fix default pipeline and typo in README

README.md

@@ -7,7 +7,7 @@
 TODS is a full-stack automated machine learning system for outlier detection on multivariate time-series data. TODS provides exhaustive modules for building machine learning-based outlier detection systems, including: data processing, time series processing, feature analysis (extraction), detection algorithms, and reinforcement module. The functionalities provided via these modules include data preprocessing for general purposes, time series data smoothing/transformation, extracting features from time/frequency domains, various detection algorithms, and involving human expertise to calibrate the system. Three common outlier detection scenarios on time-series data can be performed: point-wise detection (time points as outliers), pattern-wise detection (subsequences as outliers), and system-wise detection (sets of time series as outliers), and a wide-range of corresponding algorithms are provided in TODS. This package is developed by [DATA Lab @ Texas A&M University](https://people.engr.tamu.edu/xiahu/index.html).
 
 TODS is featured for:
-* **Full Sack Machine Learning System** which supports exhaustive components from preprocessings, feature extraction, detection algorithms and also human-in-the loop interface. 
+* **Full Stack Machine Learning System** which supports exhaustive components from preprocessings, feature extraction, detection algorithms and also human-in-the loop interface. 
 
 * **Wide-range of Algorithms**, including all of the point-wise detection algorithms supported by [PyOD](https://github.com/yzhao062/pyod), state-of-the-art pattern-wise (collective) detection algorithms such as [DeepLog](https://www.cs.utah.edu/~lifeifei/papers/deeplog.pdf), [Telemanon](https://arxiv.org/pdf/1802.04431.pdf), and also various ensemble algorithms for performing system-wise detection.
 
@@ -48,14 +48,14 @@ pip install -e .
 ```
 
 # Examples
-Examples are available in [/examples](examples/). For basic usage, you can evaluate a pipeline on a given datasets. Here, we provide an example to load our default pipeline and evaluate it on a subset of yahoo dataset.
+Examples are available in [/examples](examples/). For basic usage, you can evaluate a pipeline on a given datasets. Here, we provide example to load our default pipeline and evaluate it on a subset of yahoo dataset.
 ```python
 import pandas as pd
 
 from tods import schemas as schemas_utils
 from tods import generate_dataset, evaluate_pipeline
 
-table_path = 'datasets/yahoo_sub_5.csv'
+table_path = 'datasets/data/raw_data/yahoo_sub_5.csv'
 target_index = 6 # what column is the target
 metric = 'F1_MACRO' # F1 on both label 0 and 1