Google’s self-driving cars and robots get a lot of press, but the company’s real future is in machine learning, the technology that enables computers to get smarter and more personal.

Eric Schmidt (Google Chairman)

We are probably living in the most defining period of human history. The period when computing moved from large mainframes to PCs to the cloud. But what makes it defining is not what has happened but what is coming our way in years to come. What makes this period exciting and enthralling for someone like me is the democratization of the various tools, techniques, and machine learning algorithms that followed the boost in computing. Welcome to the world of data science!

Today, as a data scientist, I can build data-crunching machines with complex algorithms for a few dollars per hour. But reaching here wasn’t easy! I had my dark days and nights.

- Major focus on commonly used machine learning techniques and algorithms.
- Algorithms covered – Linear regression, logistic regression, Naive Bayes, kNN, Random forest, etc.
- Learn both theory and implementation of the machine learning algorithms in R and python.

*Are you a beginner looking for a place to start your data science journey and learn machine learning models? Presenting a list of comprehensive courses, full of knowledge and data science learning, curated just for you to learn data science (using Python) from scratch:*

- Machine Learning Certification Course for Beginners
*Introduction to Data Science*- Certified AI & ML Blackbelt+ Program

**What I am giving out today is probably the most valuable guide I have ever created.**

The idea behind creating this guide is to simplify the journey of aspiring data scientists and machine learning (which is part of artificial intelligence) enthusiasts across the world. Through this guide, I will enable you to work on machine-learning problems and gain from experience. **I am providing a high-level understanding of various machine learning algorithms along with R & Python codes to run them. **These should be sufficient to get your hands dirty. You can also check out our Machine Learning Course.

Essentials of machine learning algorithms with implementation in R and Python

I have deliberately skipped the statistics behind these techniques and artificial neural networks, as you don’t need to understand them initially. So, if you are looking for a statistical understanding of these algorithms, you should look elsewhere. But, if you want to equip yourself to start building a machine learning project, you are in for a treat.

**How it works:** This algorithm consists of a target/outcome variable (or dependent variable) which is to be predicted from a given set of predictors (independent variables). Using this set of variables, we generate a function that maps input data to desired outputs. The training process continues until the model achieves the desired level of accuracy on the training data. Examples of Supervised Learning: Regression, Decision Tree, Random Forest, KNN, Logistic Regression, etc.

**How it works: **In this algorithm, we do not have any target or outcome variable to predict / estimate (which is called unlabelled data). It is used for recommendation systems or clustering populations in different groups. clustering algorithms are widely used for segmenting customers into different groups for specific interventions. Examples of Unsupervised Learning: Apriori algorithm, K-means clustering.

**How it works:** Using this algorithm, the machine is trained to make specific decisions. The machine is exposed to an environment where it trains itself continually using trial and error. This machine learns from past experience and tries to capture the best possible knowledge to make accurate business decisions. Example of Reinforcement Learning: Markov Decision Process

Here is the list of commonly used machine learning algorithms. These algorithms can be applied to almost any data problem:

- Linear Regression
- Logistic Regression
- Decision Tree
- SVM
- Naive Bayes
- kNN
- K-Means
- Random Forest
- Dimensionality Reduction Algorithms
- Gradient Boosting algorithms
- GBM
- XGBoost
- LightGBM
- CatBoost

It is used to estimate real values (cost of houses, number of calls, total sales, etc.) based on a continuous variable(s). Here, we establish the relationship between independent and dependent variables by fitting the best line.

This best-fit line is known as the regression line and is represented by a linear equation** Y= a*X + b.**

The best way to understand linear regression is to relive this experience of childhood. Let us say you ask a child in fifth grade to arrange people in his class by increasing the order of weight without asking them their weights! What do you think the child will do? He/she would likely look (visually analyze) at the height and build of people and arrange them using a combination of these visible parameters. This is linear regression in real life! The child has actually figured out that height and build would be correlated to weight by a relationship, which looks like the equation above.

In this equation:

- Y – Dependent Variable
- a – Slope
- X – Independent variable
- b – Intercept

These coefficients a and b are derived based on minimizing the sum of the squared difference of distance between data points and the regression line.

Look at the below example. Here we have identified the best-fit line having linear equation **y=0.2811x+13.9**. Now using this equation, we can find the weight, knowing the height of a person.

