AI News, How do I learn machine learning?
How do I learn machine learning?
Machine learning has vast use cases for different domains either in the e-commerce business, finance, intelligence, sci-fi movies, marketing, real estate etc Amazon, Flipkart or other e-commerce giants recommend books or other products based on your previous purchased.
Ok coming to your question how does a people can learn machine learning, I can divide it in different cases like a person who is purely newbie to this field and other who is intermediary and wants to enhance his/her skills as mastery in this field.
Programming Tools For Machine Learning: Although there are many programming languages that offers machine learning works but i prefer Python, Here i am confirming one thing is i prefere Python because of i have good grasp of Python than R or other programming languages but honestly i can say you that all languages or tools has it own pros and cons but a person who is keen interested in this field always hands on with these programming and tools and always know when and where he/she can uses these tools.
Step 4 Common Machine learning for beginners Remember machine learning involved step 3 in which we talked about model, Model is nothing but machine learning algorithm which is created by training process, Since there are three types of Machine learning 1.
Now we are really ready for hands on with some project.Here i am listing some beginners project name: Books and Online tutorials For starting Machine learning with python you have from basic Python Python book: How to think like a computer scientist Numpy and Pandas: Python for Data Analysis by Wes McKinney and also practice Pandas with 10 minutes.
A Complete Machine Learning Project Walk-Through in Python: Part One
Here we are using the seaborn visualization library and the PairGrid function to create a Pairs Plot with scatterplots on the upper triangle, histograms on the diagonal, and 2D kernel density plots and correlation coefficients on the lower triangle.
machine learning model can only learn from the data we provide it, so ensuring that data includes all the relevant information for our task is crucial.
Taking the square root, natural log, or various powers of features is common practice in data science and can be based on domain knowledge or what works best in practice.
The following code selects the numeric features, takes log transformations of these features, selects the two categorical features, one-hot encodes these features, and joins the two sets together.
Features that are strongly correlated with each other are known as collinear and removing one of the variables in these pairs of features can often help a machine learning model generalize and be more interpretable.
(I should point out we are talking about correlations of features with other features, not correlations with the target, which help our model!) There are a number of methods to calculate collinearity between features, with one of the most common the variance inflation factor.
For regression problems, a reasonable naive baseline is to guess the median value of the target on the training set for all the examples in the test set.
Before calculating the baseline, we need to split our data into a training and a testing set: We will use 70% of the data for training and 30% for testing: Now we can calculate the naive baseline performance: The naive estimate is off by about 25 points on the test set.
The second post (available here) will show how to evaluate machine learning models using Scikit-Learn, select the best model, and perform hyperparameter tuning to optimize the model.
7 Steps to Mastering Machine Learning With Python
This post aims to take a newcomer from minimal knowledge of machine learning in Python all the way to knowledgeable practitioner in 7 steps, all while using freely available materials and resources along the way.
Fortunately, due to its widespread popularity as a general purpose programming language, as well as its adoption in both scientific computing and machine learning, coming across beginner's tutorials is not very difficult.
If you have no knowledge of programming, my suggestion is to start with the following free online book, then move on to the subsequent materials: If you have experience in programming but not with Python in particular, or if your Python is elementary, I would suggest one or both of the following: And for those looking for a 30 minute crash course in Python, here you go: Of course, if you are an experienced Python programmer you will be able to skip this step.
Gaining an intimate understanding of machine learning algorithms is beyond the scope of this article, and generally requires substantial amounts of time investment in a more academic setting, or via intense self-study at the very least.
The good news is that you don't need to possess a PhD-level understanding of the theoretical aspects of machine learning in order to practice, in the same manner that not all programmers require a theoretical computer science education in order to be effective coders.
For example, when you come across an exercise implementing a regression model below, read the appropriate regression section of Ng's notes and/or view Mitchell's regression videos at that time.
good approach to learning these is to cover this material: This pandas tutorial is good, and to the point: You will see some other packages in the tutorials below, including, for example, Seaborn, which is a data visualization library based on matplotlib.
Step by step approach to perform data analysis using Python
So you have decided to learn Python, but you don’t have prior programming experience.
