The development and growth of Machine Learning/Deep Learning will trendy in the future. Machine learning is the process of data analysis. So you are able to learn from the gathered data by using various algorithms of Machine Learning. Through algorithms, you can also identify many patterns from which the new information can be forwarded. There are many steps which provide help to research in Machine Learning/Deep Learning that are discussed below:

    1. Identification of Problem: You can use any algorithms to defining your problem with machine learning. It uses the framework when you are defining a new problem which follows:
      • The first step includes the definition of the problem. It used a number of methods to gather the information which is relating to the defining During the describing your problem, it provides the information about highlight areas which you require to fill it.
      • In this step, you tell about what is the requirement to solve this occurring problem. So the need for motivation is essential to solve the problem. After this, you investigate various possible solutions to solve this problem. You also make sure about the benefits of finding solutions and all the solutions must be meaningful. Finally, how can you use the best solution to solve the problem and what type of expectations you want from the choosing solution?
      • In the last step, you have to decide the design of the program to solve the problem. You used various prototypes methods and experiments for it.
    2. Analysis of Data: The main purpose of the data analysis of better understanding of the problems data. So it provides multiple ways to describe the data so that you can review all the observations and assumptions that can be used later. It includes two types of data that are:
      1. Summarize Data: In this, you describe the actual structure of the data.
        • Data Structure concept is used to describe number and data types of attributes. In this process, highlight ideas are transformed in the data preparation in which the attribute is converted from one type to another.
        • Data Distributions concept is used to summarize the distributions of each attribute which is used in the Data Preparation step like distribution of each real-valued attribute such as minimum, maximum, mean, median, standard deviation, missing values, and mode etc.
      2. Visualize Data: In this, the data is creating in the form of various graphs like Attribute Histograms and Pairwise Scatter-Plots. 
    3. Preparation of Data: The algorithms of machine learning learn from the data. This process includes following steps:
      1. Select Data: This step includes the subset of all available data.
        • What data you need to address the question or problem you are working on.
        • What is the extent of the data you require for it like database tables, connected systems?
        • It chooses valid data and excluded unusual data.
      2. Pre-Process Data: It contains Formatting, Cleaning & Sampling of the data.
        • Formatting: When you select the data, it is not in the format which is not required for working. So, the data is arranged in a relational database like a flat file or a text file.
        • Cleaning: During the gathering process of the data, some data is not in the complete form. So cleaning process is used to remove or fix the missing data.
        • Sampling: In this, you select the more appropriate data for working.
      3. Transform Data: It includes the step of transforming the process data. It contains following things:
        • Scaling
        • Decomposition
        • Aggregation




      1. Evaluation of Algorithms: When you define the problem and prepare the data, then you apply algorithms to evaluate it and solve your problem. Firstly you choose the appropriate algorithm and then run it. It contains following things:
        1. Test Harness: In this, you train and test the algorithm and its performance measures against its coming performance. It quickly and consistent results of test algorithms against a fair representation of the problem being solved.
        2. Performance Measure: You can measure the performance by the predictions which are made by a trained model test dataset. So you can easily evaluate the solution to the problem.
        3. Test & Train Dataset: Firstly select the test set and training set. So the algorithm is trained on the training dataset and then evaluate against the test set.
        4. Cross-Validation: You divide the data into a number of equally sized groups of instances which are called folds. You trained all folds and left one, then prepared model is used to test left one fold. So this process is repeated until covers all folds.


      1. Improve Results: You can improve your results through this step which includes following strategies:
        1. Algorithm Tuning: Gathering better results from algorithms that are well-performed on your problem. So the learning process gives the outcomes of all parameterized and modification parameters. So the main objective of this tuning is to find the best solutions to the problems.
        2. Ensembles: In this, ensembles techniques are used to combine the results of all applying methods and then improve the getting results. It uses following tools to ensembles that are:
          • Bagging
          • Boosting
          • Blending
        3. Extreme Feature Engineering: You expose more structure for solving the problem by using algorithms. So you decompose and aggregate the data in order to better normalize the data for machine learning algorithms.
      2. Write the Solution & Present Results: After finding and tuning the data, you write the solution of the defining problem and present the results. You follow two steps such as:
        1. Report the Results: When you discovered a better model then you summarize a final report. So the clients can learn from it. The final report consists following points:
          • Context: Define the working environment where the problem exists
          • Problem: Now define the problem in described manner
          • Solution: Find all possible solution to solve a specific problem
          • Findings: Search the appropriate data and methods which will make performance data models
          • Limitations: List all the disadvantages of finding models
          • Conclusions: In this, finally you select best data model which is capable to provide good result report.
        2. Operationalize the System: In this step, you will operate the various operating on the selected data model that is:
          • Algorithm Implementation
          • Model Tests
          • Tracking



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