Linear Regression is mainly of two types: Simple Linear Regression and Multiple Linear Regression. Simple Linear Regression is characterized by one independent variable. And, Multiple Linear Regression(as the name suggests) is characterized by multiple (more than 1) independent variables. While finding the best-fit line, you can fit a polynomial or curvilinear regression. And these are known as polynomial or curvilinear regression.

Here’s a coding window to try out your hand and build your own linear regression model:

```
#Load Train and Test datasets
#Identify feature and response variable(s) and values must be numeric and numpy arrays
x_train <- input_variables_values_training_datasets
y_train <- target_variables_values_training_datasets
x_test <- input_variables_values_test_datasets
x <- cbind(x_train,y_train)
# Train the model using the training sets and check score
linear <- lm(y_train ~ ., data = x)
summary(linear)
#Predict Output
predicted= predict(linear,x_test)
```

Don’t get confused by its name! It is a classification algorithm, not a regression algorithm. It is used to estimate discrete values ( Binary values like 0/1, yes/no, true/false ) based on a given set of independent variable(s). In simple words, it predicts the probability of the occurrence of an event by fitting data to a logistic function. Hence, it is also known as **logit regression**. Since it predicts the probability, its output values lie between 0 and 1 (as expected).

Again, let us try and understand this through a simple example.

Let’s say your friend gives you a puzzle to solve. There are only 2 outcome scenarios – either you solve it, or you don’t. Now imagine that you are being given a wide range of puzzles/quizzes in an attempt to understand which subjects you are good at. The outcome of this study would be something like this – if you are given a trigonometry-based tenth-grade problem, you are 70% likely to solve it. On the other hand, if it is a grade fifth history question, the probability of getting an answer is only 30%. This is what Logistic Regression provides you.

Coming to the math, the log odds of the outcome are modeled as a linear combination of the predictor variables.

```
odds= p/ (1-p) = probability of event occurrence / probability of not event occurrence
ln(odds) = ln(p/(1-p))
logit(p) = ln(p/(1-p)) = b0+b1X1+b2X2+b3X3....+bkXk
```

Above, p is the probability of the presence of the characteristic of interest. It chooses parameters that maximize the likelihood of observing the sample values rather than that minimize the sum of squared errors (like in ordinary regression).

Now, you may ask, why take a log? For the sake of simplicity, let’s just say that this is one of the best mathematical ways to replicate a step function. I can go into more details, but that will beat the purpose of this article.

```
x <- cbind(x_train,y_train)
# Train the model using the training sets and check score
logistic <- glm(y_train ~ ., data = x,family='binomial')
summary(logistic)
#Predict Output
predicted= predict(logistic,x_test)
```

**Furthermore…**

There are many different steps that could be tried in order to improve the model:

- including interaction terms
- removing features
- regularization techniques
- using a non-linear model

This is one of my favorite algorithms, and I use it quite frequently. It is a type of supervised learning algorithm that is mostly used for classification problems. Surprisingly, it works for both categorical and continuous dependent variables. In this algorithm, we split the population into two or more homogeneous sets. This is done based on the most significant attributes/ independent variables to make as distinct groups as possible. For more details, you can read Decision Tree Simplified.

In the image above, you can see that population is classified into four different groups based on multiple attributes to identify ‘if they will play or not’. To split the population into different heterogeneous groups, it uses various techniques like Gini, Information Gain, Chi-square, and entropy.

The best way to understand how the decision tree works, is to play Jezzball – a classic game from Microsoft (image below). Essentially, you have a room with moving walls and you need to create walls such that the maximum area gets cleared off without the balls.

So, every time you split the room with a wall, you are trying to create 2 different populations within the same room. Decision trees work in a very similar fashion by dividing a population into as different groups as possible.

*More: **Simplified Version of Decision Tree Algorithms*

```
library(rpart)
x <- cbind(x_train,y_train)
# grow tree
fit <- rpart(y_train ~ ., data = x,method="class")
summary(fit)
#Predict Output
predicted= predict(fit,x_test)
```

It is a classification method. In SVM algorithm, we plot each data item as a point in n-dimensional space (where n is the number of features you have), with the value of each feature being the value of a particular coordinate.

For example, if we only had two features like the Height and Hair length of an individual, we’d first plot these two variables in two-dimensional space where each point has two coordinates (these co-ordinates are known as **Support Vectors**)

Now, we will find some *lines* that split the data between the two differently classified groups of data. This will be the line such that the distances from the closest point in each of the two groups will be the farthest away. If there are more variables, a hyperplane is used to separate the classes.

