The Machine Learning Crash Course – Part 2: Linear Regression

Welcome back to the second part of the Machine Learning Crash Course…🌟🌟🌟🌟🌟

In the first part we’ve covered the basic terminologies of Machine Learning and have taken a first look at Colab – a Python-based development environment which is great for solving Machine Learning exercises with Python and TensorFlow.

In this second part we’ll move on and start with the first practical machine learning scenario which is solving a simple linear regression problem. First, let’s clarify what linear regression is in general.

Linear Regression

The first Machine Learning exercise we’re going to solve is a simple linear regression task. Linear regression is a linear approach to modelling the relationship between a dependent variable and one or more independent variables. If only one independent variable is used we’re talking about a simple linear regression. A simple linear regression is what we’ll be using for the Machine Learning exercise in this tutorial:

y = 2x + 30

In this example x is the independant variable and y is the dependant variable. For every input value of x the corresponding output value y can be determined.

Create A New Colab Notebook And ImporT Dependencies

To get started let’s create a new Python 3 Colab notebook first. Go to https://colab.research.google.com login with your Google account and create a new notebook which is initially empty.

As the first step we need to make sure to import needed libraries. We’ll use TensorFlow, NumPy and Matplotlib. Insert the following lines of code in code cell:

from __future__ import absolute_import, division, print_function, unicode_literals
import tensorflow as tf
import numpy as np
import matplotlib.pyplot as plt

The first line of imports is just added because of compatibility reasons and can be ignored. The import statements for TensorFlow, NumPy and Matplotlib are working out of the box because all three libraries are preinstalled in the Colab environment.

Preparing The Training Data

Having imported the needed libraries the next step is to prepare the training data which should be used to train our model. Insert another code cell and insert the following Python code:

values_x = np.array([-10, 0, 2, 6, 12, 15], dtype=float)
values_y = np.array([10, 30, 34, 42, 54, 60], dtype=float)

for i,x in enumerate(values_x):
  print("X: {} Y: {}".format(x, values_y[i]))

Two NumPy arrays are initialised here. The first array (values_x) is containing the x values of our linear regression. This is the independent variable of y = 2x + 30. For each of the x values in the first array the second array (values_y) contains the corresponding y value.

By using a for-loop the value pairs are printed out:

If you like you can also use Matplotlib to visualise the the linear regression function as a graph:

x = np.linspace(-10,10,100) 
plt.title('Graph of y=2x+30') 
plt.plot(x, x*2+30);

Creating The Model

Next, we’re ready to create the model (neural network) which we need to solve our linear regression task. Insert the following code into the notebook:

model = tf.keras.Sequential([
    tf.keras.layers.Dense(units=1, input_shape=[1])
])

model.compile(loss='mean_squared_error', 
              optimizer=tf.keras.optimizers.Adam(0.1))

Here we’re using the TensorFlow-integrated Keras API to create out neural network. In order to create a new sequential model the tf.keras.Sequential method is used.

Note:

Keras is a high-level interface for neural networks that runs on top of different back-ends. Its API is user-friendly, yet flexible enough to build all kinds of applications. Keras quickly gained traction after its introduction and in 2017, the Keras API was integrated into core Tensorflow as tf.keras

In Keras, you assemble layers to build models. A model is (usually) a graph of layers. The most common type of model is a stack of layers: the* tf.keras.Sequential* model.

The call of the Sequential method is expecting to get an array (stack) of layers. In our case it is just one layer of type Dense. A Dense layer can be seen as a linear operation in which every input is connected to every output by a weight and a bias. The number of inputs is specified by the first parameter units. The number of neurons in the layer is determined by the value of the parameter input_shape.

In our case we only need one input element because for the linear regression problem we’re trying to solve by the neural network we’ve only defined one dependant variable (x). Furthermore the Dense layer is setup in the most simple way: it consists of just one neuron:

With that simple neural network defined it’s easy to take a look at some of the insights to further understand how the neurons work. Each neuron has a specific weight which is adapted when the training is performed. The weight of every neuron in the fully connected Dense layer is multiplied with each input variable. As we only have defined one input variable (x) this input is multiplied with the weight w1 of the first and only neuron of the defined Dense layer. Furthermore, for each Dense layer a bias (b1) is added to the formula:

Now we can see why it is sufficient to only ad a Dense layer with just one neuron to solve our simple linear regression problem. By training the model the weight of the neuron will approach a value of 2 and the bias will approach a value of 30. The trained neuron will then be able to provide the output for inputs of x.

