Machine Learning Fundamental 

Tensorflow

INTRODUCTION

Core Learning Algorithms 

Working with Data 

Training and Testing Data 

The Training Process 

Classification

Building the Model

Clustering

Hidden Markov Models

Using Probabilities to make Predictions 

CORE LEARNING ALGORITHMS

Neural Networks with TensorFlow

Activation Functions

Optimizers

Creating a Model

NEURAL NETWORKS 

Convolutional Neural Networks

The Convolutional Layer

Creating a Convolutional Neural Network

Evaluating the Model

Picking a Pretrained Model

 CONVOLUTIONAL NEURAL NETWORKS

Natural Language Processing With RNNs

RNSS-Part2

Recurring Neural Networks

Sentiment Analysis

Making Predictions

Create a Play Generator

Training the Model

NATURAL LANGUAGE PROCESSING

Reinforcement Learning With Q-Learning

Q-Learning: Part 2

Q-Learning: Example

REINFORCEMENT LEARNING WITH Q-LEARNING

Conclusion

CONCLUSION

Machine learning has many practical applications that you can use in your projects or on the job.

In the Machine Learning with Python Certification, you'll use the TensorFlow framework to build several neural networks and explore more advanced techniques like natural language processing and reinforcement learning.

You'll also dive into neural networks, and learn the principles behind how deep, recurrent, and convolutional neural networks work.

In the Machine Learning with Python Certification, you'll use the TensorFlow framework to build several neural networks and explore more advanced techniques like natural language processing and reinforcement learning.

Machine Learning with Python

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