In the example shown above, the line which splits the data into two differently classified groups is the *black* *line *since the two closest points are the farthest apart from the line. This line is our classifier. Then, depending on where the testing data lands on either side of the line, that’s what class we can classify the new data as.

**Think of this algorithm as playing JezzBall in n-dimensional space. The tweaks in the game are:**

- You can draw lines/planes at any angle (rather than just horizontal or vertical as in the classic game)
- The objective of the game is to segregate balls of different colors in different rooms.
- And the balls are not moving.

```
library(e1071)
x <- cbind(x_train,y_train)
# Fitting model
fit <-svm(y_train ~ ., data = x)
summary(fit)
#Predict Output
predicted= predict(fit,x_test)
```

It is a classification technique based on Bayes’ theorem with an assumption of independence between predictors. In simple terms, a Naive Bayes classifier assumes that the presence of a particular feature in a class is unrelated to the presence of any other feature. For example, a fruit may be considered to be an apple if it is red, round, and about 3 inches in diameter. Even if these features depend on each other or upon the existence of the other features, a naive Bayes classifier would consider all of these properties to independently contribute to the probability that this fruit is an apple.

The Naive Bayesian model is easy to build and particularly useful for very large data sets. Along with simplicity, Naive Bayes is known to outperform even highly sophisticated classification methods.

Bayes theorem provides a way of calculating posterior probability P(c|x) from P(c), P(x), and P(x|c). Look at the equation below:

Here,

*P*(*c|x*) is the posterior probability of*class*(*target*) given*predictor*(*attribute*).*P*(*c*) is the prior probability of the*class*.*P*(*x|c*) is the likelihood which is the probability of the*predictor*given the*class*.*P*(*x*) is the prior probability of the*predictor*.

Let’s understand it using an example. Below is a training data set of weather and the corresponding target variable, ‘Play.’ Now, we need to classify whether players will play or not based on weather conditions. Let’s follow the below steps to perform it.

Time needed: 3 minutes

**Convert the data set to a frequency table.****Create a Likelihood table by finding the probabilities like Overcast probability = 0.29 and probability of playing is 0.64.****Now, use the Naive Bayesian equation to calculate the posterior probability for each class. The class with the highest posterior probability is the outcome of the prediction.**

**Problem: **Players will pay if the weather is sunny. Is this statement correct?

We can solve it using above discussed method, so P(Yes | Sunny) = P( Sunny | Yes) * P(Yes) / P (Sunny)

Here we have P (Sunny | Yes) = 3/9 = 0.33, P(Sunny) = 5/14 = 0.36, P(Yes)= 9/14 = 0.64

Now, P (Yes | Sunny) = 0.33 * 0.64 / 0.36 = 0.60, which has a higher probability.

Naive Bayes uses a similar method to predict the probability of different classes based on various attributes. This algorithm is mostly used in text classification and with problems having multiple classes.

```
library(e1071)
x <- cbind(x_train,y_train)
# Fitting model
fit <-naiveBayes(y_train ~ ., data = x)
summary(fit)
#Predict Output
predicted= predict(fit,x_test)
```

It can be used for both classification and regression problems. However, it is more widely used in classification problems in the industry. K nearest neighbors is a simple algorithm that stores all available cases and classifies new cases by a majority vote of its k neighbors. The case assigned to the class is most common amongst its K nearest neighbors measured by a distance function.

These distance functions can be Euclidean, Manhattan, Minkowski, and Hamming distances. The first three functions are used for continuous functions, and the fourth one (Hamming) for categorical variables. If K = 1, then the case is simply assigned to the class of its nearest neighbor. At times, choosing K turns out to be a challenge while performing kNN modeling.

More: Introduction to k-nearest neighbors: Simplified.

KNN can easily be mapped to our real lives. If you want to learn about a person with whom you have no information, you might like to find out about his close friends and the circles he moves in and gain access to his/her information!

**Things to consider before selecting kNN:**

- KNN is computationally expensive
- Variables should be normalized else higher range variables can bias it
- Works on pre-processing stage more before going for kNN like an outlier, noise removal

```
library(knn)
x <- cbind(x_train,y_train)
# Fitting model
fit <-knn(y_train ~ ., data = x,k=5)
summary(fit)
#Predict Output
predicted= predict(fit,x_test)
```

It is a type of unsupervised algorithm which solves the clustering problem. Its procedure follows a simple and easy way to classify a given data set through a certain number of clusters (assume k clusters). Data points inside a cluster are homogeneous and heterogeneous to peer groups.

