Difference Between Machine Learning:
👉Tutorial-1:-Supervised vs Unsupervised Learning: Key Differences and Techniques Explained Great Opportunity-2025
👉Tutorial-2:-TensorFlow vs Theano vs Torch vs Keras: A Comparison of Deep Learning Libraries for AI Development Great Opportunity-2025
✅What is AI?
Artificial Intelligence (AI) is a field in computer science where machines are programmed to think, learn, and mimic human-like behavior such as reasoning, speech, vision, and problem-solving. AI aims to replicate human intelligence, which is still a distant goal.
AI is categorized into three levels:
- Narrow AI: This type of AI can perform specific tasks better than humans. It is the AI we have today.
- General AI: When AI reaches the ability to perform any intellectual task with the same accuracy as humans.
- Active AI: AI that surpasses humans in various tasks.
Early AI systems relied on pattern matching and expert systems.
✅What is Machine Learning (ML)?
Machine Learning (ML) is a subset of AI where computers are trained to automatically perform tasks that are either too tedious or complex for humans. It is used to analyze data and identify patterns using algorithms. ML algorithms learn from data and can make predictions or decisions with minimal human input.
In ML, algorithms are fed data, which helps them understand the relationship between input and output. Once the learning process is complete, the machine can predict the values or classifications of new data points.
✅What is Deep Learning (DL)?
Deep Learning (DL) is a subset of Machine Learning that mimics the human brain’s neural network. In DL, the machine uses multiple layers of neural networks to learn from data. Deep Learning’s “depth” refers to the number of layers in the neural network.
Deep learning is considered the state of the art in AI, offering high performance, especially with large datasets.
✅Key Differences Between Machine Learning and Deep Learning:
| Parameter | Machine Learning (ML) | Deep Learning (DL) |
|---|---|---|
| Data Dependency | Performs well with small/medium datasets | Requires large datasets for optimal performance |
| Hardware Dependency | Works on low-end machines | Requires powerful machines, preferably with GPU for processing |
| Feature Engineering | Requires manual feature selection | No need for manual feature selection, as neural networks learn features automatically |
| Execution Time | Ranges from minutes to hours | Can take weeks due to complex computations |
| Interpretability | Some algorithms (e.g., decision trees) are interpretable, others (e.g., SVM) are complex | Interpretation of the model is difficult to impossible |
✅Difference Between Machine Learning When to Use ML or DL?
| Parameter | Machine Learning (ML) | Deep Learning (DL) |
|---|---|---|
| Training Dataset Size | Small | Large |
| Feature Selection | Yes, manual selection of features is needed | No, neural networks learn features automatically |
| Number of Algorithms | Many algorithms available | Few algorithms (mostly neural networks) |
| Training Time | Shorter training time | Longer training time |
✅Machine Learning Process:
In ML, to build a model that recognizes objects (e.g., a bicycle, boat, car, or plane), you need a classifier. This classifier uses the features of an object to identify which class the object belongs to.
The ML training process includes:
- Collecting Data: Gathering relevant data (features).
- Training the Classifier: Using the data to train the algorithm.
- Making Predictions: The trained model predicts the class of new, unseen data.
In supervised learning, the data fed into the algorithm contains both features and labels. The classifier learns the relationship between the data and labels, then predicts the class of new data.
✅Difference Between Machine Learning Deep Learning Process:
In deep learning, a neural network model is used. The process involves:
- Input Data: Each input is passed through neurons in the neural network.
- Weighted Multiplication: Inputs are multiplied by weights and passed through layers.
- Layering Process: Each layer processes data and passes it to the next layer.
- Output Layer: The final layer outputs a result, such as a class label or predicted value.
Deep learning automatically extracts features from the data, unlike traditional machine learning, where manual feature extraction is needed.
✅Automating Feature Extraction with Deep Learning:
Deep learning, particularly Convolutional Neural Networks (CNNs), automates the feature extraction process. For image data, CNNs learn small features (e.g., edges) in the first layers and combine these to detect more complex patterns in deeper layers. This removes the need for manual feature extraction, which is common in traditional machine learning.
✅Difference Between Machine Learning Summary:
- AI enables machines to think and act like humans.
- Machine Learning allows systems to learn from data without being explicitly programmed.
- Deep Learning is a powerful subset of ML that uses neural networks with multiple layers for learning from data, especially large datasets. It performs exceptionally well for tasks like image recognition and text translation, where feature extraction is automated through deep neural networks.
Machine Learning and Deep Learning are both integral to advancing AI technologies, but they differ in terms of data requirements, hardware dependencies, and the complexity of the models they build.
