ML

Machine learning (ML) is a field where both art and science converge to create models that can predict outcomes based on data. One of the most effective strategies employed in ML to enhance model performance is ensemble methods.

Rather than relying on a single model, ensemble methods combine multiple models to produce better results. This approach can significantly boost accuracy, reduce overfitting, and improve generalization. 

In this blog, we’ll explore various ensemble techniques, their working principles, and their applications in real-world scenarios.

 

 

What Are Ensemble Methods?

Ensemble methods are techniques that create multiple models and then combine them to produce a more accurate and robust final prediction. The idea is that by aggregating the predictions of several base models, the ensemble can capture the strengths of each individual model while mitigating their weaknesses.

 

Also explore this: Azure Machine Learning in 5 Simple Steps

 

Why Use Ensemble Methods? 

Ensemble methods are used to improve the robustness and generalization of machine learning models by combining the predictions of multiple models. This can reduce overfitting and improve performance on unseen data.

 

Read more Gini Index and Entropy

 

Types of Ensemble Methods 

There are three primary types of ensemble methods: Bagging, Boosting, and Stacking. 

Bagging (Bootstrap Aggregating) 

Bagging involves creating multiple subsets of the original dataset using bootstrap sampling (random sampling with replacement). Each subset is used to train a different model, typically of the same type, such as decision trees. The final prediction is made by averaging (for regression) or voting (for classification) the predictions of all models.

 

 

How Bagging Works: 

• Bootstrap Sampling: Create multiple subsets from the original dataset by sampling with replacement. 

• Model Training: Train a separate model on each subset. 

• Aggregation: Combine the predictions of all models by averaging (regression) or majority voting (classification). 

Random Forest 

Random Forest is a popular bagging method where multiple decision trees are trained on different subsets of the data, and their predictions are averaged to get the final result. 

Boosting 

Boosting is a sequential ensemble method where models are trained one after another, each new model focusing on the errors made by the previous models. The final prediction is a weighted sum of the individual model’s predictions.

 

 

How Boosting Works:

• Initialize Weights: Start with equal weights for all data points. 

• Sequential Training: Train a model and adjust weights to focus more on misclassified instances. 

• Aggregation: Combine the predictions of all models using a weighted sum. 

AdaBoost (Adaptive Boosting)  

It assigns weights to each instance, with higher weights given to misclassified instances. Subsequent models focus on these hard-to-predict instances, gradually improving the overall performance. 

 

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Gradient Boosting 

It builds models sequentially, where each new model tries to minimize the residual errors of the combined ensemble of previous models using gradient descent. 

XGBoost (Extreme Gradient Boosting) 

An optimized version of Gradient Boosting, known for its speed and performance, is often used in competitions and real-world applications. 

Stacking 

Stacking, or stacked generalization, involves training multiple base models and then using their predictions as inputs to a higher-level meta-model. This meta-model is responsible for making the final prediction.

 

 

How Stacking Works: 

• Base Model Training: Train multiple base models on the training data. 

• Meta-Model Training: Use the predictions of the base models as features to train a meta-model. 

Example: 

A typical stacking ensemble might use logistic regression as the meta-model and decision trees, SVMs, and KNNs as base models.

 

 

Benefits of Ensemble Methods

Improved Accuracy 

By combining multiple models, ensemble methods can significantly enhance prediction accuracy. 

Robustness 

Ensemble models are less sensitive to the peculiarities of a particular dataset, making them more robust and reliable. 

Reduction of Overfitting 

By averaging the predictions of multiple models, ensemble methods reduce the risk of overfitting, especially in high-variance models like decision trees. 

Versatility 

Ensemble methods can be applied to various types of data and problems, from classification to regression tasks. 

Applications of Ensemble Methods 

Ensemble methods have been successfully applied in various domains, including: 

• Healthcare: Improving the accuracy of disease diagnosis by combining different predictive models.
• Finance: Enhancing stock price prediction by aggregating multiple financial models.
• Computer Vision: Boosting the performance of image classification tasks with ensembles of CNNs.

 

Here’s a list of the top 7 books to master your learning on computer vision

 

Implementing Random Forest in Python 

Now let’s walk through the implementation of a Random Forest classifier in Python using the popular scikit-learn library. We’ll use the Iris dataset, a well-known dataset in the machine learning community, to demonstrate the steps involved in training and evaluating a Random Forest model. 

Explanation of the Code 

Import Necessary Libraries

 

We start by importing the necessary libraries. numpy is used for numerical operations, train_test_split for splitting the dataset, RandomForestClassifier for building the model, accuracy_score for evaluating the model, and load_iris to load the Iris dataset. 

Load the Iris Dataset

 

The Iris dataset is loaded using load_iris(). The dataset contains four features (sepal length, sepal width, petal length, and petal width) and three classes (Iris setosa, Iris versicolor, and Iris virginica). 

Split the Dataset

 

We split the dataset into training and testing sets using train_test_split(). Here, 30% of the data is used for testing, and the rest is used for training. The random_state parameter ensures the reproducibility of the results. 

