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While performing data analysis, the model is trained with the training data and then inference on new data according to this trained model.

Model training is a difficult process. A large amount of labeling and computing power is required to train a model from scratch. For example, thousands of hours of GPU were run to train the NASNet model developed in 2017.

Fortunately, there are pre-trained models we can use for analysis such as text or image classification. These trained models are also available in libraries such as TensorFlow. …

Computer vision is one of the important research areas in machine learning. With smartphones and tablets, people take pictures every day and upload them to social media platforms. Specialists are needed to analyze this huge amount of produced pictures.

Computer vision is used in many fields such as robotics, healthcare, drones, driver-less cars, sports and entertainment.

In my previous article, I was introduced to deep learning with TensorFlow.

In this article, I will explain classifying images using deep neural networks.

With the development of artificial intelligence, previously unsolvable problems could be solved. In this article, I will introduce deep learning with TensorFlow.

In recent years, data production has increased with the development of the internet and technology. Machine learning techniques are used to extract information from data. But machine learning techniques were insufficient to analyze big data.

Deep learning techniques have been used to find patterns in big data in recent years. Most of the deep learning techniques had been discovered before, but these techniques were not fully utilized due to scarcity of data and lack of hardware.

In recent…

Data visualization is one of the most enjoyable stages of data analysis. You can draw graphics very easily with the recently developed open source and free libraries. The Pandas library is one of the most used Python libraries for data preprocessing and data cleaning. Libraries such as Matplotlib and Seaborn are often used to visualize data. But with Pandas, you can easily visualize Series and DataFrame data.

In my last article, I explained the bar, histogram and box graphics with Pandas.

In summary, I will explain the following topics in this article.

- Area charts
- Scatter plots
- Hexagonal bin charts
- Pie…

As a data scientist, one of my favorite stages of data analysis is data visualization. When I visualize data, I feel like an artist. Here you can watch how masterpieces can be created with data visualization and machine learning.

Data visualization is one of the important steps of data analysis. To visualize data, most people usually use Matplotlib and Seaborn. Pandas is one of Python’s most important libraries used for data preprocessing and data cleaning. You can also easily visualize Series and DataFrames with methods in Pandas. In my last article, I talked about scatter plots with Matplotlib in Python…

Data visualization is one of the first stages of data analysis. In my last post, I explained histograms with Matplotlib in Python. I will talk about the scatter plots in this post.

A Scatter plot can be plotted to see the relationship between two variables. In the scatter plot, the observation values are shown with dots. If these points are spread around a line, it means that there is a relationship between the variables. Scatter plot also called a scatterplot, scatter graph, scatter chart, scattergram, or scatter diagram.

In summary, in this article, I will explain the following topics using…

Suppose you have average salary data for five different jobs and you want to compare these salaries. You can use bar plots to visualize this data. Bar plots are used to compare values in different categories. Bars can be plotted horizontally or vertically. In my last article, I explained histograms with Matplotlib in Python. In this article, I will describe the bar plot.

In summary, I will explain the following topics in this article.

- How to plot bar plots?
- How to plot error bars?
- How to plot bar graphs with error bars?

You may have heard of histograms or seen in data analysis. If you are not good at statistics, you can confuse the histograms with the bar graphs. In my last post, I talked about line plots in python using matplotlib. Histograms are also similar to bar charts. But there are some differences.

A histogram is used to summarize continuous or discrete data. For example, you have the notes of the introductory computer science course of the students of X universities. You want to see which grade has been taken the most, with less than average grade fields or more grade…

Data visualization is one of the important steps of data analysis. By visualizing the data, we can explore the information in the data. One of the simplest graphs is line plots. In my last post, I explained how to plot statistical graphs with Seaborn and Pandas. In this post, I will talk about how to plot line graphs with the Matplotlib.

In summary, I will mention the following topics in this post,

- What are the line plots?
- How to plot a simple line graph?
- How to specify the color and style of the line plot?
- How to fill the area…

Visualizing data is one of the important steps of data analysis. I explained the Seaborn library in the last post. In this post, I will talk about data visualization with Pandas and Seaborn. Matplotlib library and plot method in Pandas are not enough to plot advanced graphics. In order to visualize the data better and easier, Seaborn library is used, which works on the Matplotlib library and is compatible with Pandas.

Many statistical graphs are easily plotted with Seaborn. In summary, I will mention the following topics in this post,

- Line plots
- Bar plots
- Histogram and density plots
- Scatter plots
- …