Remember figuring out shapes from ink blots? k means is somewhat similar to this activity. You look at the shape and spread to decipher how many different clusters/populations are present!

- K-means picks k number of points for each cluster known as centroids.
- Each data point forms a cluster with the closest centroids, i.e., k clusters.
- Finds the centroid of each cluster based on existing cluster members. Here we have new centroids.
- As we have new centroids, repeat steps 2 and 3. Find the closest distance for each data point from new centroids and get associated with new k-clusters. Repeat this process until convergence occurs, i.e., centroids do not change.

In K-means, we have clusters, and each cluster has its own centroid. The sum of the square of the difference between the centroid and the data points within a cluster constitutes the sum of the square value for that cluster. Also, when the sum of square values for all the clusters is added, it becomes a total within the sum of the square value for the cluster solution.

We know that as the number of clusters increases, this value keeps on decreasing, but if you plot the result, you may see that the sum of squared distance decreases sharply up to some value of k and then much more slowly after that. Here, we can find the optimum number of clusters.

```
library(cluster)
fit <- kmeans(X, 3) # 5 cluster solution
```

Random Forest is a trademarked term for an ensemble learning of decision trees. In Random Forest, we’ve got a collection of decision trees (also known as “Forest”). To classify a new object based on attributes, each tree gives a classification, and we say the tree “votes” for that class. The forest chooses the classification having the most votes (over all the trees in the forest).

Each tree is planted & grown as follows:

- If the number of cases in the training set is N, then a sample of N cases is taken at random but
*with replacement*. This sample will be the training set for growing the tree. - If there are M input variables, a number m<<M is specified such that at each node, m variables are selected at random out of the M, and the best split on this m is used to split the node. The value of m is held constant during the forest growth.
- Each tree is grown to the largest extent possible. There is no pruning.

For more details on this algorithm, compared with the decision tree and tuning model parameters, I would suggest you read these articles:

- Introduction to Random forest – Simplified
- Comparing a CART model to Random Forest (Part 1)
- Comparing a Random Forest to a CART model (Part 2)
- Tuning the parameters of your Random Forest model

```
library(randomForest)
x <- cbind(x_train,y_train)
# Fitting model
fit <- randomForest(Species ~ ., x,ntree=500)
summary(fit)
#Predict Output
predicted= predict(fit,x_test)
```

In the last 4-5 years, there has been an exponential increase in data capturing at every possible stage. Corporates/ Government Agencies/ Research organizations are not only coming up with new sources, but also they are capturing data in great detail.

For example, E-commerce companies are capturing more details about customers like their demographics, web crawling history, what they like or dislike, purchase history, feedback, and many others to give them personalized attention more than your nearest grocery shopkeeper.

As data scientists, the data we are offered also consists of many features, this sounds good for building a good robust model, but there is a challenge. How’d you identify highly significant variable(s) out of 1000 or 2000? In such cases, the dimensionality reduction algorithm helps us, along with various other algorithms like Decision Tree, Random Forest, PCA (principal component analysis), Factor Analysis, Identity-based on the correlation matrix, missing value ratio, and others.

To know more about these algorithms, you can read “Beginners Guide To Learn Dimension Reduction Techniques“.

```
library(stats)
pca <- princomp(train, cor = TRUE)
train_reduced <- predict(pca,train)
test_reduced <- predict(pca,test)
```

Now, let’s look at the 4 most commonly used gradient boosting algorithms.

GBM is a boosting algorithm used when we deal with plenty of data to make a prediction with high prediction power. Boosting is actually an ensemble of learning algorithms that combines the prediction of several base estimators in order to improve robustness over a single estimator. It combines multiple weak or average predictors to build a strong predictor. These boosting algorithms always work well in data science competitions like Kaggle, AV Hackathon, and CrowdAnalytix.

More: Know about Boosting algorithms in detail

```
library(caret)
x <- cbind(x_train,y_train)
# Fitting model
fitControl <- trainControl( method = "repeatedcv", number = 4, repeats = 4)
fit <- train(y ~ ., data = x, method = "gbm", trControl = fitControl,verbose = FALSE)
predicted= predict(fit,x_test,type= "prob")[,2]
```

GradientBoostingClassifier and Random Forest are two different boosting tree classifiers, and often people ask about the difference between these two algorithms.

