Learn Data Science for FREE with Machine Learning Mastery

Key Takeaways

do you want to learn machine learning do you want to learn it using python do you want to follow a guided tutorial that will provide you the step-by-step necessary to build your machine learning models and also description of the available options and functionality of the various components necessary for machine learning model building if you answered yes then you want to watch this video to the end because i'm going to share with you a very powerful website that you could use to jump start your machine learning journey and so without further ado we're starting right now and so the machine learning resource that we're going to be talking about today is called the machine learning mastery and so the contents on the machine learning mastery website provides you small bite-sized tutorials that you could easily implement into your own existing projects and so whenever i get stuck aside from going to step overflow i might also give this website a general browse in order to find some inspiration about which function to use how to approach some of the machine learning problems and so this website provides a very good resources for you for doing that and so let's have a look at the functionality provided by this website so upon arriving at the website you're going to be showing the logo of the machine learning mastery and the slogan of this website and also a picture of dr jason brownlee and if you're interested you could also click here to get your free ebook and you have to provide your email and he will provide you some interesting tips in his email list and there are three major components here quick start guide so this will get you started immediately latest tutorial so he will be releasing tutorials every day or every other day and he will be posting that on the linkedin on twitter and on facebook okay and contents from the tutorials will be categorized into the various categories headings here that you see and so if you like what you see then you could also purchase some of the ebooks that dr jason has written and there are about 19 books that he has already written so far on various topics in machine learning and also deep learning okay so let's click on the quick start guides and so here it is nicely divided into four sections so the first section is the foundation so if you're first starting out then it's recommending you how do you get started and he's going to tell you the step-by-step process and then there will be an article about probability about statistical methods about linear algebra so let's have a look at the how do i get started okay so here he's providing you some of the tips on how you could get started so step one you have to adjust your mindset step two you pick a process and then step three you pick a tool whether you would like to have a no code approach using weka or if you would like to have some intermediate or advanced and he's saying here that python is more of an intermediate and advanced is the r platform and then he discusses about how you could practice on dataset and then how you could build your portfolio okay some pretty solid advice on getting started and then there will be other in the machine learning process defining the problem preparing the data spot check the algorithms improve the result present the results and the probability for machine learning and then there will be various articles in the topics of probability and then for statistics there will be several topics as mentioned here and in linear algebra as well so he has several articles written here and then if you would like to get a general understanding of the underlying basis of the machine learning algorithms then check out this so here he provides a tour of machine learning algorithms and then it provides you some information about overfitting under fitting and then explanation going in-depth into each of the linear algorithms non-linear algorithms ensemble algorithms okay and if you prefer a non-coding approach then you could have a look at wika and he also has several articles about that even a book about that as well and python machine learning using psychic learn so several articles about that and also machine learning using r particularly using the carrot package so not to be confused with the pie carrot so pie carrot is a package in python and it is not used in carrot but it is using scikit-learn xc boost and cat boost but the name just resembles carrot okay and then he has some code algorithm from scratch where he's going to show you how to use python in order to implement various machine learning algorithms from scratch and he also has a section about time series forecasting data preparation how to implement xt boost how to handle imbalanced data sets when you have higher number of samples in one class versus the other class okay and how you could use keras to do deep learning and talking about better deep learning performance long short term memory networks deep learning for natural language processing word embedding maps of words model language modeling photo captioning text translation text summarization text classification and also using deep learning for computer vision covers many topics image data handling image data augmentation image classification object recognition the basics of cnn image data preparation showing you how to use deep learning for time series forecasting and also having a section about generative adversarial networks all right so that's pretty much it for the getting started so let's head over back and then let's have a look at the latest tutorial so if you would like to have a look at the top tutorials that he has written it is right here how to install everything your first complete project your first neural network okay so you can see here that he has published this recently july 27 on the leave one outcross validation for evaluating machine learning algorithms and on 24th of july train test split for evaluating machine learning algorithms on 22nd of july how to selectively scale numerical input variables for machine learning so i would normally browse this section of the website to see what interesting techniques or functions that we could use for our machine learning projects and so the thing is there is no expectation of having to learn things at the spot but just by browsing casually then you will learn a couple of things okay so he'll release a video in a couple of days about three to four days in between the articles okay so let's have a look at the ebooks that he has written okay so he has written some beginner friendly ebooks linear algebra for machine learning statistical methods for machine learning probability for machine learning master machine learning algorithms machine learning algorithms from scratch so let's have a look at the cost of the books here so it's worthy to note that this video is not sponsored by machine learning mastery and so today we're covering machine learning mastery because i truly believe it is a very great resource for learning machine learning first book here is 27 statistical methods for machine learning let's have a look and this is also 27 dollars okay and let's have a look at the intermediate books so there's machine learning mastery with wika and so it will also provide you all of the code all of the necessary data to follow step by step in his tutorial machine learning mastery with python machine learning mastery with r time series forecasting imbalance classification data preparation so if you have done the beginner's level and the intermediate level then i believe that you could pretty much tackle a lot of the machine learning tasks and for the advanced topics it is predominantly based on deep learning and so here we have deep learning with python and so he's going to be using thiano and keras deep learning for computer vision generative adversarial network how to have better deep learning how to make better predictions train faster and reduce overfitting lstm network with python deep learning for time series forecasting so they're predominantly based on the topics that he has already provided for free and if you would like to have a deeper treatment into the topics then it is recommended that you also purchase the book so for one thing to help the author out and that he could use that extra resource to write more books that are helping all of us in our data science journey okay and if you buy the books in bundles you'll be getting some discount here as well so if you buy the beginner bundle 34 discount so if you would like to buy everything it's going to be called the super bundle and it will be comprising of 19 books and at a discount of 31 and so all of the 19 books will be costing you 467 so on average it will cost about 20 something dollars so considering that all of the books comes with actionable code that you could actually use to learn and refer to i would say this is a very good deal okay but don't take my word for it and so he has some testimonials and if you don't like the book then you could ask him for a 100 money-back guarantee so there's no risk to this okay and he also updates us in twitter and linkedin and also facebook so if you're finding value in this video please give it a thumbs up subscribe if you haven't yet done so hit on the notification bell in order to be notified of the next video and as always the best way to learn data science is to do data science and please enjoy the journey thank you for watching please like subscribe and share and i'll see you in the next one but in the meantime please check out these videos