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The Fun and Easy Guide to Machine Learning using Keras

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The Fun and Easy Guide to Machine Learning using Keras, available at $19.99, has an average rating of 3.85, with 50 lectures, 1 quizzes, based on 141 reviews, and has 1860 subscribers.

You will learn about You will learn the fundamentals of the main Machine Learning Algorithms and how they work on an Intuitive level. We teach you these algorithms without boring you with the complex mathematics and equations. You will learn how to implement these algorithms in Python using sklearn and numpy. You will learn how to implement neural networks using the h2o package You will learn to implement some of the most common Deep Learning algorithms in Keras Build an arsenal of powerful Machine Learning models and how to use them to solve any problem. You will learn to Automate Manual Data Analysis Tasks. This course is ideal for individuals who are Student who starting out or interested in Machine Learning or Deep Learning. or Students with Prior Python Programming Exposure Who Want to Use it for Machine Learning or Students interested in gaining exposure to the Keras library for Deep Learning. or Data analysts who want to expand into Machine Learning. or College students who want to start a career in Data Science. It is particularly useful for Student who starting out or interested in Machine Learning or Deep Learning. or Students with Prior Python Programming Exposure Who Want to Use it for Machine Learning or Students interested in gaining exposure to the Keras library for Deep Learning. or Data analysts who want to expand into Machine Learning. or College students who want to start a career in Data Science.

Enroll now: The Fun and Easy Guide to Machine Learning using Keras

Summary

Title: The Fun and Easy Guide to Machine Learning using Keras

Price: $19.99

Average Rating: 3.85

Number of Lectures: 50

Number of Quizzes: 1

Number of Published Lectures: 50

Number of Published Quizzes: 1

Number of Curriculum Items: 51

Number of Published Curriculum Objects: 51

Original Price: $199.99

Quality Status: approved

Status: Live

What You Will Learn

  • You will learn the fundamentals of the main Machine Learning Algorithms and how they work on an Intuitive level.
  • We teach you these algorithms without boring you with the complex mathematics and equations.
  • You will learn how to implement these algorithms in Python using sklearn and numpy.
  • You will learn how to implement neural networks using the h2o package
  • You will learn to implement some of the most common Deep Learning algorithms in Keras
  • Build an arsenal of powerful Machine Learning models and how to use them to solve any problem.
  • You will learn to Automate Manual Data Analysis Tasks.

Who Should Attend

  • Student who starting out or interested in Machine Learning or Deep Learning.
  • Students with Prior Python Programming Exposure Who Want to Use it for Machine Learning
  • Students interested in gaining exposure to the Keras library for Deep Learning.
  • Data analysts who want to expand into Machine Learning.
  • College students who want to start a career in Data Science.

Target Audiences

  • Student who starting out or interested in Machine Learning or Deep Learning.
  • Students with Prior Python Programming Exposure Who Want to Use it for Machine Learning
  • Students interested in gaining exposure to the Keras library for Deep Learning.
  • Data analysts who want to expand into Machine Learning.
  • College students who want to start a career in Data Science.

Welcome to the Fun and Easy Machine learning Course in Python and Keras.
 

Are you Intrigued by the field of Machine Learning? Then this course is for you! We will take you on an adventure into the amazing of field Machine Learning. Each section consists of fun and intriguing white board explanations with regards to important concepts in Machine learning as well as practical python labs which you will enhance your comprehension of this vast yet lucrative sub-field of Data Science.

So Many Machine Learning Courses Out There, Why This One?

This is a valid question and the answer is simple. This is the ONLY course on Udemy which will get you implementing some of the most common machine learning algorithms on real data in Python. Plus, you will gain exposure to neural networks (using the H2o framework) and some of the most common deep learning algorithms with the Keraspackage.

We designed this course for anyone who wants to learn the state of the art in Machine learning in a simple and fun way without learning complex math or boring explanations.  Each theoretically lecture is uniquely designed using whiteboard animations which can maximize engagement in the lectures and improves knowledge retention. This ensures that you absorb more content than you would traditionally would watching other theoretical videos and or books on this subject.

What you will Learn in this Course

This is how the course is structured:

  • Regression – Linear Regression, Decision Trees, Random Forest Regression,

  • Classification – Logistic Regression, K Nearest Neighbors (KNN), Support Vector Machine (SVM) and Naive Bayes,

  • Clustering – K-Means, Hierarchical Clustering,

  • Association Rule Learning– Apriori, Eclat,

  • Dimensionality Reduction – Principle Component Analysis, Linear Discriminant  Analysis,

  • Neural Networks – Artificial Neural Networks, Convolution Neural Networks, Recurrent Neural Networks.

Practical Lab Structure
You DO NOT need any prior Python or Statistics/Machine Learning Knowledge to get Started. The course will start by introducing students to one of the most fundamental statistical data analysis models and its practical implementation in Python- ordinary least squares (OLS) regression. Subsequently some of the most common machine learning regression and classification techniques such as random forests, decision trees and linear discriminant analysis will be covered. In addition to providing a theoretical foundation for these, hands-on practical labs will demonstrate how to implement these in Python. Students will also be introduced to the practical applications of common data miningtechniques in Python and gain proficiency in using a powerful Python based framework for machine learning which isAnaconda (Python Distribution). Finally you will get a solid grounding in both Artificial Neural Networks (ANN) and the Keras package for implementing deep learning algorithms such as the Convolution Neural Network(CNN). Deep Learning is an in-demand topic and a knowledge of this will make you more attractive to employers.

