta = this.getInputMetaOneHot(dataRaw, meta.inputs, 'xs');
     const outputMeta = this.getInputMetaOneHot(dataRaw, meta.outputs, 'ys');
@@ -521,7 +515,7 @@ class NeuralNetworkData {
    * @param {*} xsOrYs
    */
   getInputMetaOneHot(_dataRaw, _inputsMeta, xsOrYs) {
-    const inputsMeta = Object.assign({}, _inputsMeta);
+    const inputsMeta = { ..._inputsMeta};
 
     Object.entries(inputsMeta).forEach(arr => {
       // the key
@@ -615,7 +609,7 @@ class NeuralNetworkData {
       let json;
       // handle loading parsedJson
       if (dataUrlOrJson instanceof Object) {
-        json = Object.assign({}, dataUrlOrJson);
+        json = { ...dataUrlOrJson};
       } else {
         const {data} = await axios.get(dataUrlOrJson);
         json = data;
@@ -684,7 +678,7 @@ class NeuralNetworkData {
    * @param {*} filesOrPath
    * @param {*} callback
    */
-  async loadData(filesOrPath = null, callback) {
+  async loadData(filesOrPath, callback) {
     try {
       let loadedData;
 
@@ -769,7 +763,7 @@ class NeuralNetworkData {
    * @param {*} filesOrPath
    * @param {*} callback
    */
-  async loadMeta(filesOrPath = null, callback) {
+  async loadMeta(filesOrPath, callback) {
     if (filesOrPath instanceof FileList) {
       const files = await Promise.all(
         Array.from(filesOrPath).map(async file => {
@@ -778,13 +772,13 @@ class NeuralNetworkData {
               name: 'model',
               file,
             };
-          } else if (file.name.includes('.json') && file.name.includes('_meta.json')) {
+          } if (file.name.includes('.json') && file.name.includes('_meta.json')) {
             const modelMetadata = await file.text();
             return {
               name: 'metadata',
               file: modelMetadata,
             };
-          } else if (file.name.includes('.bin')) {
+          } if (file.name.includes('.bin')) {
             return {
               name: 'weights',
               file,
@@ -925,11 +919,11 @@ class NeuralNetworkData {
    * @param {*} _data
    */
   findEntries(_data) {
-    const parentCopy = Object.assign({}, _data);
+    const parentCopy = { ..._data};
 
     if (parentCopy.entries && parentCopy.entries instanceof Array) {
       return parentCopy.entries;
-    } else if (parentCopy.data && parentCopy.data instanceof Array) {
+    } if (parentCopy.data && parentCopy.data instanceof Array) {
       return parentCopy.data;
     }
 
diff --git a/src/NeuralNetwork/NeuralNetworkUtils.js b/src/NeuralNetwork/NeuralNetworkUtils.js
index 870a4a12e..1c9a9b6ab 100644
--- a/src/NeuralNetwork/NeuralNetworkUtils.js
+++ b/src/NeuralNetwork/NeuralNetworkUtils.js
@@ -32,9 +32,7 @@ class NeuralNetworkUtils {
   // eslint-disable-next-line no-unused-vars, class-methods-use-this
   getMin(_array) {
     // return Math.min(..._array)
-    return _array.reduce((a, b) => {
-      return Math.min(a, b);
-    });
+    return _array.reduce((a, b) => Math.min(a, b));
   }
 
   /**
@@ -43,9 +41,7 @@ class NeuralNetworkUtils {
    */
   // eslint-disable-next-line no-unused-vars, class-methods-use-this
   getMax(_array) {
-    return _array.reduce((a, b) => {
-      return Math.max(a, b);
-    });
+    return _array.reduce((a, b) => Math.max(a, b));
     // return Math.max(..._array)
   }
 
@@ -75,12 +71,10 @@ class NeuralNetworkUtils {
       return [];
     }
 
-    const output = [...new Array(arr1.length).fill(null)].map((item, idx) => {
-      return {
+    const output = [...new Array(arr1.length).fill(null)].map((item, idx) => ({
         ...arr1[idx],
         ...arr2[idx]
-      }
-    })
+      }))
 
     return output;
   }
@@ -116,7 +110,7 @@ class NeuralNetworkUtils {
         result[label] = item;
       });
       return result;
-    } else if (typeof incoming === 'object') {
+    } if (typeof incoming === 'object') {
       result = incoming;
       return result;
     }
@@ -148,4 +142,4 @@ const neuralNetworkUtils = () => {
   return instance;
 }
 
