Machine learning and deep learning differences

7. Machine Learning and Deep learning

In this post, I will write about what are exactly machine learning and deep learning subfields, their differences, model training in python, some completed projects, and possible research areas in the fields so, let’s get started!

Difference between Machine Learning and Deep learning

Machine learning and deep learning are two subfields of artificial intelligence that involve the use of algorithms to learn patterns from data. Although they share some similarities, there are significant differences between them in terms of their approach, models, and capabilities. I have mentioned them right below:

  1. Approach:

Machine learning algorithms are designed to learn patterns in data by identifying and analyzing specific features or attributes that are relevant to the task at hand. These features are typically hand-crafted and chosen by experts in the domain. For example, in a spam detection system, features such as the presence of certain keywords or phrases may be used to identify spam emails.

Deep learning, on the other hand, is a subfield of machine learning that uses artificial neural networks to learn features directly from the raw data itself. This approach is known as feature learning, and it allows deep learning models to automatically learn and extract meaningful patterns from the data without the need for manual feature engineering. This makes deep learning models more flexible and adaptive to different types of data.

2. Models:

Machine learning algorithms include linear regression, logistic regression, decision trees, and support vector machines (SVMs), among others. These models typically have fewer parameters and are simpler to train than deep learning models.

Deep learning models, on the other hand, include deep neural networks, convolutional neural networks (CNNs), and recurrent neural networks (RNNs). These models have a significantly larger number of parameters and require more computation power and memory to train. They are capable of learning complex patterns in large datasets, such as images, video, and audio.

3. Capabilities:

Machine learning models are generally best suited for tasks that involve small to medium-sized datasets and well-defined features. They are typically used in applications such as fraud detection, customer segmentation, and recommendation systems.

Deep learning models, on the other hand, excel at handling large and complex datasets, particularly those that involve images, speech, and natural language processing. They are widely used in applications such as image recognition, object detection, speech recognition, and language translation.

Now let’s dive into a simple model development, first using machine learning later using deep learning. By this, you will get clear undestanding on the steps that one needs to follow while training a supervised model. The implementation is in Python.

Machine Learning Model

In this example, I am using logistic regression, a popular machine learning algorithm, to classify iris flowers based on their sepal length, sepal width, petal length, and petal width. I split the dataset into training and testing sets, train the model on the training set, and evaluate its accuracy on the testing set.

# Import libraries
from sklearn.datasets import load_iris
from sklearn.linear_model import LogisticRegression
from sklearn.model_selection import train_test_split

# Load dataset
iris = load_iris()

# Split dataset
X_train, X_test, y_train, y_test = train_test_split(,, test_size=0.2)

# Train model
model = LogisticRegression(), y_train)

# Evaluate model
score = model.score(X_test, y_test)
print('Accuracy:', score)

Deep Learning Model

In this example, I use a neural network for deep learning to classify the iris and MNIST datasets, respectively. The code shows how the models are trained, evaluated, and used to make predictions.

# Import libraries
import tensorflow as tf
from tensorflow import keras
from tensorflow.keras import layers

# Load dataset
mnist = keras.datasets.mnist
(X_train, y_train), (X_test, y_test) = mnist.load_data()

# Normalize pixel values
X_train = X_train / 255.0
X_test = X_test / 255.0

# Build neural network model
model = keras.Sequential([
    layers.Flatten(input_shape=(28, 28)),
    layers.Dense(128, activation='relu'),

# Compile model

# Train model, y_train, epochs=10)

# Evaluate model performance
test_loss, test_acc = model.evaluate(X_test, y_test, verbose=2)
print('Test accuracy:', test_acc)

There have been several accomplished projects in both machine learning and deep learning

    1. DeepMind’s Protein Folding: In 2018, DeepMind, a subsidiary of Google, developed an AI system that could predict the folding of proteins, which is a critical step in developing new drugs. The system, called AlphaFold, achieved remarkable accuracy, outperforming all other methods in a global competition. For more details:
    2. Tesla Autopilot: Tesla Autopilot is a suite of advanced driver assistance features that use machine learning algorithms to enable semi-autonomous driving. The system uses deep neural networks to process data from cameras, radar, and ultrasonic sensors to make decisions about acceleration, braking, and steering. Tesla Autopilot has been shown to reduce accidents and make driving safer for Tesla drivers. For more details visit here:
    3. Netflix recommendation system: Netflix’s recommendation system is a famous example of a successful application of machine learning. By analyzing users’ viewing history and behavior, the system is able to make personalized recommendations for new content to watch. This has helped to increase user engagement and retention on the platform. For more details:

Possible Research Areas

Here I am providing the list of some possible research areas in machine learning and deep learning

Machine Learning Research Domains

    1. Explainable AI: Explainable AI is an emerging area of research that focuses on making machine learning models more transparent and interpretable. This is important for applications in domains such as healthcare and finance where model decisions can have significant consequences.
    2. Active learning: Active learning is a type of machine learning where the model is allowed to choose which data it is trained on. This is useful when labeled data is difficult to obtain, as the model can focus on the most informative data.
    3. Reinforcement learning: Reinforcement learning is a type of machine learning where an agent interacts with an environment and learns to make decisions that maximize a reward signal. This has shown promising results in domains such as robotics, game playing, and autonomous systems.
    4. Transfer learning: Transfer learning is a technique in machine learning where a model trained on one task is reused for another related task. This can help reduce the amount of data needed to train a model for a new task and improve its performance.

Deep Learning Research Domains

    1. Generative models: Generative models are deep learning models that can generate new data samples similar to those in the training data. This area of research has many applications, including image and video synthesis, text generation, and music composition.
    2. Graph neural networks: Graph neural networks are a type of deep learning model that can operate on graph-structured data. This has applications in areas such as social network analysis, drug discovery, and traffic flow prediction.
    3. Continual learning or Incremental Learning: Continual learning is a research area that focuses on developing deep learning models that can learn continuously over time. This is important as many real-world applications require models to adapt to new data and tasks without forgetting previously learned information.

Here I am providing some useful research papers on deep learning and machine learning, for those who would like to explore it in details:

    1. Fernández-Delgado, M., Cernadas, E., Barro, S., & Amorim, D. (2014). Do we need hundreds of classifiers to solve real world classification problems?The journal of machine learning research15(1), 3133-3181.
    2. Fernández-Delgado, M., Sirsat, M. S., Cernadas, E., Alawadi, S., Barro, S., & Febrero-Bande, M. (2019). An extensive experimental survey of regression methods. Neural Networks111, 11-34.
    3. Shrestha, A., & Mahmood, A. (2019). Review of deep learning algorithms and architectures. IEEE access7, 53040-53065.
    4. Goodfellow, I., Bengio, Y., & Courville, A. (2016). Deep learningMIT press.

You can also check some interesting posts on machine learning from blog page:

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