The Fun And Easy Guide To Machine Learning Using Keras

Learn 16 Machine Learning Algorithms in a Fun and Easy along with Practical Python Labs using Keras

What you’ll 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.

Requirements
PC/ Laptop to implement the Practical Labs, running Windows or Mac.
High school knowledge in mathematics.
Willingness to Learn and Open Mind.
Background in engineering, data science, computer science and statistics is recommended (but not a requirement)
Basic Python or Programming Background recommended (but not a requirement).

Description
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 Keras package. 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 CourseThis 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 StructureYou 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 mining techniques in Python and gain proficiency in using a powerful Python based framework for machine learning which is Anaconda (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.

Overview
Section 1: Introduction

Lecture 1 Introduction

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

Lecture 2 Download and Install Python Anaconda Distribution

Lecture 3 “Hello World” in Jupyter Notebook

Lecture 4 Installation for Mac Users

Lecture 5 Datasets, Python Notebooks and Scripts For the Course

Section 3: =======Regression=======

Lecture 6 Regression

Section 4: Linear Regression

Lecture 7 Linear Regression – Theory

Lecture 8 Linear Regression – Practical Labs

Section 5: Decision Tree – Classification and Regression Trees

Lecture 9 Decision Tree – Theory

Lecture 10 Decision Tree – Practical Labs

Section 6: Random Forests

Lecture 11 Random Forest – Theory

Lecture 12 Random Forest Practical Labs

Section 7: =======Classification=======

Lecture 13 Classification

Section 8: Logistic Regression

Lecture 14 Logistic Regression – Theory

Lecture 15 Logistic Regression Classification – Practical Labs

Section 9: K Nearest Neighbors

Lecture 16 K -Nearest Neighbors – Theory

Lecture 17 KNN Classification – Practical Labs

Section 10: Support Vector Machines (SVM)

Lecture 18 Support Vector Machine -Theory

Lecture 19 Linear SVM – Practical Labs

Lecture 20 Non Linear SVM – Practical Labs

Section 11: Naive Bayes

Lecture 21 Naive Bayes – Theory

Lecture 22 Naive Bayes – Practical Labs

Section 12: =======Clustering=======

Lecture 23 Clustering

Section 13: K – Means Clustering

Lecture 24 K – Means Clustering

Lecture 25 K – Means Clustering – Practical Labs Part A

Lecture 26 K – Means Clustering – Practical Labs Part B

Section 14: Hierarchical Clustering

Lecture 27 Hierarchical Clustering – Theory

Lecture 28 Hierarchical clustering – Practical Labs

Lecture 29 Review Lecture

Section 15: =======Association Rule Learning=======

Lecture 30 Associated Rule Learning

Section 16: Eclat and Apior

Lecture 31 Apriori

Lecture 32 Apriori – Practical Labs

Lecture 33 Eclat – Theory

Lecture 34 Eclat Practical Labs

Section 17: =======Dimensionality Reduction=======

Lecture 35 Dimensionality Reduction

Section 18: Principal Component Analysis

Lecture 36 Principal Component Analysis – Theory

Lecture 37 PCA – Practical Labs

Section 19: Linear Discriminant Analysis LDA

Lecture 38 Linear Discriminant Analysis – Theory

Lecture 39 Linear Discriminant Analysis LDA – Practical Labs

Section 20: =======Neural Networks=======

Lecture 40 Artificial Neural Networks

Section 21: Artificial Neural Networks

Lecture 41 Artificial Neural Networks – Theory

Lecture 42 ANN-perceptron – Practical Labs A

Lecture 43 ANN Perceptron – Practical Labs_B

Lecture 44 ANN MLC – Practical Labs_C

Section 22: Convolutional Neural Networks

Lecture 45 Convolutional Neural Networks – Theory

Lecture 46 Convolution Neural Networks – Practical Labs

Section 23: Recurrent Neural Networks

Lecture 47 Recurrent Neural Networks – Theory

Lecture 48 Recurrent Neural Networks – Practical Labs

Section 24: Conclusion and Bonus Section

Lecture 49 Conclusion

Lecture 50 Little something for our Students

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.

Password/解压密码www.tbtos.com

转载请注明：0daytown » The Fun And Easy Guide To Machine Learning Using Keras