-export default neuralNetworkUtils();
\ No newline at end of file
+export default neuralNetworkUtils();
diff --git a/src/NeuralNetwork/NeuralNetworkVis.js b/src/NeuralNetwork/NeuralNetworkVis.js
index 1d15c55c4..41bd00bbd 100644
--- a/src/NeuralNetwork/NeuralNetworkVis.js
+++ b/src/NeuralNetwork/NeuralNetworkVis.js
@@ -23,12 +23,10 @@ class NeuralNetworkVis {
    * @param {*} data
    */
   scatterplot(inputLabel, outputLabel, data) {
-    const values = data.map(item => {
-      return {
+    const values = data.map(item => ({
         x: item.xs[inputLabel],
         y: item.ys[outputLabel],
-      };
-    });
+      }));
 
     const visOptions = {
       name: "debug mode",
@@ -53,12 +51,10 @@ class NeuralNetworkVis {
 
     inputLabels.forEach(inputLabel => {
       outputLabels.forEach(outputLabel => {
-        const val = data.map(item => {
-          return {
+        const val = data.map(item => ({
             x: item.xs[inputLabel],
             y: item.ys[outputLabel],
-          };
-        });
+          }));
 
         values = [...values, ...val];
       });
@@ -84,12 +80,10 @@ class NeuralNetworkVis {
    * @param {*} data
    */
   barchart(inputLabel, outputLabel, data) {
-    const values = data.map(item => {
-      return {
+    const values = data.map(item => ({
         value: item.xs[inputLabel],
         index: item.ys[outputLabel],
-      };
-    });
+      }));
 
     const chartOptions = {
       xLabel: "label",
diff --git a/src/NeuralNetwork/index.js b/src/NeuralNetwork/index.js
index 1a5fcf003..5991a01d9 100644
--- a/src/NeuralNetwork/index.js
+++ b/src/NeuralNetwork/index.js
@@ -330,7 +330,7 @@ class DiyNeuralNetwork {
     if (Array.isArray(input)) {
       formattedInputs = input.map(item => this.formatInputItem(item));
     } else if (typeof input === 'object') {
-      const newXInputs = Object.assign({}, input);
+      const newXInputs = { ...input};
       Object.keys(input).forEach(k => {
         const val = input[k];
         newXInputs[k] = this.formatInputItem(val);
@@ -393,14 +393,10 @@ class DiyNeuralNetwork {
     // to run predict or classify on a batch of data
 
     if (_input instanceof Array) {
-      inputData = inputHeaders.map((prop, idx) => {
-        return this.isOneHotEncodedOrNormalized(_input[idx], prop, meta.inputs);
-      });
+      inputData = inputHeaders.map((prop, idx) => this.isOneHotEncodedOrNormalized(_input[idx], prop, meta.inputs));
     } else if (_input instanceof Object) {
       // TODO: make sure that the input order is preserved!
-      inputData = inputHeaders.map(prop => {
-        return this.isOneHotEncodedOrNormalized(_input[prop], prop, meta.inputs);
-      });
+      inputData = inputHeaders.map(prop => this.isOneHotEncodedOrNormalized(_input[prop], prop, meta.inputs));
     }
 
     // inputData = tf.tensor([inputData.flat()])
@@ -420,9 +416,7 @@ class DiyNeuralNetwork {
 
     if (_input instanceof Array) {
       if (_input.every(item => Array.isArray(item))) {
-        output = _input.map(item => {
-          return this.formatInputsForPrediction(item, meta, inputHeaders);
-        });
+        output = _input.map(item => this.formatInputsForPrediction(item, meta, inputHeaders));
 
         return tf.tensor(output, [_input.length, inputHeaders.length]);
       }
@@ -601,7 +595,7 @@ class DiyNeuralNetwork {
   createNetworkLayers(layerJsonArray, meta) {
     const layers = [...layerJsonArray];
 
-    const { inputUnits, outputUnits } = Object.assign({}, meta);
+    const { inputUnits, outputUnits } = { ...meta};
     const layersLength = layers.length;
 
     if (!(layers.length >= 2)) {
@@ -869,8 +863,7 @@ class DiyNeuralNetwork {
     if (meta !== null) {
       const labels = Object.keys(meta.outputs);
 