Another classic gradient-boosting algorithm that’s known to be the decisive choice between winning and losing in some Kaggle competitions is the XGBoost. It has an immensely high predictive power, making it the best choice for accuracy in events. It possesses both a linear model and the tree learning algorithm, making the algorithm almost 10x faster than existing gradient booster techniques.

One of the most interesting things about the XGBoost is that it is also called a regularized boosting technique. This helps to reduce overfit modeling and has massive support for a range of languages such as Scala, Java, R, Python, Julia, and C++.

The support includes various objective functions, including regression, classification, and ranking. Supports distributed and widespread training on many machines that encompass GCE, AWS, Azure, and Yarn clusters. XGBoost can also be integrated with Spark, Flink, and other cloud dataflow systems with built-in cross-validation at each iteration of the boosting process.

To learn more about XGBoost and parameter tuning, visit https://www.analyticsvidhya.com/blog/2016/03/complete-guide-parameter-tuning-xgboost-with-codes-python/.

```
require(caret)
x <- cbind(x_train,y_train)
# Fitting model
TrainControl <- trainControl( method = "repeatedcv", number = 10, repeats = 4)
model<- train(y ~ ., data = x, method = "xgbLinear", trControl = TrainControl,verbose = FALSE)
OR
model<- train(y ~ ., data = x, method = "xgbTree", trControl = TrainControl,verbose = FALSE)
predicted <- predict(model, x_test)
```

LightGBM is a gradient-boosting framework that uses tree-based learning algorithms. It is designed to be distributed and efficient with the following advantages:

- Faster training speed and higher efficiency
- Lower memory usage
- Better accuracy
- Parallel and GPU learning supported
- Capable of handling large-scale data

The framework is a fast and high-performance gradient-boosting one based on decision tree algorithms used for ranking, classification, and many other machine-learning tasks. It was developed under the Distributed Machine Learning Toolkit Project of Microsoft.

Since the LightGBM is based on decision tree algorithms, it splits the tree leaf-wise with the best fit, whereas other boosting algorithms split the tree depth-wise or level-wise rather than leaf-wise. So when growing on the same leaf node in Light GBM, the leaf-wise algorithm can reduce more loss than the level-wise algorithm, resulting in much better accuracy, which any existing boosting algorithms can rarely achieve.

Also, it is surprisingly very fast, hence the word ‘Light.’

Refer to the article to know more about LightGBM: https://www.analyticsvidhya.com/blog/2017/06/which-algorithm-takes-the-crown-light-gbm-vs-xgboost/

```
data = np.random.rand(500, 10) # 500 entities, each contains 10 features
label = np.random.randint(2, size=500) # binary target
train_data = lgb.Dataset(data, label=label)
test_data = train_data.create_valid('test.svm')
param = {'num_leaves':31, 'num_trees':100, 'objective':'binary'}
param['metric'] = 'auc'
num_round = 10
bst = lgb.train(param, train_data, num_round, valid_sets=[test_data])
bst.save_model('model.txt')
# 7 entities, each contains 10 features
data = np.random.rand(7, 10)
ypred = bst.predict(data)
```

```
library(RLightGBM)
data(example.binary)
#Parameters
num_iterations <- 100
config <- list(objective = "binary", metric="binary_logloss,auc", learning_rate = 0.1, num_leaves = 63, tree_learner = "serial", feature_fraction = 0.8, bagging_freq = 5, bagging_fraction = 0.8, min_data_in_leaf = 50, min_sum_hessian_in_leaf = 5.0)
#Create data handle and booster
handle.data <- lgbm.data.create(x)
lgbm.data.setField(handle.data, "label", y)
handle.booster <- lgbm.booster.create(handle.data, lapply(config, as.character))
#Train for num_iterations iterations and eval every 5 steps
lgbm.booster.train(handle.booster, num_iterations, 5)
#Predict
pred <- lgbm.booster.predict(handle.booster, x.test)
#Test accuracy
sum(y.test == (y.pred > 0.5)) / length(y.test)
#Save model (can be loaded again via lgbm.booster.load(filename))
lgbm.booster.save(handle.booster, filename = "/tmp/model.txt")
```