Excited Yet?

So as you can see you are going to be learning to build a lot of impressive Machine Learning apps in this 3 hour course. The underlying motivation for the course is to ensure you can apply Python based data science on real data into practice today. Start analyzing  data for your own projects, whatever your skill level and IMPRESS your potential employers with an actual examples of your  machine learning abilities.

It is a practical, hands-on course, i.e. we will spend some time dealing with some of the theoretical concepts related to data science. However, majority of the course will focus on implementing different  techniques on real data and interpret the results. After each video you will learn a new concept or technique which you may apply to your own projects.

TAKE ACTION TODAY! We will personally support you and ensure your experience with this course is a success.And for any reason you are unhappy with this course, Udemy has a 30 day Money Back Refund Policy, So no questions asked, no quibble and no Risk to you. You got nothing to lose. Click that enroll button and we’ll see you in side the course.

Course Curriculum

Chapter 1: Introduction

Lecture 1: Introduction

Chapter 2: Setting up your Python Integrated Development Environment (IDE) for Course Labs

Lecture 1: Download and Install Python Anaconda Distribution

Lecture 2: "Hello World" in Jupyter Notebook

Lecture 3: Installation for Mac Users

Lecture 4: Datasets, Python Notebooks and Scripts For the Course

Chapter 3: =======Regression=======

Lecture 1: Regression

Chapter 4: Linear Regression

Lecture 1: Linear Regression – Theory

Lecture 2: Linear Regression – Practical Labs

Chapter 5: Decision Tree – Classification and Regression Trees

Lecture 1: Decision Tree – Theory

Lecture 2: Decision Tree – Practical Labs

Chapter 6: Random Forests

Lecture 1: Random Forest – Theory

Lecture 2: Random Forest Practical Labs

Chapter 7: =======Classification=======

Lecture 1: Classification

Chapter 8: Logistic Regression

Lecture 1: Logistic Regression – Theory

Lecture 2: Logistic Regression Classification – Practical Labs

Chapter 9: K Nearest Neighbors

Lecture 1: K -Nearest Neighbors – Theory

Lecture 2: KNN Classification – Practical Labs

Chapter 10: Support Vector Machines (SVM)

Lecture 1: Support Vector Machine -Theory

Lecture 2: Linear SVM – Practical Labs

Lecture 3: Non Linear SVM – Practical Labs

Chapter 11: Naive Bayes

Lecture 1: Naive Bayes – Theory

Lecture 2: Naive Bayes – Practical Labs

Chapter 12: =======Clustering=======

Lecture 1: Clustering

Chapter 13: K – Means Clustering

Lecture 1: K – Means Clustering

Lecture 2: K – Means Clustering – Practical Labs Part A

Lecture 3: K – Means Clustering – Practical Labs Part B

Chapter 14: Hierarchical Clustering

Lecture 1: Hierarchical Clustering – Theory

Lecture 2: Hierarchical clustering – Practical Labs

Lecture 3: Review Lecture

Chapter 15: =======Association Rule Learning=======

Lecture 1: Associated Rule Learning

Chapter 16: Eclat and Apior

Lecture 1: Apriori

Lecture 2: Apriori – Practical Labs

Lecture 3: Eclat – Theory

Lecture 4: Eclat Practical Labs

Chapter 17: =======Dimensionality Reduction=======

Lecture 1: Dimensionality Reduction

Chapter 18: Principal Component Analysis

Lecture 1: Principal Component Analysis – Theory

Lecture 2: PCA – Practical Labs

Chapter 19: Linear Discriminant Analysis LDA

Lecture 1: Linear Discriminant Analysis – Theory

Lecture 2: Linear Discriminant Analysis LDA – Practical Labs

Chapter 20: =======Neural Networks=======

Lecture 1: Artificial Neural Networks

Chapter 21: Artificial Neural Networks

Lecture 1: Artificial Neural Networks – Theory

Lecture 2: ANN-perceptron – Practical Labs A

Lecture 3: ANN Perceptron – Practical Labs_B

Lecture 4: ANN MLC – Practical Labs_C

Chapter 22: Convolutional Neural Networks

Lecture 1: Convolutional Neural Networks – Theory

Lecture 2: Convolution Neural Networks – Practical Labs

Chapter 23: Recurrent Neural Networks

Lecture 1: Recurrent Neural Networks – Theory

Lecture 2: Recurrent Neural Networks – Practical Labs

Chapter 24: Conclusion and Bonus Section

Lecture 1: Conclusion

Lecture 2: Little something for our Students

Instructors

Rating Distribution

  • 1 stars: 8 votes
  • 2 stars: 20 votes
  • 3 stars: 30 votes
  • 4 stars: 32 votes
  • 5 stars: 51 votes

Frequently Asked Questions


How long do I have access to the course materials?


You can view and review the lecture materials indefinitely, like an on-demand channel.


Can I take my courses with me wherever I go?


Definitely! If you have an internet connection, courses on Udemy are available on any device at any time. If you don’t have an internet connection, some instructors also let their students download course lectures. That’s up to the instructor though, so make sure you get on their good side!

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