-      const formattedResults = unformattedResults.map(unformattedResult => {
-        return labels.map((item, idx) => {
+      const formattedResults = unformattedResults.map(unformattedResult => labels.map((item, idx) => {
           // check to see if the data were normalized
           // if not, then send back the values, otherwise
           // unnormalize then return
@@ -897,8 +890,7 @@ class DiyNeuralNetwork {
           }
 
           return d;
-        });
-      });
+        }));
 
       // return single array if the length is less than 2,
       // otherwise return array of arrays
@@ -929,8 +921,7 @@ class DiyNeuralNetwork {
     if (meta !== null) {
       const labels = Object.keys(meta.outputs);
 
-      const formattedResults = unformattedResults.map(unformattedResult => {
-        return labels.map((item, idx) => {
+      const formattedResults = unformattedResults.map(unformattedResult => labels.map((item, idx) => {
           // check to see if the data were normalized
           // if not, then send back the values, otherwise
           // unnormalize then return
@@ -957,8 +948,7 @@ class DiyNeuralNetwork {
           }
 
           return d;
-        });
-      });
+        }));
 
       // return single array if the length is less than 2,
       // otherwise return array of arrays
@@ -1014,17 +1004,13 @@ class DiyNeuralNetwork {
       const label = Object.keys(meta.outputs)[0];
       const vals = Object.entries(meta.outputs[label].legend);
 
-      const formattedResults = unformattedResults.map(unformattedResult => {
-        return vals
-          .map((item, idx) => {
-            return {
+      const formattedResults = unformattedResults.map(unformattedResult => vals
+          .map((item, idx) => ({
               [item[0]]: unformattedResult[idx],
               label: item[0],
               confidence: unformattedResult[idx],
-            };
-          })
-          .sort((a, b) => b.confidence - a.confidence);
-      });
+            }))
+          .sort((a, b) => b.confidence - a.confidence));
 
       // return single array if the length is less than 2,
       // otherwise return array of arrays
@@ -1079,17 +1065,13 @@ class DiyNeuralNetwork {
       const label = Object.keys(meta.outputs)[0];
       const vals = Object.entries(meta.outputs[label].legend);
 
-      const formattedResults = unformattedResults.map(unformattedResult => {
-        return vals
-          .map((item, idx) => {
-            return {
+      const formattedResults = unformattedResults.map(unformattedResult => vals
+          .map((item, idx) => ({
               [item[0]]: unformattedResult[idx],
               label: item[0],
               confidence: unformattedResult[idx],
-            };
-          })
-          .sort((a, b) => b.confidence - a.confidence);
-      });
+            }))
+          .sort((a, b) => b.confidence - a.confidence));
 
       // return single array if the length is less than 2,
       // otherwise return array of arrays
@@ -1121,7 +1103,7 @@ class DiyNeuralNetwork {
    * @param {*} filesOrPath
    * @param {*} callback
    */
-  async loadData(filesOrPath = null, callback) {
+  async loadData(filesOrPath, callback) {
     this.neuralNetworkData.loadData(filesOrPath, callback);
   }
 
@@ -1151,7 +1133,7 @@ class DiyNeuralNetwork {
    * @param {*} filesOrPath
    * @param {*} callback
    */
-  async load(filesOrPath = null, cb) {
+  async load(filesOrPath, cb) {
     let callback;
     if (cb) {
       callback = cb;
diff --git a/src/NeuralNetwork/index.test.js b/src/NeuralNetwork/index.test.js
index 18b42de73..325c56bdd 100644
--- a/src/NeuralNetwork/index.test.js
+++ b/src/NeuralNetwork/index.test.js
@@ -335,9 +335,7 @@ describe('NeuralNetwork', () => {
           epochs: 2,
           shuffle: true,
           validationSplit: 0.2,
-          whileTraining: () => {
-            return null;
-          },
+          whileTraining: () => null,
         };
 
