**Machine Learning A-Z: Hands-On Python & R In Data Science**

In this course, You will learn to:

- Master Machine Learning on Python & R
- Have a great intuition of many Machine Learning models
- Make accurate predictions
- Make powerful analysis
- Make robust Machine Learning models
- Create strong added value for your business
- Use Machine Learning for personal purpose
- Handle specific topics like Reinforcement Learning, NLP and Deep Learning
- Handle advanced techniques like Dimensionality Reduction
- Know which Machine Learning model to choose for each type of problem
- Build an army of powerful Machine Learning models and know how to combine them to solve any problem

**A-Z Machine Learning using Azure Machine Learning (AzureML)**

In this course, You will learn to:

- Master Machine Learning Models using Azure ML.
- Understand the concepts and intuition of Machine Learning models
- Build Machine Learning models within minutes
- Choose the correct Machine Learning Algorithm using the cheat sheet
- Deploy production grade Machine Learning models
- Use Machine Learning in the simplest form possible, using excel
- Bring in great value to the business you manage

### Python Mini Degree in Deep Learning

From Zero to Expert. Learn Computer Vision, Machine Learning, Deep Learning, Game Development and Internet of Things (IoT) App Development.

### Complete Guide to TensorFlow for Deep Learning with Python

Learn how to use Google’s Deep Learning Framework – TensorFlow with Python! Solve problems with cutting-edge techniques!

In this course, You will learn to:

- Understand how Neural Networks Work
- Build your own Neural Network from Scratch with Python
- Use TensorFlow for Classification and Regression Tasks
- Use TensorFlow for Image Classification with Convolutional Neural Networks
- Use TensorFlow for Time Series Analysis with Recurrent Neural Networks
- Use TensorFlow for solving Unsupervised Learning Problems with AutoEncoders
- Learn how to conduct Reinforcement Learning with OpenAI Gym
- Create Generative Adversarial Networks with TensorFlow
- Become a Deep Learning Guru!

### Modern Deep Learning in Python

Build with modern libraries like Tensorflow, Theano, Keras, PyTorch, CNTK, MXNet. Train faster with GPU on AWS.

In this course, You will learn to:

- Apply momentum to backpropagation to train neural networks
- Apply adaptive learning rate procedures like AdaGrad, RMSprop, and Adam to backpropagation to train neural networks
- Understand the basic building blocks of Theano
- Build a neural network in Theano
- Understand the basic building blocks of TensorFlow
- Build a neural network in TensorFlow
- Build a neural network that performs well on the MNIST dataset
- Understand the difference between full gradient descent, batch gradient descent, and stochastic gradient descent
- Understand and implement dropout regularization in Theano and TensorFlow
- Understand and implement batch normalization in Theano and Tensorflow
- Write a neural network using Keras
- Write a neural network using PyTorch
- Write a neural network using CNTK
- Write a neural network using MXNet

### Learning Path: The Road to TensorFlow-2nd Edition

Discover deep learning and machine learning with Python and TensorFlow.

In this course, you will learn to:

- Build Python packages to efficiently create reusable code
- Become proficient at creating tools and utility programs in Python
- Design and train a multilayer neural network with TensorFlow
- Understand convolutional neural networks for image recognition
- Create pipelines to deal with real-world input data
- Set up and run cross domain-specific examples (economics, medicine, text classification, and advertising)
- Learn how to go from concept to a production-ready machine learning setup/pipeline capable of real-world usage

### Deep Learning with TensorFlow

Channel the power of deep learning with Google’s TensorFlow!

In this course, you will learn to:

- Set up your computing environment and install TensorFlow
- Build simple TensorFlow graphs for everyday computations
- Apply logistic regression for classification with TensorFlow
- Design and train a multilayer neural network with TensorFlow
- Understand intuitively convolutional neural networks for image recognition
- Bootstrap a neural network from simple to more accurate models
- See how to use TensorFlow with other types of networks
- Program networks with SciKit-Flow, a high-level interface to TensorFlow

### TensorFlow 101: Introduction to Deep Learning

Ready to build the future with Deep Neural Networks? Stand on the shoulder of TensorFlow and Keras for Machine Learning.

In this course, you will learn to:

- You will be able to build deep learning models for different business domains in TensorFlow
- You can distinguish classification and regression problems, apply supervised learning, and can develop solutions
- You can also apply segmentation analysis to unsupervised learning and clustering
- You can consume TensorFlow via Keras in an easier way.
- Finally, you will be informed about tuning machine learning models to produce more successful results

### TensorFlow: Getting Started

This course shows you how to install and use TensorFlow, a leading machine learning library from Google. You’ll see how TensorFlow can create a range of machine learning models, from simple linear regression to complex deep neural networks.

In this course, you will learn to:

- Introduce you to TensorFlow – introduce you to TensorFlow’s special architecture that let you easily develop your solution on your laptop, and scale your solution to large server farms.
- Construct Solutions with TensorFlow – We start by creating simple models.Then go on to learn about Neural Networks in TensorFlow and apply their features to create complex solutions which identify items in pictures.
- Utilize and Maintain Solutions – Once we have a solution, we show how to deploy it and learn about the unique monitoring and debugging tools provided with TensorFlow.
- Easy to do – And all of this will be easy to do, with TensorFlow’s expressive syntax and structures.

By the end of this course, you will have a solid foundation on using TensorFlow. And know how to apply TensorFlow to create your world-class machine-learning solutions.