If you’re familiar with the Caret package in R, this is another way of implementing the LightGBM.

```
require(caret)
require(RLightGBM)
data(iris)
model <-caretModel.LGBM()
fit <- train(Species ~ ., data = iris, method=model, verbosity = 0)
print(fit)
y.pred <- predict(fit, iris[,1:4])
library(Matrix)
model.sparse <- caretModel.LGBM.sparse()
#Generate a sparse matrix
mat <- Matrix(as.matrix(iris[,1:4]), sparse = T)
fit <- train(data.frame(idx = 1:nrow(iris)), iris$Species, method = model.sparse, matrix = mat, verbosity = 0)
print(fit)
```

CatBoost is one of open-sourced machine learning algorithms from Yandex. It can easily integrate with deep learning frameworks like Google’s TensorFlow and Apple’s Core ML. The best part about CatBoost is that it does not require extensive data training like other ML models and can work on a variety of data formats, not undermining how robust it can be.

Catboost can automatically deal with categorical variables without showing the type conversion error, which helps you to focus on tuning your model better rather than sorting out trivial errors. Make sure you handle missing data well before you proceed with the implementation.

Learn more about Catboost from this article: https://www.analyticsvidhya.com/blog/2017/08/catboost-automated-categorical-data/

```
import pandas as pd
import numpy as np
from catboost import CatBoostRegressor
#Read training and testing files
train = pd.read_csv("train.csv")
test = pd.read_csv("test.csv")
#Imputing missing values for both train and test
train.fillna(-999, inplace=True)
test.fillna(-999,inplace=True)
#Creating a training set for modeling and validation set to check model performance
X = train.drop(['Item_Outlet_Sales'], axis=1)
y = train.Item_Outlet_Sales
from sklearn.model_selection import train_test_split
X_train, X_validation, y_train, y_validation = train_test_split(X, y, train_size=0.7, random_state=1234)
categorical_features_indices = np.where(X.dtypes != np.float)[0]
#importing library and building model
from catboost import CatBoostRegressormodel=CatBoostRegressor(iterations=50, depth=3, learning_rate=0.1, loss_function='RMSE')
model.fit(X_train, y_train,cat_features=categorical_features_indices,eval_set=(X_validation, y_validation),plot=True)
submission = pd.DataFrame()
submission['Item_Identifier'] = test['Item_Identifier']
submission['Outlet_Identifier'] = test['Outlet_Identifier']
submission['Item_Outlet_Sales'] = model.predict(test)
```

```
set.seed(1)
require(titanic)
require(caret)
require(catboost)
tt <- titanic::titanic_train[complete.cases(titanic::titanic_train),]
data <- as.data.frame(as.matrix(tt), stringsAsFactors = TRUE)
drop_columns = c("PassengerId", "Survived", "Name", "Ticket", "Cabin")
x <- data[,!(names(data) %in% drop_columns)]y <- data[,c("Survived")]
fit_control <- trainControl(method = "cv", number = 4,classProbs = TRUE)
grid <- expand.grid(depth = c(4, 6, 8),learning_rate = 0.1,iterations = 100, l2_leaf_reg = 1e-3, rsm = 0.95, border_count = 64)
report <- train(x, as.factor(make.names(y)),method = catboost.caret,verbose = TRUE, preProc = NULL,tuneGrid = grid, trControl = fit_control)
print(report)
importance <- varImp(report, scale = FALSE)
print(importance)
```

Now, it’s time to take the plunge and actually play with some other real-world datasets. So are you ready to take on the challenge? Accelerate your data science journey with the following practice problems:

Practice Problem: Food Demand Forecasting Challenge | Predict the demand for meals for a meal delivery company | |

Practice Problem: HR Analytics Challenge | Identify the employees most likely to get promoted | |

Practice Problem: Predict Number of Upvotes | Predict the number of upvotes on a query asked at an online question & answer platform |

By now, I am sure you would have an idea of commonly used machine learning algorithms. My sole intention behind writing this article and providing the codes in R and Python is to get you started right away. If you are keen to master machine learning algorithms, start right away. Take up problems, develop a physical understanding of the process, apply these codes, and watch the fun!

- We are now familiar with some of the most common ML algorithms used in the industry.
- We’ve covered the advantages and disadvantages of various ML algorithms.
- We’ve also learned the basic implementation details in R and Python languages.

A. While the suitable algorithm depends on the problem, gradient-boosted decision trees are mostly used to balance performance and interpretability.

