Python Machine Learning in 7 Days

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

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Last updated Fri, 29-Jan-2021
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Course overview

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! Style and Approach: This is a fast-paced course offering practical and actionable guidance with step-by-step instruction and assignments. This course will enable you to develop your own ML models and methods to use them efficiently in the quickest possible way. 


Target Audience 

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. 


Business Outcomes  

  • A good understanding of Machine learning to start creating practical solutions.  
  • Get an intuitive understanding of many machine learning algorithms
  • Build many different Machine Learning models and learn to combine them to solve problems

What will i 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 
Requirements
Curriculum for this course
43 Lessons 2 hrs
Python Machine Learning in 7 Days
1 Lessons 02:00:00 Hours
  • Python Machine Learning in 7 Days
    Preview 02:00:00
Section 1: Enter the Machine Learning World!
5 Lessons
  • 1.1 The Course Overview
    Preview .
  • 1.2 Setting Up Your Machine Learning Environment
    Preview .
  • 1.3 Exploring Types of Machine Learning
    Preview .
  • 1.4 Using Scikit-learn for Machine Learning
    Preview .
  • 1.5 Assignment – Train Your First Pre-built Machine Learning Model
    Preview .
Section 2: Build Your First Predicting Model
6 Lessons
  • 2.1 Supervised Learning Algorithm
    Preview .
  • 2.2 Architecture of a Machine Learning System
    Preview .
  • 2.3 Machine Learning Model and Its Components
    Preview .
  • 2.4 Linear Regression
    Preview .
  • 2.5 Predicting Weight Using Linear Regression
    Preview .
  • 2.6 Assignment – Predicting Energy Output of a Power Plant
    Preview .
Section 3: Image Classification Using Supervised Learning
7 Lessons
  • 3.1 Review of Predicting Energy Output of a Power Plant
    Preview .
  • 3.2 Logistic Regression
    Preview .
  • 3.3 Classifying Images Using Logistic Regression
    Preview .
  • 3.4 Support Vector Machines
    Preview .
  • 3.5 Kernels in a SVM
    Preview .
  • 3.6 Classifying Images Using Support Vector Machines
    Preview .
  • 3.7 Assignment – Start Image Classifying Using Support Vector Machines
    Preview .
Section 4: Improving Model Accuracy
6 Lessons
  • 4.1 Review of Classifying Images Using Support Vector Machines
    Preview .
  • 4.2 Model Evaluation
    Preview .
  • 4.3 Better Measures than Accuracy
    Preview .
  • 4.4 Understanding the Results
    Preview .
  • 4.5 Improving the Models
    Preview .
  • 4.6 Assignment – Getting Better Test Sample Results by Measuring Model Performance
    Preview .
Section 5: Finding Patterns and Structures in Unlabeled Data
6 Lessons
  • 5.1 Review of Getting Better Test Sample Results by Measuring Model Performance
    Preview .
  • 5.2 Unsupervised Learning
    Preview .
  • 5.3 Clustering
    Preview .
  • 5.4 K-means Clustering
    Preview .
  • 5.5 Determining the Number of Clusters
    Preview .
  • 5.6 Assignment – Write Your Own Clustering Implementation for Customer Segmentation
    Preview .
Section 6: Sentiment Analysis Using Neural Networks
6 Lessons
  • 6.1 Review of Clustering Customers Together
    Preview .
  • 6.2 Why Neural Network
    Preview .
  • 6.3 Parts of a Neural Network
    Preview .
  • 6.4 Working of a Neural Network
    Preview .
  • 6.5 Improving the Network
    Preview .
  • 6.6 Assignment – Build a Sentiment Analyzer Based on Social Network Using ANN
    Preview .
Section 7: Mastering Kaggle Titanic Competition Using Random Forest
6 Lessons
  • 7.1 Review of Building a Sentiment Analyser ANN
    Preview .
  • 7.2 Decision Trees
    Preview .
  • 7.3 Working of a Decision Tree
    Preview .
  • 7.4 Techniques to Further Improve a Model
    Preview .
  • 7.5 Random Forest as an Improved Machine Learning Approach
    Preview .
  • 7.6 Weekend Task – Solving Titanic Problem Using Random Forest
    Preview .
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About instructor
Includes:
  • 2 hrs On demand videos
  • 43 Lessons
  • Access on mobile and tv
  • Full lifetime access