Welcome to the Fun and Easy Machine learning Course in Python and Keras. Are youIntrigued by the field of Machine Learning? Then this course is for you! We will take you on an adventureinto the amazingof field Machine Learning. Each section consists offun and intriguingwhite board explanationswith regards to important conceptsin Machine learning as well aspractical python labs which you will enhance your comprehension of this vastyet 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 animationswhich can maximizeengagement in the lectures and improves knowledge retention. This ensures that you absorb morecontentthan you would traditionally would watching other theoreticalvideos and or books on this subject. What you willLearn 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.PracticalLab 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 alsobe introduced to the practical applications of common data mining techniques in Python andgain 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 Learningappsin 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 andIMPRESSyour potential employers with an actual examples of your machine learning abilities.It is apractical, 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 mayapply to your own projects.TAKE ACTION TODAY! We will personally support you and ensure your experience with thiscourse 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.