         await brain.trainInternal(trainingOptions);
diff --git a/src/ObjectDetector/CocoSsd/index.js b/src/ObjectDetector/CocoSsd/index.js
index 236a65295..7652e3a9d 100644
--- a/src/ObjectDetector/CocoSsd/index.js
+++ b/src/ObjectDetector/CocoSsd/index.js
@@ -72,8 +72,7 @@ export class CocoSsdBase {
   async detectInternal(imgToPredict) {
     this.isPredicting = true;
     const predictions = await this.model.detect(imgToPredict);
-    const formattedPredictions = predictions.map(prediction => {
-      return {
+    const formattedPredictions = predictions.map(prediction => ({
         label: prediction.class,
         confidence: prediction.score,
         x: prediction.bbox[0],
@@ -86,8 +85,7 @@ export class CocoSsdBase {
           width: prediction.bbox[2] / imgToPredict.width,
           height: prediction.bbox[3] / imgToPredict.height,
         },
-      };
-    });
+      }));
     this.isPredicting = false;
     return formattedPredictions;
   }
diff --git a/src/ObjectDetector/YOLO/index.js b/src/ObjectDetector/YOLO/index.js
index cf15537cd..624ed6d1a 100644
--- a/src/ObjectDetector/YOLO/index.js
+++ b/src/ObjectDetector/YOLO/index.js
@@ -11,12 +11,11 @@ Heavily derived from https://github.com/ModelDepot/tfjs-yolo-tiny (ModelDepot: m
 
 import * as tf from '@tensorflow/tfjs';
 import handleArguments from "../../utils/handleArguments";
-import Video from './../../utils/Video';
-import { imgToTensor } from "./../../utils/imageUtilities";
-import callCallback from './../../utils/callcallback';
-import CLASS_NAMES from './../../utils/COCO_CLASSES';
-import modelLoader from './../../utils/modelLoader';
-
+import Video from '../../utils/Video';
+import { imgToTensor } from "../../utils/imageUtilities";
+import callCallback from "../../utils/callcallback";
+import CLASS_NAMES from "../../utils/COCO_CLASSES";
+import modelLoader from "../../utils/modelLoader";
 import {
   nonMaxSuppression,
   boxesToCorners,
diff --git a/src/ObjectDetector/YOLO/postprocess.js b/src/ObjectDetector/YOLO/postprocess.js
index 4b05c4e99..a55279592 100644
--- a/src/ObjectDetector/YOLO/postprocess.js
+++ b/src/ObjectDetector/YOLO/postprocess.js
@@ -77,8 +77,7 @@ export async function filterBoxes(
   })
 }
 
-export const boxesToCorners = (boxXY, boxWH) => {
-  return tf.tidy(() => {
+export const boxesToCorners = (boxXY, boxWH) => tf.tidy(() => {
     const two = tf.tensor1d([2.0]);
     const boxMins = tf.sub(boxXY, tf.div(boxWH, two));
     const boxMaxes = tf.add(boxXY, tf.div(boxWH, two));
@@ -94,11 +93,9 @@ export const boxesToCorners = (boxXY, boxWH) => {
       boxMaxes.slice([0, 0, 0, 1], size),
       boxMaxes.slice([0, 0, 0, 0], size),
     ], 3);
-  })
-};
+  });
 
-export const nonMaxSuppression = (boxes, scores, iouThreshold) => {
-  return tf.tidy(() => {
+export const nonMaxSuppression = (boxes, scores, iouThreshold) => tf.tidy(() => {
     // Zip together scores, box corners, and index
     const zipped = [];
     for (let i = 0; i < scores.length; i += 1) {
@@ -129,8 +126,7 @@ export const nonMaxSuppression = (boxes, scores, iouThreshold) => {
       selectedBoxes.map(e => e[1]),
       selectedBoxes.map(e => e[0]),
     ];
-  })
-};
+  });
 
 // Convert yolo output to bounding box + prob tensors
 /* eslint no-param-reassign: 0 */
@@ -170,4 +166,4 @@ export function head(feats, anchors, numClasses) {
 
     return [boxXY, boxWH, boxConfidence, boxClassProbs];
   })
-}
\ No newline at end of file
+}
diff --git a/src/ObjectDetector/index.test.js b/src/ObjectDetector/index.test.js
index e9549585d..f69f0a768 100644
--- a/src/ObjectDetector/index.test.js
+++ b/src/ObjectDetector/index.test.js
@@ -25,15 +25,11 @@ const mockYoloObject = {
   classProbThreshold: YOLO_DEFAULTS.classProbThreshold,
   filterBoxesThreshold: YOLO_DEFAULTS.filterBoxesThreshold,
   size: YOLO_DEFAULTS.size,
-  detect: () => {
-    return [{ label: "bird", confidence: 0.9 }];
-  },
+  detect: () => [{ label: "bird", confidence: 0.9 }],
 };
 const mockCocoObject = {
   config: { ...COCOSSD_DEFAULTS },
-  detect: () => {
-    return [{ label: "bird", confidence: 0.9 }];
-  },
+  detect: () => [{ label: "bird", confidence: 0.9 }],
 };
 
