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Predicting the health of power transformers using machine learning is crucial for ensuring the reliable operation of electrical grids. Will be analyzing historical data on transformer performance found here on Kaggle, machine learning algorithms can identify patterns and anomalies that indicate potential failures or deteriorating conditions. This early detection enables proactive maintenance and replacement, minimizing downtime, reducing costs, and preventing catastrophic fa

In this guided project, we will embark on an exciting journey through the MNIST digits dataset , one of the most influential benchmarks in the field of machine learning. Our goal is to develop a comprehensive understanding of image classification and master the art of recognizing handwritten digits from images. By utilizing popular deep learning framework TensorFlow, we will build and train convolutional neural networks (CNNs) to achieve high accuracy in classifying the dig

In today's data-driven world, extracting meaningful insights from the vast amount of information available is crucial for making informed decisions. When it comes to the financial domain, understanding sentiment in news articles can provide valuable insights for investors, analysts, and decision-makers. In this blog post, we delve into our latest guided project, where we leverage the power of Deep Learning and TensorFlow to build a sentiment classification model for financial

Zipped TensorFlow Sequential Animal Prediction Model Zipped Folder of Zipped Train and Zipped test Images used in Video Expanding our convolution network in this multi-class classification guided project with the Sequential model In TensorFlow with Python. Although you can get more complex with the Sequential model , training a model of this complexity takes a long long time, as in 4 hours with a GPU and those unfortunately those aren't free. Without a GPU it took me rough

Follow in this free-to-use Python TensorFlow guided project and build a deep learning model with the functional API in TensorFlow . Over the sequential model, the functional model in TensorFlow allows you to have multiple inputs and multiple outputs. This gives us a powerful technique in our deep learning architecture and is necessary for more advanced deep learning architectures. Here we will use the functional API in TensorFlow to see if we can get a better accuracy scor

TensorFlow is a power tool for building neural networks in many data science problems from tabular data and spreadsheets to NLP or text data and even images and photographs. TensorFlow has 3 available APIs to build your neural network Sequential API , Functional API , and the Subclassing Model API . These 3 API offer different levels of flexibility and ease of writing code. The Sequential API is the easiest to use and a great place to start for some new to TensorFlow. It'

create sequences for each next character Follow along with this Free Guided TensorFlow project. In this free Python Project, we will...

DataSimple.education  Certifications Data Analysis, Pandas, Seaborn, Plotly and More Use your Python data analysis skills to better understand the Movies and TV shows in the Netflix dataset. We will use Pandas and Seaborn to perform our data analysis and better understand when new titles are released. Use WordCloud in Python to better understand the most common words in Movie and TV Show titles. Use plotting functions like the pie plot in Pandas . The countplot and histpl

Continuing our LSTM stock prediction Python guided deep learning project. In our part 4 part of our Tensorflow Stock prediction project, we start with the functional API in Tensorflow . The functional API will allow us to ingest 3 different sources of data and extract higher-level features from each dataset before combining it with the next channel inside your neural network. We set up the workflow to allow for experiments and for you to further refine the architecture. I

In this Python ML project, we will explore an individual DecisionTree and learn to use pre-pruning and post-pruning techniques. Prepruning techniques are generally easier to use and involve setting hyperparameters that limit the growth of our decision trees. Post-pruning techniques are a little harder to work with but very important to understand how to use the when applying them to a random forest of decision trees. Let's practice with pre and post-pruning techniques in

Join me on the Python Machine Learning Project where we predict the price of cars in Poland. This supervised learning problem in Python is a regression problem. In this project with will focus on linear regression techniques including Lasso , Ridge , and ARDRegression models. This is a great project for those looking to explore linear regression techniques.

In this supervised learning Python ML guided project we will be predicting whether or not someone was approved for a credit card. In this Python project, we will only be using the LogisticRegession() in Sklearn for this supervised classification problem. Although this project would work with RandomForestClassifier() or GradientBoostingClassifier() we will be focusing on error analysis in this classification problem. Understanding precision versus recall and why we would

This Python computer vision project is a minimal level of difficulty to get computer vision to work well. Overfitting in computer vision, in part, has to do with our deep learning models learning the a shape it's recognized is in a certain position in the picture. To solve this we can use Tensorflow's ImageDataGenerator . This will require us to work with a Tensorflow dataset which can be a little tricky at first as it works differently than a Pandas DataFrame. The benefi

Study what happened to the Sri Lanka economy with python from 1966 to 2022 and see if you can understand what lead to the high inflation and low employment that the country faced during and after the pandemic. Use matplotlib.pyplot as plt to assist us in plotting two Seaborn lineplot and scatterplots together. They create stunning effects in your Seaborn plots and add a really nice professional touch to your seaborn plots. This is the Sri Lanka economy over time and so we

Here we use Pandas and Seaborn in Python to go through the job listings for Data Analyst Job Postings. we'll have to do a lot of cleaning of the string features to be able to analyze this data set. Let's explore which type of Data Analyst gets hired, and has the biggest salaries. Business analyst, data analyst, BI analyst etc. Also explore important factors like education, year of experience, and location make on data analyst salaries in this instructional Python learning

Use pandas and seaborn in Python to analyze the world happiness index and better understand what affect a countries happiness by analyzing the data.

Data science and data analysis gives us access to the ability to make our own interpretations from the data. I wanted to see what was...