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8 Ways You Can 'Level Up' Your Machine Learning Projects

8 Ways You Can 'Level Up' Your Machine Learning Projects

8 Ways You Can 'Level Up' Your Machine Learning Projects

Need to classify data or predict outcomes? Are you struggling with your machine learning (Machine Learning ) project? There are various techniques that can improve the situation.

Some of the eight methods discussed below will dramatically accelerate the Machine Learning process, and others will not only accelerate the process, but will also help you build better models. Not all of these techniques will be suitable for a particular project. However, the first, exploratory data analysis, is valid for almost all projects. Here are eight ways you can take your Machine Learning or deep learning project to the next level. 

Machine learning: Start with Exploratory Data Analysis

Jumping straight into Machine Learning training without examining the data in depth is like architecture without design. It will take a lot of effort and it won't be very rewarding.

Exploratory data analysis combines graphical and statistical methods. More common techniques include histograms and box-and-whisker plots for individual variables, scatter charts for pairs of variables, and descriptive statistics plots that display correlations between variables as heatmap plots of pairwise correlations.

Exploratory data analysis may also include dimensionality reduction techniques such as Principal Component Analysis (PCA) and Nonlinear Dimensionality Reduction (NLDR). For time-dependent data, it is necessary to draw a line chart of raw variables and statistics based on time, which can identify seasonal and daily fluctuations and anomalous movements resulting from external effects such as storms and (ahem) epidemics.

Exploratory data analysis is not just a statistical graph. This is a philosophical approach to data analysis designed to help you maintain an open mind instead of forcing data into models. These days, many ideas about exploratory data analysis have been incorporated into data mining.

Machine learning: Build Unsupervised Clusters

Cluster analysis is an unsupervised learning problem in which a model is asked to find a group of similar data points . Currently, several clustering algorithms are used, each with slightly different characteristics. In general, clustering algorithms look at the index or distance feature between feature vectors of data points and then group those that are 'close' to each other. The clustering algorithm is particularly effective when classes are not nested.

A common clustering method is K-means, which attempts to split n observations into k clusters using the Euclidean distance scale to minimize the variance (sum of squares) within each cluster. This is a vector quantization method and is useful for feature learning.

Lloyd's algorithm (iterative clusters with central updates) is the most popular heuristic and is used to solve problems and is relatively efficient, but does not guarantee global convergence. To improve this, people run the algorithm multiple times using the initial cluster centroids generated by Forgy or Random Partition methods.

K-means assumes separable spherical clusters such that the mean converges to the cluster centroid, and the placement of data points is irrelevant. Clusters are expected to be similar in size so that the nearest cluster centroid is the correct allocation.

If K-means clustering does not work, consider hierarchical cluster analysis, mixed models, DBSCAN, etc. We also consider other types of autonomous learning, such as autoencoders and moment methods.

Machine learning: Tag data through semi-autonomous learning Tagged

data is an essential part of Machine Learning. Without tagged data, the model cannot be trained to predict the target value.

A simple but expensive solution to this is to manually tag all data. Some professors 'joke' about this by asking graduate students to do it (which would be a funny story for graduate students).

A less expensive solution is to manually tag some of the data and then predict the rest of the target values ​​with one or more models. This is called semi-supervised learning . Through an autonomous training algorithm (a kind of semi-autonomous learning), we accept the values ​​expected from a single model whose probability exceeds a certain threshold, and build a refined model using a larger dataset. 

The model is then used for other predictions, and it repeats until there are no reliable predictions. Autonomous training is sometimes effective, but the model is broken by false predictions.

If you build multiple models and use them to check each other, you can develop something more robust, such as tri-training. Another alternative is to combine semi-autonomous learning with transfer learning of existing models built on other data.

You can implement this schema yourself. Alternatively, web services through trained labelers such as Amazon SageMaker Ground Truth, Hive Data, Labelbox, Dataloop, Datasaur, etc. is available.

Machine learning: Add complementary datasets

Externalities often expose anomalies in datasets, especially with time series datasets. For example, adding weather data to a bike rental dataset could account for many changes you might not have known about, such as a sharp drop in rentals during a storm.

Retail sales forecasting is a good example. Sales, competitive products, advertising changes, economic events, and weather can all affect sales. To put it simply, if your data doesn't make sense, adding some context will make everything clearer.

Machine learning: Train Automated Machine Learning  At 

one time, the only way to find the best model for your data was to train all possible models and find the best among them. From many kinds of data, including tagged tabular data, you can provide a dataset to an AutoMachine Learning  tool and get an appropriate answer later. 

Sometimes the best model is a combination of other models. They can be expensive to use for inference, but often the simplest models are just as good as their combination, while operating is often much lower.

The AutoMachine Learning service goes beyond just trying all the appropriate models. For example, some automatically generate normalized and engineered feature sets for time series prediction, impute missing values, discard correlated features, and add lagging columns. 

Another optional activity is to perform hyperparameter optimizations so that the best model can take it further. In order to get the best possible results in the allotted time, some AutoMachine Learning services quickly stop training models that do not improve significantly and put more cycles into the models that seem most promising.

Machine learning: Customize the trained model through transfer learning Training a

large neural network from scratch typically requires a lot of data (often millions of training items) and significant time and compute resources (using multiple server GPUs for weeks). below) is required. 

A powerful shortcut called transfer learning aims to either customize a trained neural network by training a few new layers on the network with new data, or to extract features from the network and then use it to train a simple linear classifier. 

This can be done using cloud services such as Azure Custom Vision or custom Language Understanding, or using a library of trained neural networks created with TensorFlow or PyTorch, etc. can Transfer learning or fine tuning can often be completed in minutes with a single GPU.

Machine learning: Try Deep Learning Algorithms from the 'Model Zoo'

Even if you can't easily create the model you need through transfer learning using your preferred cloud service or deep learning framework, it may still prevent you from designing and training a deep neural network model from scratch. 

Most major frameworks have a broader model stock than the model API. There are even websites that maintain model notes for multiple frameworks or frameworks that can handle specific representations, such as ONNX . Many models in the model week have been trained and are ready to use. However, some are partially trained snapshots, and their weights are useful as a starting point for training using your own dataset.