Initialize the RandomForestClassifier

 

We create an instance of the RandomForestClassifier with 100 decision trees (n_estimators=100). The random_state parameter ensures that the results are reproducible. 

Train the Model

 

We train the Random Forest classifier on the training data using the fit() method. 

Make Predictions

 

After training, we use the predict() method to make predictions on the testing data. 

Evaluate the Model

 

Finally, we evaluate the model’s performance by calculating the accuracy using the accuracy_score() function. The accuracy score is printed to two decimal places. 

Output Analysis 

When you run this code, you should see an output similar to:

 

 

This output indicates that the Random Forest classifier achieved 100% accuracy on the testing set. This high accuracy is expected for the Iris dataset, as it is relatively small and simple, making it easy for many models to achieve perfect or near-perfect performance.

In practice, the accuracy may vary depending on the complexity and nature of the dataset, but Random Forests are generally robust and reliable classifiers. 

By following this guided practice, you can see how straightforward it is to implement a Random Forest model in Python. This powerful ensemble method can be applied to various datasets and problems, offering significant improvements in predictive performance.

 

 

 

 

Summing it Up

To sum up, Ensemble methods are powerful tools in the machine learning toolkit, offering significant improvements in predictive performance and robustness. By understanding and applying techniques like bagging, boosting, and stacking, you can create models that are more accurate and reliable. 

Ensemble methods are not just theoretical constructs; they have practical applications in various fields. By leveraging the strengths of multiple models, you can tackle complex problems with greater confidence and precision.

The development of generative AI relies on important machine-learning techniques in today’s technological advancement. It makes machine learning (ML) a critical component of data science where algorithms are statistically trained on data.

 

 

An ML model learns iteratively to make accurate predictions and take actions. It enables computer programs to perform tasks without depending on programming. Today’s recommendation engines are one of the most innovative products based on machine learning.

Exploring Important Machine-Learning Techniques

 

 

The realm of ML is defined by several learning methods, each aiming to improve the overall performance of a model. Technological advancement has resulted in highly sophisticated algorithms that require enhanced strategies for training models.

Let’s look at some of the critical and cutting-edge machine-learning techniques of today.

Transfer Learning

This technique is based on training a neural network on a base model and using the learning to apply the same model to a new task of interest. Here, the base model represents a task similar to that of interest, enabling the model to learn the major data patterns.

 

 

• Why use transfer learning? It leverages knowledge gained from the first (source) task to improve the performance of the second (target) task. As a result, you can avoid training a model from scratch for related tasks. It is also a useful machine-learning technique when data for the task of interest is limited.
• Pros: Transfer learning enhances the efficiency of computational resources as the model trains on target tasks with pre-learned patterns. Moreover, it offers improved model performance and allows the reusability of features in similar tasks.
• Cons: This machine-learning technique is highly dependent on the similarity of two tasks. Hence, it cannot be used for extremely dissimilar and if applied to such tasks, it risks overfitting the source task during the model training phase.

 

 

Learn more about Transfer Learning

 

Fine-Tuning

Fine-tuning is a machine-learning technique that aims to support the process of transfer learning. It updates the weights of a model trained on a source task to enhance its adaptability to the new target task. While it looks similar to transfer learning, it does not involve replacing all the layers of a pre-trained network.

 

 

• Why use fine-tuning? It is useful to enhance the adaptability of a pre-trained model on a new task. It enables the ML model to refine its parameters and learn task-specific patterns needed for improved performance on the target task.
• Pros: This machine-learning technique is computationally efficient and offers improved adaptability to an ML model when dealing with transfer learning. The utilization of pre-learned features becomes beneficial when the target task has a limited amount of data.
• Cons: Fine-tuning is sensitive to the choice of hyperparameters and you cannot find the optimal settings right away. It requires experimenting with the model training process to ensure optimal results. Moreover, it also has the risk of overfitting and limited adaptation in case of high dissimilarity in source and target tasks.

 

Another interesting read: Hyperparameter tuning for ML models

 

Multitask Learning

As the name indicates, the multitask machine-learning technique unlocks the power of simultaneity.  Here, a model is trained to perform multiple tasks at the same time, sharing the knowledge across these tasks.

 

 

• Why use multitask learning? It is useful in sharing common representations across multiple tasks, offering improved generalization. You can use it in cases where several related ML tasks can benefit from shared representations.
• Pros: The enhanced generalization capability of models ensures the efficient use of data. Leveraging information results in improved model performance and regularization of training. Hence, it results in the creation of more robust training models.
• Cons: The increased complexity of this machine-learning technique requires advanced architecture and informed weightage of different tasks. It also depends on the availability of large and diverse datasets for effective results. Moreover, the dissimilarity of tasks can result in unwanted interference in the model performance of other tasks.

 

Read more about machine learning here

 

Federated Learning

It is one of the most advanced machine-learning techniques that focuses on decentralized model training. As a result, the data remains on the user-end devices, and the model is trained locally. It is a revolutionized ML methodology that enhances collaboration among decentralized devices.

