What will you learn in this course?

Our seven sections in this course address every aspect of neural networks, and machine learning in python-based TensorFlow and Keras frameworks. This course will cover the following:

• A complete introduction to python-based anaconda, Keras, and TensorFlow-driven data science, machine learning, and neural networks.
• Interaction with python Jupyter notebooks and Libraries of Keras and TensorFlow to implement AI-based techniques of machine learning, and neural networks
• Presentation of Tensorflow and Keras installation.
• Interaction with basic packages and frameworks of python used in Keras and TensorFlow
• Introduction to Pandas and Numpy libraries
• Understanding of basic syntax and visualization in TensorFlow environment
• Statistical modeling in Tensorflow frameworks
• Machine learning in both Supervised and Unsupervised Learning under Keras and Tensorflow frameworks
• Understanding structures of artificial neural networks and deep learning structures

But wait! My course is not just an average course!

In this course, you will absorb the most powerful hacks that work behind Keras and Tensorflow techniques associated with machine learning, deep learning, and neural networks.

I will help you to get started with valuable concepts and techniques in TensorFlow and Keras neural networks. As mentioned earlier, I have used the most simple, easy-to-understand, and straightforward methods to address every concept of TensorFlow and keras frameworks along with python libraries.

This course will be the proven ultimate guide for you that will help you to implement the machine learning and deep learning techniques in the real world. No matter, what type of data you want to interpret.

At the end of this course, you will be able to use TensorFlow and Keras frameworks such as NumPy, matplotlib, and pandas in different scenarios and gain fluency in Tensorflow. What is more?

Deep conceptualization of python's statistical modeling is not going anywhere if you invest your time in deep learning.

Even, I assure you that my course will cover other deep learning models, too. For example, I have added lectures that cover Convolution Neural network (CNN), Long Short Term Memory Networks (LSTMs), Generative Adversarial Networks (GANs), Multilayer Perceptrons (MLPs) and more.

All-In-One neural networks Course in Python

As I said earlier, this course is wholesome. In TensorFlow and Keras, you will learn all aspects of deep learning such as visualization, stats, neural networks, image recognition and mining of data.

The motivation behind this course is the practice, which students are lacking these days on the real-time interpretations. My vision is to provide students with real-world data and let them interpret it. Students will be able to analyze their own projects in order to impress reputable employers with their skill sets under neural networks.

Finally, this course is practical. In this course, you will be spending time dealing with theoretical as well as practical concepts. Around 30%, of course, is based on theoretical concepts, and 70%, of course, is based on practice where students will implement deep learning techniques to interpret and analyze probable outcomes. In every lecture, our video will help you to learn the technical concepts of your own projects.