“How long does it take to learn Python” “How much Python should I learn for performing data analysis” “What are the best books/courses to learn Python” “Should I be an expert Python programmer, in order to work with data sets” It is good to be confused, while beginning to learn a new skill, that’s what author of “learn anything in 20 hours” says.
For 3 months(spending 3 hours per day), I was learning Python programming by completing small software projects.
After a few hours of research, I found out that I need to learn 5 Python libraries to effectively solve a broad set of data analysis problems.
Ignore the resources intended for general audience While there are many excellent Python books and online courses, I wouldn’t recommend some of them as they are intended for a general audience rather than for some one who wants to do data analysis.
After completing the code academy exercises go through this I python notebook: Python Essentials Tutorials (I have provided the links to download the file in conclusion part) It consists of concepts that are not covered in code academy.You can complete this tutorial within an hour or two.
The tutorial covers the most frequently performed operations in Numpy, such as, working with N-dimensional array, Indexing and slicing of arrays, Indexing using integer arrays, transposing an array, universal functions, data processing using arrays, frequently used statistical methods, etc.
MatplotLib Part 1 2nd part: Covers how to control the style and color of a figure, such as markers, line thickness, line patterns and using color maps.
Your First Machine Learning Project in Python Step-By-Step
Do you want to do machine learning using Python, but you’re having trouble getting started?
In this step-by-step tutorial you will: If you are a machine learning beginner and looking to finally get started using Python, this tutorial was designed for you.
The best way to learn machine learning is by designing and completing small projects.
machine learning project may not be linear, but it has a number of well known steps: The best way to really come to terms with a new platform or tool is to work through a machine learning project end-to-end and cover the key steps.
You can fill in the gaps such as further data preparation and improving result tasks later, once you have more confidence.
The best small project to start with on a new tool is the classification of iris flowers (e.g.
The scipy installation page provides excellent instructions for installing the above libraries on multiple different platforms, such as Linux, mac OS X and Windows.
recommend working directly in the interpreter or writing your scripts and running them on the command line rather than big editors and IDEs.
If you do have network problems, you can download the iris.csv file into your working directory and load it using the same method, changing URL to the local file name.
In this step we are going to take a look at the data a few different ways: Don’t worry, each look at the data is one command.
We can get a quick idea of how many instances (rows) and how many attributes (columns) the data contains with the shape property.
We are going to look at two types of plots: We start with some univariate plots, that is, plots of each individual variable.
This gives us a much clearer idea of the distribution of the input attributes: We can also create a histogram of each input variable to get an idea of the distribution.
We also want a more concrete estimate of the accuracy of the best model on unseen data by evaluating it on actual unseen data.
That is, we are going to hold back some data that the algorithms will not get to see and we will use this data to get a second and independent idea of how accurate the best model might actually be.
We will split the loaded dataset into two, 80% of which we will use to train our models and 20% that we will hold back as a validation dataset.
This will split our dataset into 10 parts, train on 9 and test on 1 and repeat for all combinations of train-test splits.
The specific random seed does not matter, learn more about pseudorandom number generators here: We are using the metric of ‘accuracy‘
This is a ratio of the number of correctly predicted instances in divided by the total number of instances in the dataset multiplied by 100 to give a percentage (e.g.
We get an idea from the plots that some of the classes are partially linearly separable in some dimensions, so we are expecting generally good results.
We reset the random number seed before each run to ensure that the evaluation of each algorithm is performed using exactly the same data splits.
We can also create a plot of the model evaluation results and compare the spread and the mean accuracy of each model.
There is a population of accuracy measures for each algorithm because each algorithm was evaluated 10 times (10 fold cross validation).
It is valuable to keep a validation set just in case you made a slip during training, such as overfitting to the training set or a data leak.
We can run the KNN model directly on the validation set and summarize the results as a final accuracy score, a confusion matrix and a classification report.
Finally, the classification report provides a breakdown of each class by precision, recall, f1-score and support showing excellent results (granted the validation dataset was small).
You can learn about the benefits and limitations of various algorithms later, and there are plenty of posts that you can read later to brush up on the steps of a machine learning project and the importance of evaluating accuracy using cross validation.
You discovered that completing a small end-to-end project from loading the data to making predictions is the best way to get familiar with a new platform.
- On Tuesday, January 21, 2020
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