 

 

• Why use federated learning? Federated learning is focused on locally trained models that do not require the sharing of raw data of end-user devices. It enables the sharing of key parameters through ML models while not requiring an exchange of sensitive data.
• Pros: This machine-learning technique addresses the privacy concerns in ML training. The decentralized approach enables increased collaborative learning with reduced reliance on central servers for ML processes. Moreover, this method is energy-efficient as models are trained locally.
• Cons: It cannot be implemented in resource-constrained environments due to large communication overhead. Moreover, it requires compatibility between local data and the global model at the central server, limiting its ability to handle heterogeneous datasets.

 

 

Factors determining the Best Machine-Learning Technique

While there are numerous machine-learning techniques available for model training today, it is crucial to make the right choice for your business. Below is a list of important factors that you must consider when selecting an ML method for your processes.

 

 

Context Matters!

Context refers to the type of problem or task at hand. The requirements and constraints of the model-training process is pivotal in choosing an ML technique. For instance, transfer learning and fine-tuning promote knowledge sharing, multitask learning promotes simultaneity, and federated learning supports decentralization.

 

Also learn about ML algorithms

 

Data Availability and Complexity

ML processes require large datasets to develop high-performing models. Hence, the amount and complexity of data determine the choice of method. While transfer learning and multitask learning need large amounts of data, fine-tuning is suitable for a limited dataset. Moreover, data complexity determines knowledge sharing and feature interactions.

Computational Resources

Large neural networks and complex machine-learning techniques require large computational power. The availability of hardware resources and time required for training are important measures of consideration when making your choice of the right ML method.

Data Privacy Considerations

With rapidly advancing technological processes, ML and AI have emerged as major tools that heavily rely on available datasets. It makes data a highly important part of the process, leading to an increase in privacy concerns and protection of critical information. Hence, your choice of machine-learning technique must fulfill your data privacy demands.

 

To explore more about Data Ethics, click here

 

Make an Informed Choice!

In conclusion, it is important to understand the specifications of the four important machine-learning techniques before making a choice. Each method has its requirements and offers unique benefits. It is crucial to understand the dimensions of each technique in the light of key considerations discussed above. Hence, make an informed choice for your ML training processes.

 

In this blog, we’re diving into a new approach called rank-based encoding that promises not just to shake things up but to guarantee top-notch results.

Rank-Based Encoding – A Breakthrough?

Say hello to rank-based encoding – a technique you probably haven’t heard much about yet, but one that’s about to change the game.

 

 

In the vast world of machine learning, getting your data ready is like laying the groundwork for success. One key step in this process is encoding – a way of turning non-numeric information into something our machine models can understand. This is particularly important for categorical features – data that is not in numbers.

Join us as we explore the tricky parts of dealing with non-numeric features, and how rank-based encoding steps in as a unique and effective solution. Get ready for a breakthrough that could redefine your machine-learning adventures – making them not just smoother but significantly more impactful.

 

Here’s a list of common data analyst interview questions for you

 

Problem Under Consideration

In our blog, we’re utilizing a dataset focused on House Price Prediction to illustrate various encoding techniques with examples. In this context, we’re treating the city categorical feature as our input, while the output feature is represented by the price.

 

 

 

Some Common Techniques

The following section will cover some of the commonly used techniques and their challenges. We will conclude by digging deeper into rank-based encoding and how it overcomes these challenges.

 

 

• One-Hot Encoding  

In One-hot encoding, each category value is represented as an n-dimensional, sparse vector with zero entries except for one of the dimensions. For example, if there are three values for the categorical feature City, i.e. Chicago, Boston, Washington DC, the one-hot encoded version of the city will be as depicted in Table 1.

If there is a wide range of categories present in a categorical feature, one-hot encoding increases the number of columns(features) linearly which requires high computational power during the training phase.  

 

City	City Chicago	City Boston	Washington DC
Chicago	1	0	0
Boston	0	1	0
Washington DC	0	0	1

  Table 1 

 

• Label encoding  

This technique assigns a label to each value of a categorical column based on alphabetical order. For example, if there are three values for the categorical feature City, i.e. Chicago, Boston, Washington DC, the label encoded version will be as depicted in Table 2.

Since B comes first in alphabetical order, this technique assigns Boston the label 0, which leads to meaningless learning of parameters.  

 

City	City Label Encoding
Chicago	1
Boston	0
Washington DC	2

Table 2 

 

• Binary encoding  

It involves converting each category into a binary code and then splitting the resulting binary string into columns. For example, if there are three values for the categorical feature City, i.e. Chicago, Boston, Washington DC, the binary encoded version of a city can be observed from Table 3.

Since there are 3 cities, two bits would be enough to uniquely represent each category. Therefore, two columns will be constructed which increases dimensions. However, this is not meaningful learning as we are assigning more weightage to one category than others.

Chicago is assigned 00, so our model would give it less weightage during the learning phase. If any categorical column has a wide range of unique values, this technique requires a large amount of computational power, as an increase in the number of bits results in an increase in the number of dimensions (features) significantly. 

