robitalhazmi/machine-learning

This is code repository that consists summary of various resources to learn machine learning.

Machine Learning

This is code repository that consists summary of various resources to learn machine learning.

Explore machine learning and deep learning techniques for building intelligent systems using scikit-learn and TensorFlow

What is this repository about?

Machine learning allows systems to learn without being explicitly programmed. This repository addresses recent developments in the field, by covering the most used datasets and libraries to help you build practical machine learning systems.

This repository covers the following exciting features:

• Build a classification system that can be applied to text, image, and sound
• Employ Google Cloud Services (GCP) to run analysis on the cloud
• Solve problems related to regression using TensorFlow
• Recommend products to users based on their past purchases
• Explore the steps required to add collaborative filtering using TensorFlow

Instructions and Navigations

All of the code is organized into folders. For example, Chapter01.

The code will look like the following:

 def fetch_posts(fn):
     for line in open(fn, "r"):
         post_id, text = line.split("\t")
         yield int(post_id), text.strip()
 

Following is what you need for this repository: This Repository is for data scientists, machine learning developers, and Python developers who want to learn how to build increasingly complex machine learning systems. Prior knowledge of Python programming is expected.

With the following software and hardware list you can run all code files present in the repository (Chapter 1-14).

Software and Hardware List

Chapter	Software required	OS required
1-14	Python 3, NumPy, SciPy, scikit-learn (latest version)	Ubuntu/Linux, macOS or Windows

Chapter List

• Chapter 1: Getting started [Link]
• Chapter 2: The Iris dataset [Link]
• Chapter 3: Predicting house prices with regression [Link]
• Chapter 4: Learning to classify classy answers [Link]
• Chapter 5: Dimensionality Reduction [Link]
• Chapter 6: Measuring the relatedness of posts [Link]
• Chapter 7: Rating predictions and recommendations [Link]
• Chapter 8: Artificial Neural Networks and Deep Learning [Link]
• Chapter 9: Classification II – Sentiment Analysis [Link]
• Chapter 10: Latent Dirichlet allocation [Link]
• Chapter 11: Classification III – Music Genre Classification [Link]
• Chapter 12: Computer Vision [Link]
• Chapter 13: Reinforcement Learning [Link]
• Chapter 14: Using Amazon Web Services [Link]

Get to Know the Contributor

Robit Al Hazmi LinkedIn