Python Machine Learning in 7 Days

Video description

Machine learning is one of the most sought-after skills in the market. But have you ever wondered where to start or found the course not so easy to follow. With this hands-on and practical machine learning course, you can learn and start applying machine learning in less than a week without having to be an expert mathematician.

In this course, you will be introduced to a new machine learning aspect in each section followed by a practical assignment as a homework to help you in efficiently implement the learnings in a practical manner. With the systematic and fast-paced approach to this course, learn machine learning using Python in the most practical and structured way to develop machine learning projects in Python in a week.

This course is structured to unlock the potential of Python machine learning in the shortest amount of time. If you are looking to upgrade your machine learning skills using Python in the quickest possible time, then this course is for you!

This course uses Python 3.6 while not the latest version available, it provides relevant and informative content for legacy users of Python.

What You Will Learn

  • Master the most important algorithms in machine learning
  • Make predictions based on data
  • Get an intuitive understanding of how machine learning works
  • Get an intuitive understanding of where to use which machine learning approach
  • How to use pre-written libraries in python to work with powerful algorithms
  • Learn advanced machine learning techniques like Neural Networks


If you are interested in Machine Learning and have a basic understanding of python and looking to expand your Python skills in a quick time-frame.

About The Author

Arish Ali: Arish Ali started his machine learning journey 5 years ago by winning an all India machine learning competition conducted by Indian Institute of Science and Microsoft. He was a data scientist at Mu Sigma, one of the biggest analytics firms in India. He has also worked on some of the cutting edge problems of Multi-Touch Attribution Modelling, Market Mix Modelling, and Deep Neural Networks. He has also been an Adjunct faculty for Predictive Business Analytics at Bridge School of Management, which offers its course in Predictive Business Analytics along with North-western University (SPS). Currently, he is working at a mental health startup called Bemo as an AI developer where his role is to help automate the therapy provided to users and make it more personalized.

Table of contents

  1. Chapter 1 : Enter the Machine Learning World!
    1. The Course Overview
    2. Setting Up Your Machine Learning Environment
    3. Exploring Types of Machine Learning
    4. Using Scikit-learn for Machine Learning
    5. Assignment – Train Your First Pre-built Machine Learning Model
  2. Chapter 2 : Build Your First Predicting Model
    1. Supervised Learning Algorithm
    2. Architecture of a Machine Learning System
    3. Machine Learning Model and Its Components
    4. Linear Regression
    5. Predicting Weight Using Linear Regression
    6. Assignment – Predicting Energy Output of a Power Plant
  3. Chapter 3 : Image Classification Using Supervised Learning
    1. Review of Predicting Energy Output of a Power Plant
    2. Logistic Regression
    3. Classifying Images Using Logistic Regression
    4. Support Vector Machines
    5. Kernels in a SVM
    6. Classifying Images Using Support Vector Machines
    7. Assignment – Start Image Classifying Using Support Vector Machines
  4. Chapter 4 : Improving Model Accuracy
    1. Review of Classifying Images Using Support Vector Machines
    2. Model Evaluation
    3. Better Measures than Accuracy
    4. Understanding the Results
    5. Improving the Models
    6. Assignment – Getting Better Test Sample Results by Measuring Model Performance
  5. Chapter 5 : Finding Patterns and Structures in Unlabeled Data
    1. Review of Getting Better Test Sample Results by Measuring Model Performance
    2. Unsupervised Learning
    3. Clustering
    4. K-means Clustering
    5. Determining the Number of Clusters
    6. Assignment – Write Your Own Clustering Implementation for Customer Segmentation
  6. Chapter 6 : Sentiment Analysis Using Neural Networks
    1. Review of Clustering Customers Together
    2. Why Neural Network
    3. Parts of a Neural Network
    4. Working of a Neural Network
    5. Improving the Network
    6. Assignment – Build a Sentiment Analyzer Based on Social Network Using ANN
  7. Chapter 7 : Mastering Kaggle Titanic Competition Using Random Forest
    1. Review of Building a Sentiment Analyser ANN
    2. Decision Trees
    3. Working of a Decision Tree
    4. Techniques to Further Improve a Model
    5. Random Forest as an Improved Machine Learning Approach
    6. Weekend Task – Solving Titanic Problem Using Random Forest

Product information

  • Title: Python Machine Learning in 7 Days
  • Author(s): Arish Ali
  • Release date: June 2018
  • Publisher(s): Packt Publishing
  • ISBN: 9781788999137