 

City	City 0	City 1
Chicago	0	0
Boston	0	1
Washington DC	1	0

  Table 3 

 

• Hash encoding  

It uses the hashing function to convert category data into numerical values. Using hashed functions solves the problem of a high number of columns if the categorical feature has a large number of categories. We can define how many numerical columns we want to encode our feature into.

However, in the case of high cardinality of a categorical feature, while mapping it into a lower number of numerical columns, loss of information is inevitable. If we use a hash function with one-to-one mapping, the result would be the same as one-hot encoding.

 

You can also explore different types of statistical distributions

 

• Rank-based Encoding

In this blog, we propose rank-based encoding which aims to encode the data in a meaningful manner with no increase in dimensions. Thus, eliminating the increased computational complexity of the algorithm as well as preserving all the information of the feature.

Rank-based encoding works by computing the average of the target variable against each category of the feature under consideration. This average is then sorted in decreasing order from high to low and each category is assigned a rank based on the corresponding average of a target variable. An example is illustrated in Table 4 which is explained below:

The average price of Washington DC = (60 + 55)/2 = 57.5 Million 

The average price of Boston = (20 +12+18)/3 = 16.666 Million 

The average price of Chicago = (40 + 35)/2 = 37.5 Million

In the rank-based encoding process, each average value is assigned a rank in descending order.

For instance, Washington DC is given rank 1, Chicago gets rank 2, and Boston is assigned rank 3. This technique significantly enhances the correlation between the city (input feature) and price variable (output feature), ensuring more efficient model learning.

 

 

In my evaluation, I assessed model metrics such as R2 and RMSE. The results demonstrated significantly lower values compared to other techniques discussed earlier, affirming the effectiveness of this approach in improving overall model performance. 

 

City	Price	City Rank
Washington DC	60 Million	1
Boston	20 Million	3
Chicago	40 Million	2
Chicago	35 Million	2
Boston	12 Million	3
Washington DC	55 Million	1
Boston	18 Million	3

Table 4 

 

Dig deeper into understanding what is categorical data encoding

 

Results

We summarize the pros and cons of each technique in Table 5. Rank-based encoding emerges to be the best in all aspects. Effective data preprocessing is crucial for the optimal performance of machine learning models. Among the various techniques, rank-based encoding is a powerful method that contributes to enhanced model learning.

The rank-based encoding technique facilitates a stronger correlation between input and output variables, leading to improved model performance. The positive impact is evident when evaluating the model using metrics like RMSE and R2 etc. In our case, these enhancements reflect a notable boost in overall model performance. 

 

Encoding Technique	Meaningful Learning	Loss of Information	Increase in Dimensionality
One-hot	✓	x	✓
Label	x	x	✓
Binary	x	x	✓
Hash	✓	✓	x
Rank-based	✓	x	x

Table 5 

What can be a better way to spend your days listening to interesting bits about trending AI and Machine learning topics? Here’s a list of the 10 best AI and ML podcasts.

 

 

1. Future of Data and AI Podcast

Hosted by Data Science Dojo

Throughout history, we’ve chased the extraordinary. Today, the spotlight is on AI—a game-changer, redefining human potential, augmenting our capabilities, and fueling creativity. Curious about AI and how it is reshaping the world? You’re right where you need to be.

The Future of Data and AI podcast hosted by the CEO and Chief Data Scientist at Data Science Dojo, dives deep into the trends and developments in AI and technology, weaving together the past, present, and future. It explores the profound impact of AI on society, through the lens of the most brilliant and inspiring minds in the industry.

 

 

2. The TWIML AI Podcast (formerly This Week in Machine Learning & Artificial Intelligence)

Hosted by Sam Charrington

Artificial intelligence and machine learning are fundamentally altering how organizations run and how individuals live. It is important to discuss the latest innovations in these fields to gain the most benefit from technology. The TWIML AI Podcast outreaches a large and significant audience of ML/AI academics, data scientists, engineers, tech-savvy business, and IT (Information Technology) leaders, as well as the best minds and gather the best concepts from the area of ML and AI.  

The podcast is hosted by a renowned industry analyst, speaker, commentator, and thought leader Sam Charrington. Artificial intelligence, deep learning, natural language processing, neural networks, analytics, computer science, data science, and other technologies are discussed. 

3. The AI Podcast

Hosted by NVIDIA

One individual, one interview, one account. This podcast examines the effects of AI on our world. The AI podcast creates a real-time oral history of AI that has amassed 3.4 million listens and has been hailed as one of the best AI and machine learning podcasts.

They always bring you a new story and a new 25-minute interview every two weeks. Consequently, regardless of the difficulties, you are facing in marketing, mathematics, astrophysics, paleo history, or simply trying to discover an automated way to sort out your kid’s growing Lego pile, listen in and get inspired.

 

Here are 6 Books to Help you Learn Data Science

 

4. DataFramed

Hosted by DataCamp

DataFramed is a weekly podcast exploring how artificial intelligence and data are changing the world around us. On this show, we invite data & AI leaders at the forefront of the data revolution to share their insights and experiences into how they lead the charge in this era of AI.