Having added the Dense layer to the sequential model we finally need to compile the model in order to make it usable for training and prediction in the next step.

The compilation of the model is done by executing the method model.compile:

model.compile(loss='mean_squared_error', 
              optimizer=tf.keras.optimizers.Adam(0.1))

Here we need to specify which loss function and which type of optimizer to use.

Loss function:

Loss functions are a central concept in machine learning. By using loss functions the machine learning algorithm is able to measure how much a prediction deviates from the actual result. Based on that determination the machine algorithm knows if the prediction results are getting better or worse.

The mean squred error is a specific loss function which is suitable to train a model for a linear regession problem.

As the name suggests, Mean square error is measured as the average of squared difference between predictions and actual observations. Due to squaring, predictions which are far away from actual values are penalized heavily in comparison to less deviated predictions.

Optimizer:

Based on the outcome which is calculated by the loss function the optimizer is used to determine the learning rate which is applied for the parameters in the model (weights and biases).

In our example we’re making use of the Adam optimizer which is great for linear regression tasks.

Training The Model

The model is ready and the next thing we need to do is to train the model with the test data. This is being done by using the model.fit method:

history = model.fit(values_x, values_y, epochs=500, verbose=False)

As the first and the second argument we’re passing in the test values which are available in arrays values_x and values_y. The third argument is the number of epochs which will be used for training. An epoch is an iteration over the entire x and y data provided. In our example we’re using 500 iterations over the test data set to train the model.

After executing the training of the model let’s take a look inside the development of the loss over all 500 epochs. This can be printed out as a diagram by using the following three lines of code:

plt.xlabel("Epoch Number") 
plt.ylabel("Loss Magnidute") 
plt.plot(history.history['loss'])

The result should be a diagram that looks like the following:

Here you can see that the loss gets better and better from epoch to epoch. Over the 500 epochs used for training we’re able to see that the loss magnitude is approaching zero which shows that the model is able to predict values with a high accuracy.

Predicting Values

Now that the model is fully trained let’s try to perform a prediction by calling function model.predict.

print(model.predict([20.0]))

The argument which is passed into the predict method is an array containing the *x *value for which the corresponding *y *value should be determined. The expected result should be somewhere near 70 (because of y=2x+30) . The output can be seen in the following:

Here we’re getting returned the value 70.05354 which is pretty close to 70.0, so that our model is working as expected.

Getting Model Insights, Retrieving Weights And Bias

We’re able to get more model insights by taking a look at the weight and the bias which is determined for the first layer:

print("These are the layer variables: {}".format(model.layers[0].get_weights()))

As expected we’re getting returned two parameter for our first and only layer in the model:

The two parameters corresponds to the two variables we have in the model:

• Weight
• Bias

For the weight the value which is determined is near the target value of 2 and for the bias the value which is determined is near the target value of 30 (according to our linear regression formula: y = 2x + 30).

#machine-learning

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Pros and Cons of Machine Learning Language

Amid all the promotion around Big Data, we continue hearing the expression “AI”. In addition to the fact that it offers a profitable vocation, it vows to tackle issues and advantage organizations by making expectations and helping them settle on better choices. In this blog, we will gain proficiency with the Advantages and Disadvantages of Machine Learning. As we will attempt to comprehend where to utilize it and where not to utilize Machine learning.

In this article, we discuss the Pros and Cons of Machine Learning.
Each coin has two faces, each face has its property and highlights. It’s an ideal opportunity to reveal the essence of ML. An extremely integral asset that holds the possibility to reform how things work.

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1. **Effectively recognizes patterns and examples **

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2. No human mediation required (mechanization)

With ML, you don’t have to keep an eye on the venture at all times. Since it implies enabling machines to learn, it lets them make forecasts and improve the calculations all alone. A typical case of this is hostile to infection programming projects; they figure out how to channel new dangers as they are perceived. ML is additionally acceptable at perceiving spam.

3. **Constant Improvement **

As ML calculations gain understanding, they continue improving in precision and productivity. This lets them settle on better choices. Let’s assume you have to make a climate figure model. As the measure of information you have continues developing, your calculations figure out how to make increasingly exact expectations quicker.

4. **Taking care of multi-dimensional and multi-assortment information **

AI calculations are acceptable at taking care of information that is multi-dimensional and multi-assortment, and they can do this in unique or unsure conditions. Key Difference Between Machine Learning and Artificial Intelligence

5. **Wide Applications **

You could be an e-posterior or a social insurance supplier and make ML work for you. Where it applies, it holds the ability to help convey a considerably more close to home understanding to clients while additionally focusing on the correct clients.