 function mockObjectDetector(modelName) {
diff --git a/src/Pix2pix/index.js b/src/Pix2pix/index.js
index b1c32faf0..b6f68e7f3 100644
--- a/src/Pix2pix/index.js
+++ b/src/Pix2pix/index.js
@@ -113,17 +113,13 @@ class Pix2pix {
   }
 
   static preprocess(inputPreproc) {
-    const result = tf.tidy(() => {
-      return tf.sub(tf.mul(inputPreproc, tf.scalar(2)), tf.scalar(1));
-    });
+    const result = tf.tidy(() => tf.sub(tf.mul(inputPreproc, tf.scalar(2)), tf.scalar(1)));
     inputPreproc.dispose();
     return result;
   }
 
   static deprocess(inputDeproc) {
-    const result = tf.tidy(() => {
-      return tf.div(tf.add(inputDeproc, tf.scalar(1)), tf.scalar(2));
-    });
+    const result = tf.tidy(() => tf.div(tf.add(inputDeproc, tf.scalar(1)), tf.scalar(2)));
     inputDeproc.dispose();
     return result;
   }
@@ -140,9 +136,7 @@ class Pix2pix {
 
   static conv2d(inputCon, filterCon) {
     const tempFilter = filterCon.clone()
-    const result = tf.tidy(() => {
-      return tf.conv2d(inputCon, tempFilter, [2, 2], 'same');
-    });
+    const result = tf.tidy(() => tf.conv2d(inputCon, tempFilter, [2, 2], 'same'));
     inputCon.dispose();
     tempFilter.dispose();
     return result;
@@ -166,4 +160,4 @@ const pix2pix = (model, callback) => {
   return callback ? instance : instance.ready;
 };
 
-export default pix2pix;
\ No newline at end of file
+export default pix2pix;
diff --git a/src/SoundClassifier/index.js b/src/SoundClassifier/index.js
index 746379e7e..31d7d1668 100644
--- a/src/SoundClassifier/index.js
+++ b/src/SoundClassifier/index.js
@@ -65,7 +65,7 @@ class SoundClassifier {
    * @param {function} cb - a callback function that handles the results of the function.
    * @return {function} a promise or the results of a given callback, cb.
    */
-  async classify(numOrCallback = null, cb) {
+  async classify(numOrCallback, cb) {
     const args = handleArguments(numOrCallback, cb);
     const numberOfClasses = args.number || this.topk;
 
diff --git a/src/SoundClassifier/speechcommands.js b/src/SoundClassifier/speechcommands.js
index 5b4ed327a..8d8cabb5e 100644
--- a/src/SoundClassifier/speechcommands.js
+++ b/src/SoundClassifier/speechcommands.js
@@ -21,6 +21,7 @@ export class SpeechCommands {
     this.allLabels = this.model.wordLabels();
   }
 
+  // eslint-disable-next-line default-param-last
   classify(topk = this.allLabels.length, cb) {
     return this.model.listen(result => {
       if (result.scores) {
@@ -30,9 +31,7 @@ export class SpeechCommands {
       }
       return cb(`ERROR: Cannot find scores in result: ${result}`);
     }, this.options)
-      .catch(err => {
-        return cb(`ERROR: ${err.message}`);
-      });
+      .catch(err => cb(`ERROR: ${err.message}`));
   }
 }
 
diff --git a/src/StyleTransfer/index.js b/src/StyleTransfer/index.js
index a38b9bd4a..f6ac4f950 100644
--- a/src/StyleTransfer/index.js
+++ b/src/StyleTransfer/index.js
@@ -13,7 +13,7 @@ The original TensorFlow implementation was developed by Logan Engstrom: github.c
 
 import * as tf from '@tensorflow/tfjs';
 import handleArguments from "../utils/handleArguments";
-import Video from './../utils/Video';
+import Video from '../utils/Video';
 import CheckpointLoader from '../utils/checkpointLoader';
 import { array3DToImage } from '../utils/imageUtilities';
 import callCallback from '../utils/callcallback';
diff --git a/src/index.js b/src/index.js
index 2735a9c81..acfdb4b5c 100644
--- a/src/index.js
+++ b/src/index.js
@@ -5,20 +5,20 @@
 