Whether you’re a beginner looking to gain insights into a career in data & AI, a practitioner needing to stay up-to-date on the latest tools and trends, or a leader looking to transform how your organization uses data & AI, there’s something here for everyone.

5. Data Skeptic

Hosted by Kyle Polich

Data Skeptic launched as a podcast in 2014. Hundreds of interviews and tens of millions of downloads later, it is a widely recognized authoritative source on data science, artificial intelligence, machine learning, and related topics. 

The Data Skeptic Podcast features interviews and discussion of topics related to data science, statistics, machine learning, artificial intelligence, and the like, all from the perspective of applying critical thinking and the scientific method to evaluate the veracity of claims and efficacy of approaches.

Data Skeptic runs in seasons. By speaking with active scholars and business leaders who are somehow involved in our season’s subject, we probe it. 

Data Skeptic is a boutique consulting company in addition to its podcast. Kyle participates directly in each project the team undertakes. Our work primarily focuses on end-to-end machine learning, cloud infrastructure, and algorithmic design. 

       

 Pro-tip: Enroll in the Large Language Models Bootcamp today to get ahead in the world of Generative AI

 

 

 

 

6. Last Week in AI

Hosted by Skynet Today

Tune in to Last Week in AI for your weekly dose of insightful summaries and discussions on the latest advancements in AI, deep learning, robotics, and beyond. Whether you’re an enthusiast, researcher, or simply curious about the cutting-edge developments shaping our technological landscape, this podcast offers insights on the most intriguing topics and breakthroughs from the world of artificial intelligence.

7. Everyday AI

Hosted by Jordan Wilson

Discover The Everyday AI podcast, your go-to for daily insights on leveraging AI in your career. Hosted by Jordan Wilson, a seasoned martech expert, this podcast offers practical tips on integrating AI and machine learning into your daily routine.

Stay updated on the latest AI news from tech giants like Microsoft, Google, Facebook, and Adobe, as well as trends on social media platforms such as Snapchat, TikTok, and Instagram. From software applications to innovative tools like ChatGPT and Runway ML, The Everyday AI has you covered. 

8. Learning Machines 101

Smart machines employing artificial intelligence and machine learning are prevalent in everyday life. The objective of this podcast series is to inform students and instructors about the advanced technologies introduced by AI and the following: 

•  How do these devices work? 
• Where do they come from? 
• How can we make them even smarter? 
• And how can we make them even more human-like

9. Practical AI: Machine Learning, Data Science

Hosted by Changelog Media

Making artificial intelligence practical, productive, and accessible to everyone. Practical AI is a show in which technology professionals, businesspeople, students, enthusiasts, and expert guests engage in lively discussions about Artificial Intelligence and related topics (Machine Learning, Deep Learning, Neural Networks, GANs (Generative adversarial networks), MLOps (machine learning operations) (machine learning operations), AIOps, and more).

The focus is on productive implementations and real-world scenarios that are accessible to everyone. If you want to keep up with the latest advances in AI, while keeping one foot in the real world, then this is the show for you! 

10. The Artificial Intelligence Podcast

Hosted by Dr. Tony Hoang

The Artificial Intelligence podcast talks about the latest innovations in the artificial intelligence and machine learning industry. The recent episode of the podcast discusses text-to-image generators, Robot dogs, soft robotics, voice bot options, and a lot more.

 

 

Have we missed any of your favorite podcasts?

 Do not forget to share in the comments the names of your favorite AI and ML podcasts. Read this amazing blog if you want to know about Data Science podcasts.

Be it Netflix, Amazon, or another mega-giant, their success stands on the shoulders of experts, analysts are busy deploying machine learning through supervised, unsupervised, and reinforcement successfully. 

The tremendous amount of data being generated via computers, smartphones, and other technologies can be overwhelming, especially for those who do not know what to make of it. To make the best use of data researchers and programmers often leverage machine learning for an engaging user experience.

Many advanced techniques that are coming up every day for data scientists of all supervised, and unsupervised, reinforcement learning is leveraged often. In this article, we will briefly explain what supervised, unsupervised, and reinforcement learning is, how they are different, and the relevant uses of each by well-renowned companies.

 

 

Supervised learning

Supervised machine learning is used for making predictions from data. To be able to do that, we need to know what to predict, which is also known as the target variable. The datasets where the target label is known are called labeled datasets to teach algorithms that can properly categorize data or predict outcomes. Therefore, for supervised learning:

• We need to know the target value
• Targets are known in labeled datasets

Let’s look at an example: If we want to predict the prices of houses, supervised learning can help us predict that. For this, we will train the model using characteristics of the houses, such as the area (sq ft.), the number of bedrooms, amenities nearby, and other similar characteristics, but most importantly the variable that needs to be predicted – the price of the house.

A supervised machine learning algorithm can make predictions such as predicting the different prices of the house using the features mentioned earlier, predicting trends of future sales, and many more.

Sometimes this information may be easily accessible while other times, it may prove to be costly, unavailable, or difficult to obtain, which is one of the main drawbacks of supervised learning.

