What are Important Topics to Learn for Machine Learning Students
“AI winters were not due to imagination traps, but due to lack of imaginations”
The intersection of artificial intelligence and machine learning and also deep learning is to estimate more parameters in terms of computation efficiency and algorithmic approaches. The way we see AI is growing very fast these days. By 2040 we may see a huge number of applications around the world. Sometimes these applications or devices may not be helpful in such work like getting gold from earth. It’s just to solve simple problems that may not support human potential in some sense.
Let’s look at major topics in machine learning
- Understanding of problem statement
- Data cleaning and pre processing
- Exploratory data analysis and data visualisation
- Feature engineering and feature extraction
- Model building
- Monitor the models
- Model evaluation and production into the cloud
- Deploy the model and compare the outputs
So one by one we can see whatever the major topics in machine learning are and so that we will understand the most popular.
Eventually we are going to learn from the problem statement whatever the Defined by the problem statement that will be encountered in the different format which will also Look upon on in different ways I can say and also we will also Showcase some interpretability of these models so that the stakeholders and some of the end users.
1. The major concept of this objective comes into some kind of domain knowledge or how early we are going to take it by using machine learning and data science.It also much satisfied with some kind of the requirements set as low latency requirement and others are more costly here and also we will showcase model somewhat interpretability. and what is a business outcome and how is a revenue going to be generated by using this models.
2. The data cleaning and processing will take a lot of the time to do because we are getting the raw data. Most of the time will be spent on the data cleaning bi a male engineer. so here we are going to do with the raw data we also handle missing value imputation and outlier detection and also couple of the situations like they are doing to remove all of the HTML tag save on this and also couple of the special characters and we have to to encoded with with different formats like one hot encoding and label encoding converted into the tokenism mechanism such as Bert.
3. In the EDA analyses we have to know exactly how these data points are going to be visualised into the 2D format.You can also draw the statistics plots to get to know whether it comes into the different distributions. We can also try out the different planting mechanisms such as the scatter plot bar clouds and also the five plants by using that we will also write the sum of the observations regarding it.
4. This is the most important step in the whole process of machine learning. in the heart of the mission learning which is going to be this problem in different formats. so we have different types of techniques to handle this feature engineering mechanism such as the forward feature selection process and backward feature selection process. you can also try out for Feature interaction and from some of the domain knowledge by using we can also go with a complaint of the feature a variety of variation.
5. In model building we have to do trial and error methods to get to know which model is going to be worked out for this problem or on this data. Most of the time we can also go with some kind of the auto animal to get my vote. This model is going to improve in such a manner that we just use auto Vimal l or pycarret available in the open source libraries. By using the hyperparameter tuning and regularisation to decrease log loss and increase Accuracy.
6. Which is used to monitor the models of what will happen internally so that we can also figure out by using the Evaluation Matrix for KPI.
7. As we already know we use different methods for classification and regression such as precision-recall and f1 score And also AUC ROC.For regression for R square error, mean square error, root mean square error, MAPE.For clustering we used Dunn matrix.
8. So the final step is to deploy some kind of cloud environment or Which device is used on Independence day. What is going to be deployed and showcase the model working or not. and also we have to monitor the model of the times very differently from most of the times.
That’s it for the most important topics you had to come with a lot of the top with inside this machine learning and AI.
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Happy learning!!