A. In the supervised learning model, the labels associated with the features are given. In unsupervised learning, no labels are provided for the model.

A. The 3 main types of ML models are based on Supervised Learning, Unsupervised Learning, and Reinforcement Learning.

Lorem ipsum dolor sit amet, consectetur adipiscing elit,

Awesowe compilation!! Thank you.

Thank you very much, A Very useful and excellent compilation. I have already bookmarked this page.

Straight, Informative and effective!! Thank you

Good Summary airticle

Super Compilation...

Wonderful! Really helpful

Very nicely done! Thanks for this.

Thank you! Well presented article.

Thank you! Well presented.

Hello, Superb information in just one blog. Can anyone help me to run the codes in R what should be replaced with "~" symbol in codes? Help is appreciated

Hello, Superb information in just one blog. Can anyone help me to run the codes in R what should be replaced with "~" symbol in codes? Help is appreciated .

"~" is used to select the variables that you'll be using for a particular model. "label~.," - Uses all your Attributes "label~Att1 + Att2," - Uses only Att1 and Att2 to create the model

Enjoyed the simplicity. Thanks for the effort.

Great Article... Helps a lot, as naive in Machine Learning.

Hi All, Thanks for the comment ...

Very good summary. Thank! One simple point. The reason for taking the log(p/(1-p)) in Logistic Regression is to make the equation linear, I.e., easy to solve.

Thanks Dalila... :)

That's not the reason for taking the log. The underlying assumption in logistic regression is that the probability is governed by a step function whose argument is linear in the attributes. First of all the assumption of linearity or otherwise introduces bias. However, logistic regression being a parametric model some bias is inevitable. The reason to choose a linear relationship is not because its easy to solve but because a higher order polynomial introduces higher bias and one would not like to do so without good reason. Now coming to the choice of log, it is just a convention. Basically, once we have decided to go with a linear model, in the case of one attribute we model the probability by p(x) = f( ax+b) such that p(-infinity)=0 and p(infinity)=0. It so happens that this is satisfied by p(x) = exp(ax+b)/ (1 + exp(ax+b)) which can be re-written as log(p(x)/(1-p(x)) = a x+ b While I am at it, it may be useful to talk about another point. One should ask is why we don't use least square method. The reason is that a yes/no choice is a Bernoulli random variable and thus we estimate the probability according to maximum likelihood wrt Bernoulli process. For linear regression the assumption is that the residuals around the 'true' function are distributed according to a normal distribution and the maximum likelihood estimate for a normal distribution amounts to the least square method. So deep down linear regression and logistic regression both use maximum likelihood estimates. Its just that they are max likelihoods according to different distributions.

Nice summary! @Huzefa: you shouldn't replace the "~" in the R code, it basically means "as a function of". You can also keep the "." right after, it stands for "all other variables in the dataset provided". If you want to be explicit, you can write y ~ x1 + x2 + ... where x1, x2 .. are the names of the columns of your data.frame or data.table. Further note on formula specification: by default R adds an intercept, so that y ~ x is equivalent to y ~ 1 + x, you can remove it via y ~ 0 + x. Interactions are specified with either * (which also adds the two variables) or : (which only adds the interaction term). y ~ x1 * x2 is equivalent to y ~ x1 + x2 + x1 : x2. Hope this helps!

You did a Wonderful job! This is really helpful. Thanks!

I took the Stanford-Coursera ML class, but have not used it, and I found this to be an incredibly useful summary. I appreciate the real-world analogues, such as your mention of Jezzball. And showing the brief code snips is terrific.

This is very easy and helpful than any other courses I have completed. simple. clear. To the point.

You Sir are a gentleman and a scholar!

Hi Sunil, This is really superb tutorial along with good examples and codes which is surely much helpful. Just, can you add Neural Network here in simple terms with example and code.

Errata:- fit <- kmeans(X, 3) # 5 cluster solution It`s a 3 cluster solution.

Well done, Thank you!

This is a great resource overall and surely the product of a lot of work. Just a note as I go through this, your comment on Logistic Regression not actually being regression is in fact wrong. It maps outputs to a continuous variable bound between 0 and 1 that we regard as probability. it makes classification easy but that is still an extra step that requires the choice of a threshold which is not the main aim of Logistic Regression. As a matter of fact it falls under the umbrella of Generalized Libear Models as the glm R package hints it in your code example. I thought this was interesting to note so as not to forget that logistic regression output is richer than 0 or 1. Thanks for the great article overall.