Saniye Alabeyi, Senior Director Analyst at Garnet calls Supervised learning the backbone of today’s economy, stating:

“Through 2022, supervised learning will remain the type of ML utilized most by enterprise IT leaders” (Source).

 

 

Types of problems:

Supervised learning deals with two distinct kinds of problems:

1. Classification problems
2. Regression problems

Classification problem: In the case of classification problems, examples are classified into one or more classes/ categories.

For example, if we are trying to predict that a student will pass or fail based on their past profile, the prediction output will be “pass/fail.” Classification problems are often resolved using algorithms such as Naïve Bayes, Support Vector Machines, Logistic Regression, and many others.

Regression problem: A problem in which the output variable is either a real or continuous value, s is defined as a regression problem. Bringing back the student example, if we are trying to predict that a student will pass or fail based on their past profuse, the prediction output will be numeric, such as “68%” likely to score.

Predicting the prices of houses in an area is an example of a regression problem and can be solved using algorithms such as linear regression, non-linear regression, Bayesian linear regression, and many others.

 

Here’s a comprehensive guide to Machine Learning Model Deployment

 

Why Amazon, Netflix, and YouTube are great fans of supervised learning?

Recommender systems are a notable example of supervised learning. E-commerce companies such as Amazon, streaming sites like Netflix, and social media platforms such as TikTok, Instagram, and even YouTube among many others make use of recommender systems to make appropriate recommendations to their target audience.

Unsupervised learning

Imagine receiving swathes of data with no obvious pattern in it. A dataset with no labels or target values cannot come up with an answer to what to predict. Does that mean the data is all waste? Nope! The dataset likely has many hidden patterns in it.

Unsupervised learning studies the underlying patterns and predicts the output. In simple terms, in unsupervised learning, the model is only provided with the data in which it looks for hidden or underlying patterns.

Unsupervised learning is most helpful for projects where individuals are unsure of what they are looking for in data. It is used to search for unknown similarities and differences in data to create corresponding groups.

An application of unsupervised learning is the categorization of users based on their social media activities.

Commonly used unsupervised machine learning algorithms include K-means clustering, neural networks, principal component analysis, hierarchical clustering, and many more.

 

 

Reinforcement learning

Another type of machine learning is reinforcement learning.

In reinforcement learning, algorithms learn in an environment on their own. The field has gained quite some popularity over the years and has produced a variety of learning algorithms.

Reinforcement learning is neither supervised nor unsupervised as it does not require labeled data or a training set. It relies on the ability to monitor the response to the actions of the learning agent.

Most used in gaming, robotics, and many other fields, reinforcement learning makes use of a learning agent. A start state and an end state are involved. For the learning agent to reach the final or end stage, different paths may be involved.

• An agent may also try to manipulate its environment and may travel from one state to another
• On success, the agent is rewarded but does not receive any reward or appreciation for failure
• Amazon has robots picking and moving goods in warehouses because of reinforcement learning

 

Also learn about Retrieval Augmented Generation

 

Numerous IT companies including Google, IBM, Sony, Microsoft, and many others have established research centers focused on projects related to reinforcement learning.

Social media platforms like Facebook have also started implementing reinforcement learning models that can consider different inputs such as languages, integrate real-world variables such as fairness, privacy, and security, and more to mimic human behavior and interactions. (Source)

Amazon also employs reinforcement learning to teach robots in its warehouses and factories how to pick up and move goods.

Comparison between supervised, unsupervised, and reinforcement learning

Caption: Differences between supervised, unsupervised, and reinforcement learning algorithms

	Supervised learning	Unsupervised learning	Reinforcement learning
Definition	Makes predictions from data	Segments and groups data	Reward-punishment system and interactive environment
Types of data	Labeled data	Unlabeled data	Acts according to a policy with a final goal to reach (No or predefined data)
Commercial value	High commercial and business value	Medium commercial and business value	Little commercial use yet
Types of problems	Regression and classification	Association and Clustering	Exploitation or Exploration
Supervision	Extra supervision	No	No supervision
Algorithms	Linear Regression, Logistic Regression, SVM, KNN and so forth	K – Means clustering,  C – Means, Apriori	Q – Learning,  SARSA
Aim	Calculate outcomes	Discover underlying patterns	Learn a series of action
Application	Risk Evaluation, Forecast Sales	Recommendation System, Anomaly Detection	Self-Driving Cars, Gaming, Healthcare

Which is the better Machine Learning technique?

We learned about the three main members of the machine learning family essential for deep learning. Other kinds of learning are also available such as semi-supervised learning, or self-supervised learning.

Supervised, unsupervised, and reinforcement learning, are all used for different to complete diverse kinds of tasks. No single algorithm exists that can solve every problem, as problems of different natures require different approaches to resolve them.

 

 

Despite the many differences between the three types of learning, all of these can be used to build efficient and high-value machine learning and Artificial Intelligence applications. All techniques are used in different areas of research and development to help solve complex tasks and resolve challenges.