**Cons of Machine Learning **

With every one of those points of interest to its effectiveness and ubiquity, Machine Learning isn’t great. The accompanying components serve to confine it:

1.** Information Acquisition**

AI requires monstrous informational indexes to prepare on, and these ought to be comprehensive/fair-minded, and of good quality. There can likewise be times where they should trust that new information will be created.

2. **Time and Resources **

ML needs sufficient opportunity to allow the calculations to learn and grow enough to satisfy their motivation with a lot of precision and pertinence. It additionally needs monstrous assets to work. This can mean extra necessities of PC power for you.
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3. **Understanding of Results **

Another significant test is the capacity to precisely decipher results produced by the calculations. You should likewise cautiously pick the calculations for your motivation.

4. High mistake weakness

AI is self-governing yet exceptionally powerless to mistakes. Assume you train a calculation with informational indexes sufficiently little to not be comprehensive. You end up with one-sided expectations originating from a one-sided preparing set. This prompts unessential promotions being shown to clients. On account of ML, such botches can set off a chain of mistakes that can go undetected for extensive periods. What’s more, when they do get saw, it takes very some effort to perceive the wellspring of the issue, and significantly longer to address it.

**Conclusion: **

Subsequently, we have considered the Pros and Cons of Machine Learning. Likewise, this blog causes a person to comprehend why one needs to pick AI. While Machine Learning can be unimaginably ground-breaking when utilized in the correct manners and in the correct spots (where gigantic preparing informational indexes are accessible), it unquestionably isn’t for everybody. You may likewise prefer to peruse Deep Learning Vs Machine Learning.

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Machine learning is one of the technologies that have already begun their promising marks in the transportation industry.Autonomous Vehicles,Smartphone Apps,Traffic Management Solutions,Law Enforcement,Passenger Transportation etc are the applications of AI and ML in the transportation industry.Following challenges in the transportation industry can be solved by machine learning and Artificial Intelligence.

• ML and AI can offer high security in the transportation industry.
• It offers high reliability of their services or vehicles.
• The adoption of this technology in the transportation industry can increase the efficiency of the service.
• In the transportation industry ML helps scientists and engineers come up with far more environmentally sustainable methods for powering and operating vehicles and machinery for travel and transport.

Healthcare industry

Technology-enabled smart healthcare is the latest trend in the healthcare industry. Different areas of healthcare, such as patient care, medical records, billing, alternative models of staffing, IP capitalization, smart healthcare, and administrative and supply cost reduction. Hire dedicated machine learning developers for any of the following applications.

• Identifying Diseases and Diagnosis
• Drug Discovery and Manufacturing
• Medical Imaging Diagnosis
• Personalized Medicine
• Machine Learning-based Behavioral Modification
• Smart Health Records
• Clinical Trial and Research
• Better Radiotherapy
• Crowdsourced Data Collection
• Outbreak Prediction

**
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In financial industries organizations like banks, fintech, regulators and insurance are Adopting machine learning to improve their facilities.Following are the use cases of machine learning in finance.

• Fraud prevention
• Risk management
• Investment predictions
• Customer service
• Digital assistants
• Marketing
• Network security
• Loan underwriting
• Algorithmic trading
• Process automation
• Document interpretation
• Content creation
• Trade settlements
• Money-laundering prevention
• Custom machine learning solutions

Education industry

Education industry is one of the industries which is investing in machine learning as it offers more efficient and easierlearning.AdaptiveLearning,IncreasingEfficiency,Learning Analytics,Predictive Analytics,Personalized Learning,Evaluating Assessments etc are the applications of machine learning in the education industry.

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**
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Continuous technological advances are bound to hit the field of machine learning, which will shape the future of machine learning as an intensively evolving language.

• Improved Unsupervised Algorithms
• Increased Adoption of Quantum Computing
• Enhanced Personalization
• Improved Cognitive Services
• Rise of Robots

**Conclusion
**
Today most of the business from different industries are hire machine learning developers in India and achieve their business goals. This technology may have multiple applications, and, interestingly, it hasn’t even started yet but having taken such a massive leap, it also opens up so many possibilities in the existing business models in such a short period of time. There is no question that the increase of machine learning also brings the demand for mobile apps, so most companies and agencies employ Android developers and hire iOS developers to incorporate machine learning features into them.

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