 import * as tf from "@tensorflow/tfjs";
 import * as tfvis from "@tensorflow/tfjs-vis";
-import pitchDetection from "./PitchDetection/";
-import imageClassifier from "./ImageClassifier/";
-import soundClassifier from "./SoundClassifier/";
-import KNNClassifier from "./KNNClassifier/";
-import featureExtractor from "./FeatureExtractor/";
-import word2vec from "./Word2vec/";
+import pitchDetection from "./PitchDetection";
+import imageClassifier from "./ImageClassifier";
+import soundClassifier from "./SoundClassifier";
+import KNNClassifier from "./KNNClassifier";
+import featureExtractor from "./FeatureExtractor";
+import word2vec from "./Word2vec";
 import {YOLO} from "./ObjectDetector/YOLO";
 import {CocoSsd} from "./ObjectDetector/CocoSsd";
 import objectDetector from "./ObjectDetector";
 import poseNet from "./PoseNet";
 import * as imageUtils from "./utils/imageUtilities";
-import styleTransfer from "./StyleTransfer/";
-import charRNN from "./CharRNN/";
-import pix2pix from "./Pix2pix/";
+import styleTransfer from "./StyleTransfer";
+import charRNN from "./CharRNN";
+import pix2pix from "./Pix2pix";
 import sketchRNN from "./SketchRNN";
 import uNet from "./UNET";
 import CVAE from "./CVAE";
@@ -72,7 +72,9 @@ const withPreload = {
   communityStatement();
 })();
 
-export default Object.assign({ p5Utils }, preloadRegister(withPreload), {
+export default ({
+  p5Utils,
+  ...preloadRegister(withPreload),
   KNNClassifier,
   communityStatement,
   ...imageUtils,
diff --git a/src/utils/callcallback_test.js b/src/utils/callcallback_test.js
index df60a2b23..e8b63502f 100644
--- a/src/utils/callcallback_test.js
+++ b/src/utils/callcallback_test.js
@@ -31,9 +31,7 @@ describe("callCallback", () => {
             resolve(greeting);
           });
 
-        const mockCallback = (err, result) => {
-          return `hello ${result}`;
-        };
+        const mockCallback = (err, result) => `hello ${result}`;
 
         const mockUtils = { mockCallback };
         spyOn(mockUtils, "mockCallback").and.callThrough();
diff --git a/src/utils/modelLoader.js b/src/utils/modelLoader.js
index 1fc039018..d2bfbdcd5 100644
--- a/src/utils/modelLoader.js
+++ b/src/utils/modelLoader.js
@@ -8,8 +8,7 @@ import axios from 'axios';
  * @returns {boolean}
  */
 export function isAbsoluteURL(str) {
-  const pattern = new RegExp('^(?:[a-z]+:)?//', 'i');
-  return pattern.test(str);
+  return /^(?:[a-z]+:)?\/\//i.test(str);
 }
 
 /**
diff --git a/src/utils/random.js b/src/utils/random.js
index aca04c8d4..614524f34 100644
--- a/src/utils/random.js
+++ b/src/utils/random.js
@@ -46,9 +46,12 @@ const randomSample = (arr, k, withReplacement = false) => {
     if (k > arr.length) {
       throw new RangeError('Sample size must be less than or equal to array length when sampling without replacement.')
     }
-    sample = arr.map(a => [a, Math.random()]).sort((a, b) => {
-      return a[1] < b[1] ? -1 : 1;}).slice(0, k).map(a => a[0]); 
-  };
+    sample = arr
+      .map(a => [a, Math.random()])
+      .sort((a, b) => a[1] < b[1] ? -1 : 1)
+      .slice(0, k)
+      .map(a => a[0]);
+  }
   return sample;
 };
 
diff --git a/src/utils/testingUtils/index.js b/src/utils/testingUtils/index.js
index d0bdfc644..acf82c2bc 100644
--- a/src/utils/testingUtils/index.js
+++ b/src/utils/testingUtils/index.js
@@ -13,9 +13,7 @@ export const asyncLoadImage = async (src) => {
   return img;
 }
 
-export const getRobin = async () => {
-  return asyncLoadImage("https://cdn.jsdelivr.net/gh/ml5js/ml5-library@main/assets/bird.jpg");
-}
+export const getRobin = async () => asyncLoadImage("https://cdn.jsdelivr.net/gh/ml5js/ml5-library@main/assets/bird.jpg")
 
 export const randomImageData = (width = 200, height = 100) => {
   const length = width * height * 4; // 4 channels - RGBA