 

 

If you would like to learn more about data science, machine learning, and artificial intelligence, visit the Data Science Dojo blog.

 

Written by Alyshai Nadeem

Artificial intelligence and machine learning are part of our everyday lives. These data science movies are my favorite.

Advanced artificial intelligence (AI) systems, humanoid robots, and machine learning are not just in science fiction movies anymore. We come across this technological advancement in our everyday life. Today our cellphones, cars, TV sets, and even household appliances are using machine learning to improve themselves.

As we advance towards faster connectivity and the possibility of making the Internet of Things (IoT) more common, the idea of machines taking over and controlling humans might sound funny, but there are some challenges that need attention, including ethical and moral dimensions of machines thinking and acting like humans.

Interstellar (2014)

 

 

Christopher Nolan’s cinematic success won an Oscar for Best Visual Effects and grossed over $677 million worldwide. The film is centered around astronauts’ journey to the far reaches of our galaxy to find a suitable planet for life as Earth is slowly dying.

The lead character played by Oscar winner Matthew McConaughey, an astronaut and spaceship pilot, along with mission commander Brand and science specialists are heading towards a newly discovered wormhole.

The mission takes the astronauts on a spectacular interstellar journey through time and space, but at the same time, they miss out on their own life back home light years away. On board the spaceship, Endurance is a pair of quadrilateral robots called TARS and CASE. They surprisingly resemble the monoliths from 2001: A Space Odyssey.

TARS is one of the crew members of Mission Endurance. TARS’ personality is witty, sarcastic, and humorous, traits programmed into him to make him a suitable companion for its human crew on this decades-long journey.

CASE’s mission is the maintenance and operations of the Endurance in the absence of human crew members. CASE’s personality is quiet and reserved as opposed to TARS. TARS and CASE are true embodiments of the progress that human beings have made in AI technology, thus promising us great adventures in the future.

The Imitation Game (2014)

 

 

Based on the real-life story of Alan Turing, A.K.A. the father of modern computer science, The Imitation Game is centered around Turing and his team of code-breakers at top secret British Government Code and Cipher School. They’re determined to decipher the Nazi German military code called “Enigma”.

Enigma is a key part of the Nazi military strategy to safely transmit important information to its units. To crack this Enigma, Turing created a primitive computer system that would consider permutations at a faster rate than any human.

This achievement helped Allied forces ensure victory over Nazi German in the second world war. The movie not only portrays the impressive life of Alan Turning but also describes the important process of creating the first ever machine of its kind giving birth to the field of cryptography and cyber security.

The Terminator (1984)

 

 

The cult classic, Terminator, starring Arnold Schwarzenegger as a cyborg assassin from the future is the perfect combination of action, sci-fi technology, and personification of machine learning.

The humanistic cyborg was created by Cyberdyne Systems and is known as T-800 model 101. Designed specifically for infiltration and combat and is sent on a mission to kill Sarah Connor before she gives birth to John Connor, who would become the ultimate savior for humanity after the robotic uprising.

In this classic, we get to see advanced artificial intelligence in the works and how it has considered humanity the biggest threat to the world. Bent upon total destruction of the human race, only freedom fighters led by John Connor stand in their way. Therefore, sending The Terminator back in time to alter their future is the top priority.

Blade Runner 2049 (2017)

 

 

The sequel to the 1982 original Blade Runner has impressive visuals capturing the audience’s attention throughout the film. The story is about bio-engineered humans known as “Replicants” After the uprising of 2022 they are being hunted down by LAPD Blade Runner.

Blade Runner is an officer who hunts and retires (kills) rogue replicants. Ryan Gosling stars as “K” hunting down replicants who are considered a threat to the world. Every decision he makes is based on analysis.

The films explore the relationships and emotions of artificially intelligent beings and raise moral questions regarding the freedom to live and the life of self-aware technology.

I, Robot (2004)

 

 

Will Smith stars as Chicago policeman Del Spooner in the year 2035. He is highly suspicious of the AI technology, data science, and robots are being used as household helpers. One of these mass-produced robots (cueing in the data science / AI angle), named Sonny, goes rogue and is held responsible for the death of its owner.

Its owner falls from a window on the 15^th floor. Del investigates this murder and discovers a larger threat to humanity by Artificial Intelligence. As the investigation continues, there are multiple murder attempts on Del but he manages to barely escape with his life.

The police detective continues to unravel mysterious threats from AI technology and tries to stop the mass uprising.

Minority Report (2002)

 

 

Minority Report and Data Science? That is correct! It is a 2002 action thriller directed by Steven Spielberg and starring Tom Cruise. The most common use of data science is using current data to infer new information, but here data are being used to predict crime predispositions.

A group of humans gifted with psychic abilities (PreCogs) provide the Washington police force with information about crimes before they are committed. Using visual data and other information by PreCogs, it is up to the PreCrime police unit to use data to explore the finer details of a crime in order to prevent it.

However, things take a turn for the worse when one day PreCogs predict John Anderson one of their own, is going to commit murder. To prove his innocence, he goes on a mission to find the “Minority Report” which is the prediction of the PreCog Agatha that might tell a different story and prove John’s innocence.

