Udemy

Machine Learning Made Easy : Beginner to Expert using Python

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  • 230 Students
  • Updated 1/2019
  • Certificate Available
4.2
(40 Ratings)
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Course Information

Registration period
Year-round Recruitment
Course Level
Study Mode
Duration
12 Hour(s) 43 Minute(s)
Language
English
Taught by
Venkata Reddy AI Classes
Certificate
  • Available
  • *The delivery and distribution of the certificate are subject to the policies and arrangements of the course provider.
Rating
4.2
(40 Ratings)
1 views

Course Overview

Machine Learning Made Easy : Beginner to Expert using Python

Learn Machine Learning Algorithms using Python from experts with hands on examples, practice sessions and projects.

Want to know how Machine Learning algorithms work and how people apply it to solve data science problems? You are looking at right course!      

This course has been created, designed and assembled by professional Data Scientists who have worked in this field for nearly a decade. We can help you understand the complex machine learning algorithms while keeping you grounded to the implementation on real business and data science problems.   

We will let you feel the water and coach you to become a full swimmer in the realm of data science and Machine Learning. Every tutorial will increase your skill level by challenging your ability to foresee, yet letting you improve upon self.   

We are sure that you will have fun while learning from our tried and tested structure of course to keep you interested in what’s coming next.   

Here is how the course is going to work:   

  • Part 1      – Introduction to Python Programming. 

    • This is the part where you will learn basic of python programming and familiarize yourself with Python environment. 

    • Be able to import, export, explore, clean and prepare the data for advance modeling. 

    • Understand the underlying statistics of data and how to report/document the insights. 

  • Part 2      – Machine Learning using Python 

    • Learn, upgrade and become expert on classic machine learning algorithms like Linear Regression, Logistic Regression and Decision Trees. 

    • Learn which algorithm to choose for specific problem, build multiple model, learn how to choose the best model and be able to improve upon it. 

    • Move on to advance machine learning algorithms like SVM, Artificial Neural Networks, Reinforced Learning, Random Forests and Boosting

Features:   

  • Fully packed with LAB Sessions. One to learn from and one for you  to do it yourself.   

  • Course includes Python source code, Datasets and other supporting material at the beginning of each section for you to download and use on your own.

  • Quiz after each section to test your learning.


Bonus:   

  • This course is packed with 5 projects on real data related to different domains to prepare you for wide variety of business problems.

  • These projects will serve as your step by step guide to solve different business and data science problems.

Course Content

  • 11 section(s)
  • 130 lecture(s)
  • Section 1 Introduction to Python Programming
  • Section 2 Data Handling in Python
  • Section 3 Descriptive Statistics Plots
  • Section 4 Data Cleaning and Treatement
  • Section 5 Linear Regression
  • Section 6 Logistic Regression
  • Section 7 Decision Trees
  • Section 8 Model Selection and Cross Validation
  • Section 9 Neural Networks
  • Section 10 SVM
  • Section 11 Random Forest and Boosting

What You’ll Learn

  • Python Programming, Data Handling and Cleaning, Basic Statistics, Classical ML Algorithms, Model Selection & Validation, Advanced ML Algorithms.
  • Write your own Python scripts and work in Python Environment.
  • Import, manipulate, clean up, sanitize and export datasets.
  • Understand basic statistics and implement using Python.
  • Understand data science life cycle while understanding steps of building, validating, improving and implementing the machine learning models.
  • Do powerful analysis on data, find insights and present them in visual manner.
  • Know how each machine learning algorithm works and which one to choose according to the type of problem.
  • Build more than one powerful machine learning model and be able to select the best one and improve it further.


Reviews

  • D
    Dishita Sharma
    2.5

    I am just having a hard time understanding everything. I think adding more structure to the videos might help.

  • D
    Debendra Das
    5.0

    Really good

  • D
    Dhevesh Kumar Domun
    1.0

    The videos don't flow well together

  • P
    Praveen Kumar
    4.0

    I am not finding it very much engaging. but yes content is so far good. it would be great if you can make it more engaging and properly finish every video with proper information such as "we will read this and that in next video" and "in the start of video we can give a seconds of overview that so far we are done this and next we are going to learn this". These are the things which can make this course the best.

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