Check out this curated list of useful frameworks and extensions for TensorFlow.
TensorFlow has a large ecosystem of libraries and extensions. If you’re a developer, you can easily add them into your ML work without having to build new functions.
In this article, we will explore some of the TensorFlow extensions that you can start using right away.
To start, let’s check out domain-specific pre-trained models from TensorFlow Hub.
Let’s get to it!
In this video I am discussing various techniques to handle imbalanced dataset in machine learning. I also have a python code that demonstrates these different techniques. In the end there is an exercise for you to solve along with a solution link. Credit card fraud detection, cancer prediction, customer churn prediction are some of the examples where you might get an imbalanced dataset. Training a model on imbalanced dataset requires making certain adjustments otherwise the model will not perform as per your expectations.
Learning about various aspects of making use of the Python programming language to achieve Machine Learning. Python is considered to be a top programming language and this is particularly true in the case of Machine Learning as it provides fantastic libraries to help us tackle complex problems in a simple manner. Scikit-learn along with other libraries help us also to perform simple EDA (Exploratory Data Analysis) alongside training and testing the machine learning model. The instructor also explains the working of Machine Learning in terms of how the models learn and perform to add more clarity to your learning. The theoretical concepts discussed in the session will be followed by practical implementation and usage to ensure that you understand the actual usage of the programming language to solve real-world datasets.
This is the video tutorial#09 for Ai Machine Learning Course for Android Developers using TensorFlow Lite. This course is designed and created for Android developers who want to learn Machine Learning & deploy machine learning models in their android applications using TensorFlow Lite. If you have very basic knowledge of Android App development and want to learn Machine Learning use in Android Applications this course is for you. This is an incredible ML course for Android Developers 2021. This will get you started in creating your first deep learning model || machine learning model and Android Application using both JAVA & Kotlin, Tensorflow Lite, and Android studio. We will learn about machine learning and deep learning and then we will train our first model and deploy it in android application using Android studio. In this video tutorial#09 you will learn about python loops and iterations & for loop in python & while loop in python for machine learning (data science) course. Loops in Python for Machine Learning & AI || Python For Loop & Python While Loop | Tensorflow Lite
Great Learning brings you this live session on “Python For Machine Learning”. We will be exploring the important libraries required for implementing machine learning tasks. We will start off with the library called NumPy, which would help us to do numerical computation. We will work with single-dimensional as well as multi-dimensional arrays. Going ahead, we will work with the Pandas library. Pandas library is the core library for data wrangling and data manipulation. In Pandas, we will be working with both Series object as well as Dataframes. Further, we will work with the library called Seaborn, which is the core library for data visualization. Finally, there will be a QnA where you can ask all your doubts and queries. If you're looking to learn and advance in Python for the purpose of Machine Learning, do not forget to join this session!
How To Plot A Decision Boundary For Machine Learning Algorithms in Python, you will discover how to plot a decision surface for a classification machine learning algorithm.