Her (2013)

 

 

Her (2013) is a Spike Jones science fiction film starring Joaquin Phoenix as Theodore Twombly, a lonely and depressed writer. He is going through a divorce at the time, and to make things easier, purchases an advanced operating system with an A.I. virtual assistant designed to adapt and evolve.

The virtual assistant names itself Samantha. Theodore is amazed at the operating system’s ability to emotionally connect with him. Samantha uses its highly advanced intelligence system to help with every one of Theodore’s needs, but now he’s facing an inner conflict of being in love with a machine.

Ex-Machina (2014)

 

 

The story is centered around a 26-year-old programmer, Caleb, who wins a competition to spend a week at a private mountain retreat belonging to the CEO of Blue Book, a search engine company. Soon afterward Caleb realizes he’s participating in an experiment to interact with the world’s first real artificially intelligent robot.

In this British science fiction, AI does not want world domination but simply wants the same civil rights as humans.

The Machine (2013)

 

 

The Machine is an Indie-British film centered around two artificial intelligence engineers who come together to create the first-ever, self-aware artificial intelligence machines. These machines are created for the Ministry of Defense.

The Government intends to create a lethal soldier for war. The cyborg told its designer, “I’m a part of the new world and you’re part of the old.” this chilling statement gives you an idea of what is to come next.

Transcendence (2014)

 

 

Transcendence is a story about a brilliant researcher in the field of Artificial Intelligence, Dr. Will Caster, played by Johnny Depp. He’s working on a project to create a conscious machine that combines the collective intelligence of everything along with the full range of human emotions.

Dr. Caster has gained fame due to his ambitious project and controversial experiments. He’s also become a target for anti-technology extremists who are willing to do anything to stop him.

However, Dr. Caster becomes more determined to accomplish his ambitious goals and achieve the ultimate power. His wife Evelyn and best friend Max are concerned with Will’s unstoppable appetite for knowledge which is evolving into a terrifying quest for power.

A.I. ARTIFICIAL INTELLIGENCE (2001)

 

 

A.I. Artificial Intelligence is a science fiction drama directed by Steven Spielberg. The story takes us to the not-so-distant future where ocean waters are rising due to global warming and most coastal cities are flooded. Humans move to the interior of the continents and keep advancing their technology.

One of the newest creations is realistic robots known as “Mechas”. Mechas are humanoid robots, very complex but lack emotions. This changes when David, a prototype Mecha child capable of experiencing love, is developed. He is given to Henry and his wife Monica, whose son contracted a rare disease and has been placed in cryostasis.

David is providing all the love and support for his new family, but things get complicated when Monica’s real son returns home after a cure is discovered. The film explores every possible emotional interaction humans could have with an emotionally capable A.I. technology.

Moneyball (2011)

 

 

Billy Beane, played by Brad Pitt, and his assistant, Peter Brand (Jonah Hill), are faced with the challenge of building a winning team for the Major League Baseball’s Oakland Athletics’ 2002 season with a limited budget.

To overcome this challenge Billy uses Brand’s computer-generated statistical analysis to analyze and score players’ potential and assemble a highly competitive team. Using historical data and predictive modeling they manage to create a playoff-bound MLB team with a limited budget.

Margin Call (2011)

 

 

The 2011 American drama film written and directed by J.C. Chandor is based on the events of the 2007-08 global financial crises. The story takes place over a 24-hour period at a large Wall Street investment bank.

One of the junior risk analysts discovers a major flaw in the risk models which has led their firm to invest in the wrong things, winding up on the brink of financial disaster. A seemingly simple error is in fact affecting millions of lives. This is not only limited to the financial world.

An economic crisis like this caused by flawed behavior between humans and machines can have trickle-down effects on ordinary people. Technology doesn’t exist in a bubble, it affects everyone around it and spreads exponentially. Margin Call explores the impact of technology and data science on our lives.

21 (2008)

 

 

Ben Campbell, a mathematics student at MIT, is accepted at the prestigious Harvard Medical School but he’s unable to afford the $300,000 tuition. One of his professors at MIT, Micky Rosa (Kevin Spacey), asks him to join his blackjack team consisting of five other fellow students.

Ben accepts the offer to win enough cash to pay his Harvard tuition. They fly to Las Vegas over the weekend to win millions of dollars using numbers, codes, and hand signals. This movie gives insights into Newton’s method and Fibonacci numbers from the perspective of six brilliant students and their professors.

Thanks for reading we hope you will enjoy our recommendations on data science-based movies. Also, check out the 18 Best Data Science Podcasts.

Want to learn more about AI, Machine Learning, and Data Science? Check out Data Science Dojo’s online Data Science Bootcamp program!

 

Written by Muhammad Bilal Awan

Data Science Dojo has launched Grafana’s offering to the azure marketplace to help you harvest insights from your data. It leverages the power of Microsoft Azure services to visualize, query, and set alerts for your data while promoting teamwork and transparency.