Very Nice.!!

Thank you.. reallu helpful article

I wanted to know if I can use rattle instead of writing the R code explicitly

Thank you. Very nice and useful article..

This is such a wonderful article.

Informative and easy to follow. I've recently started following several pages like this one and this is the best material ive seen yet.

One of the best content ever read regarding algorithms.

Thank you so much for this article

Cool stuff! I just can't get the necessary libraries...

looks sgood article. Do I need any data to do the examples?

Good Article.

I have to thank you for this informative summary. Really useful!

Somewhat irresponsible article since it does not mention any measure of performance and only gives cooking recipes without understanding what algorithm does what and the stats behind it. Cooking recipes like these are the ones that place people in Drew Conway's danger zone (https://www.quora.com/In-the-data-science-venn-diagram-why-is-the-common-region-of-Hacking-Skills-and-Substantive-Expertise-considered-as-danger-zone), thus making programmers the worst data analysts (let alone scientists, that requires another mindset completely). I highly recommend anyone wishing to enter into this brave new world not to jump into statistical learning without proper statistical background. Otherwise you could end up like Google, Target, Telefonica, or Google (again) and become a poster boy for "The Big Flops of Big Data".

Do you have a better article?Please share...

Great article. It really summarize some of the most important topics on machine learning. But as asked above I would like to present thedevmasters.com as a company with a really good course to learn more depth about machine learning with great professors and a sense of community that is always helping itself to continue learning even after the course ends.

awesome , recommended this article to all my friends

Very succinct description of some important algorithms. Thanks. I'd like to point out a mistake in the SVM section. You say "where each point has two co-ordinates (these co-ordinates are known as Support Vectors)". This is not correct, the coordinates are just features. Its the points lying on the margin that are called the 'support vectors'. These are the points that 'support' the margin i.e. define it (as opposed to a weighted average of all points for instance.)

Thank you for this wonderful article...it's proven helpful.

Thank you very much, A Very useful and excellent compilation.

Very good information interms of initial knowledge Note one warning, many methods can be fitted into a particular problem, but result might not be what you wish. Hence you must always compare models, understand residuals profile and how prediction really predicts. In that sense, analysis of data is never ending. In R, use summary, plot and check for assumptions validity .

The amazing article. I'm new in data analysis. It's very useful and easy to understand. Thanks,

This is really good article, also if you would have explain about Anomaly dection algorithm that will really helpful for everyone to know , what and where to apply in machine learning....

a very very helpful tutorial. Thanks a lot you guys.

The amazing article

Analytics Vidhya - I am loving it

Good article. thank you for explaining with python.

very useful compilation. Thanks!

Very precise quick tutorial for those who want to gain insight of machine learning

great

Very useful and informative. Thanks for sharing it.

Superb!

great summary, Thank you

Great article. It would have become even better if you had some test data with each code snippet. Add metrics and hyper parameter tunning for each of these models

Thanks for the "jezzball" example. You made my day!

Nicely complied. Every explanation is crystal clear and very easy to digest. Thanks for sharing knowledge.

Perfect! It's exactly what I was looking for! Thanks for the explanation and thanks for sharing your knowledge with us!

Very useful summary. Thank you.

Very informative article. For a person new to machine learning, this article gives a good starting point.

Good article. thank you for explaining with python.

Good article. thank you for explaining with python. -- Kareermatrix

Hi Friends, i'm new person to these machine learning algorithms. i have some questions.? 1) we have so many ML algorithms. but how can we choose the algorithms which one is suitable for my data set? 2) How does these algorithms works.? 3) why only these particular algorithms.? why not others.?

Nice Article.. Thanks for your effort

hello I have to implement machine learning algorithms in python so could you help me in this. any body provide me the proper code for any algorithm.

All programe has error named Error in model.frame.default(formula = as.list(y_train) ~ ., data = x, : invalid type (list) for variable 'as.list(y_train)' What is that ?

I think that "y_train" is data frame and it cannot be converted directly to list with "as.list" command. Try this instead y_train <- as.list(as.data.frame(t(y_train))) See if this works for you.

Very nice summary! Can you tell how to get machine learning problems for practice?

Analytics Vidhya has some practice datasets. Check Analytics Vidhya hackathon. Also UCI machine learning repository is a phenomenal place. Google it and enjoy.

Do you have R codes based on caret?

Yes.