Machine Learning with Python for Dummies The Complete Guide

By Abhilash Nelson, Senior IT Consultant

Language: English

This course is for you

Machine Learning and Data Science for programming beginners using python with scikit-learn, SciPy, Matplotlib & Pandas. Hello and welcome to my new course, Machine Learning with Python for Dummies. We will discuss about the overview of the course and the contents included in this course. Artificial Intelligence, Machine Learning  and Deep Learning Neural Networks are the most used terms now a days in the technology world. Its also the most mis-understood and confused terms too. Artificial Intelligence is a broad spectrum of science which tries to make machines intelligent like humans. Machine Learning and Neural Networks are two subsets that comes under this vast machine learning platform Lets check what's machine learning now. Just like we human babies, we were actually in our learning phase then. We learned how to crawl, stand, walk, then speak words, then make simple sentences.. We learned from our experiences. We had many trials and errors before we learned how to walk and talk. The best trials for walking and talking which gave positive results were kept in our memory and made use later. This process is highly compared to a Machine Learning Mechanism Then we grew young and started thinking logically about many things, had emotional feelings, etc. We kept on thinking and found solutions to problems in our daily life. That's what the Deep Learning Neural Network Scientists are trying to achieve. A thinking machine. But in this course we are focusing mainly in Machine Learning. Throughout this course, we are preparing our machine to make it ready for a prediction test. Its Just like how you prepare for your Mathematics Test in school or college.  We learn and train ourselves by solving the most possible number of similar mathematical problems. Lets call these sample data of similar problems and their solutions as the 'Training Input' and 'Training Output' Respectively. And then the day comes when we have the actual test. We will be given new set of problems to solve, but very similar to the problems we learned, and based on the previous practice and learning experiences, we have to solve them. We can call those problems as 'Testing Input' and our answers as 'Predicted Output'. Later, our professor will evaluate these answers and compare it with its actual answers, we call the actual answers as 'Test Output'. Then a mark will be given on basis of the correct answers. We call this mark as our 'Accuracy'. The life of a machine learning engineer and a data-scientist is dedicated to make this accuracy as good as possible through different techniques and evaluation measures. We are using Python as our programming language. Python is a great tool for the development of programs which perform data analysis and prediction. It has tons of classes and features which perform the complex mathematical analysis and give solutions in simple one or two lines of code so that we don't have to be a statistic genius or mathematical Nerd to learn data science and machine learning. Machine Learning and Data Science is the most lucrative job in the technology arena now a days. Learning this course will make you equipped to compete in this area. Best wishes with your learning. See you soon in the class room.

Course overview - 32

  • Introduction to Machine Learning

  • System and Environment Preparation

  • Learn Basics of Python

  • Learn Basics of NumPy

  • Learn Basics of Matplotlib

  • Learn Basics of Pandas

  • Understanding the CSV Data File

  • Load and Read CSV data file using Python Standard Library

  • Load and Read CSV Data File Using NumPy

  • Load and Read CSV Data File Using Pandas

  • Dataset Summary

  • Dataset Visualization

  • Multivariate Dataset Visualization

  • Data Preparation (Pre-Processing)

  • Data Preparation - Standardizing Data

  • Feature Selection

  • Refresher Session - The Mechanism of Re-sampling, Training and Testing

  • Algorithm Evaluation Techniques

  • Algorithm Evaluation Metrics

  • Classification Algorithm Spot Check

  • Regression Algorithm Spot Check

  • Compare Algorithms

  • Pipelines Data Preparation and Data Modelling

  • Performance Improvement Ensembles

  • Performance Improvement Parameter Tuning

  • Export, Save and Load Machine Learning Models

  • Finalizing a Model

  • Quick Session Imbalanced Data Set - Issue Overview and Steps

  • Iris Dataset Finalizing Multi-Class Dataset

  • Finalizing a Regression Model - The Boston Housing Price Dataset

  • Real-Time Predictions

  • Source Code

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Meet your instructor

Abhilash Abhilash
Abhilash NelsonSenior IT Consultant
I am a pioneering security-oriented Android/iOS Mobile and PHP/Python Web Developer Application Developer offering more than eight years’ overall IT experience which involves designing, implementing, integrating, testing and supporting impact-full web and mobile applications. I am a Post Graduate Masters Degree holder in Computer Science and Engineering. My experience with PHP/Python Programming is an added advantage for server based Android and iOS Client Applications.

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