Machine Learning Tutorial

Machine Learning Tutorial Machine Learning Life Cycle Python Anaconda setup Difference between ML/ AI/ Deep Learning Understanding different types of Machine Learning Data Pre-processing Supervised Machine Learning

ML Regression Algorithm

Linear Regression

ML Classification Algorithm

Introduction to ML Classification Algorithm Logistic Regression Support Vector Machine Decision Tree Naïve Bayes Random Forest

ML Clustering Algorithm

Introduction to ML Clustering Algorithm K-means Clustering Hierarchical Clustering

ML Association Rule learning Algorithm

Introduction to association Rule Learning Algorithm

How To

How to Learn AI and Machine Learning How Many Types of Learning are available in Machine Learning How to Create a Chabot in Python Using Machine Learning

ML Questions

What is Cross Compiler What is Artificial Intelligence And Machine Learning What is Gradient Descent in Machine Learning What is Backpropagation in a Neural Network Why is Machine Learning Important What Machine Learning Technique Helps in Answering the Question Is Data Science and Machine Learning Same


Difference between Machine Learning and Deep Learning Difference between Machine learning and Human Learning


Top 5 programming languages and their libraries for Machine Learning Basics Vectors in Linear Algebra in ML Decision Tree Algorithm in Machine Learning Bias and Variances in Machine Learning Machine Learning Projects for the Final Year Students Top Machine Learning Jobs Machine Learning Engineer Salary in Different Organisation Best Python Libraries for Machine Learning Regularization in Machine Learning Some Innovative Project Ideas in Machine Learning Decoding in Communication Process Working of ARP Hands-on Machine Learning with Scikit-Learn, TensorFlow, and Keras Kaggle Machine Learning Project Machine Learning Gesture Recognition Machine Learning IDE Pattern Recognition and Machine Learning a MATLAB Companion Chi-Square Test in Machine Learning Heart Disease Prediction Using Machine Learning Machine Learning and Neural Networks Machine Learning for Audio Classification Standardization in Machine Learning Student Performance Prediction Using Machine Learning Automated Machine Learning Hyper Parameter Tuning in Machine Learning IIT Machine Learning Image Processing in Machine Learning Recall in Machine Learning Handwriting Recognition in Machine Learning High Variance in Machine Learning Inductive Learning in Machine Learning Instance Based Learning in Machine Learning International Journal of Machine Learning and Computing Iris Dataset Machine Learning Disadvantages of K-Means Clustering Machine Learning in Healthcare Machine Learning is Inspired by the Structure of the Brain Machine Learning with Python Machine Learning Workflow Semi-Supervised Machine Learning Stacking in Machine Learning Top 10 Machine Learning Projects For Beginners in 2023 Train and Test datasets in Machine Learning Unsupervised Machine Learning Algorithms VC Dimension in Machine Learning Accuracy Formula in Machine Learning Artificial Neural Networks Images Autoencoder in Machine Learning Bias Variance Tradeoff in Machine Learning Disadvantages of Machine Learning Haar Algorithm for Face Detection Haar Classifier in Machine Learning Introduction to Machine Learning using C++ How to Avoid Over Fitting in Machine Learning What is Haar Cascade Handling Imbalanced Data with Smote and Near Miss Algorithm in Python Optics Clustering Explanation

Difference between Machine learning and Human Learning

Difference between Human Learning and Machine Learning

The process through which humans gain information, skills, behaviors, attitudes, and understanding via their own experiences, conversations, and observations is referred to as human learning. In addition to being necessary for resolving issues problem-adjusting and sound decision-making, it is crucial for human growth.

Difference between Human Learning and Machine Learning

Because it incorporates numerous mental, emotional, and social factors, human learning is complicated. It can happen in many different ways, such as through formal education, chance meetings, and self-directed learning.

1.Acquiring information that can be recalled and used later on entails acquiring facts, numbers, concepts, and rules. Learning skills can be achieved by reading, listening, watching, and engaging with others, and various other things.

2.Skill Development: This involves to the improvement and refining of talents, which may be either social (such as communication), or cognitive (which may include problem-solving), or physical (such as riding).

3. Cognitive Processes: Cognitive processes that are involved in consciousness, perception, recall, logic, and solving issues all have an impact on learning.

These procedures enable private data encoding, processing, storage, and retrieval.

4. Behaviorist ideas emphasize the relevance of environmental stimuli and consequences in moulding behavior, such as classical conditioning and operant conditioning. They contend that learning happens by the connection of inputs and responses, or via reinforcement and punishment.

5. Social learning theories, such as Albert Bandura's social cognitive theory, emphasize the significance of observation, modeling, and imitation in learning. By witnessing others and the repercussions of their actions, individuals can learn new behaviors and abilities.

6. Experiential Learning: Experiential learning theories emphasize the significance of direct experience in learning, such as David Kolb's experiential learning cycle. They contend that learning takes place in a continuous cycle of concrete experience, reflective observation, abstract conception, and active exploration.

Definition of Machine learning (ML) is an artificial intelligence (AI) subject that focuses on building algorithms and models that enable systems and machines to learn and make predictions or judgments based on data without being explicitly programmed. Machine learning trains computers to analyze and interpret data automatically, detect patterns, generate predictions or, take actions.

Several critical components are involved in the machine-learning process:

i) Data: For training and learning, machine learning algorithms require data as input. The information can originate from a variety of sources, including structured databases, unstructured text, photos, audio, and sensor data.

ii) Training: The machine learning model is supplied with a large dataset including input data (features) and matching output labels (target variable) during the training phase. By modifying its internal parameters or weights, the model learns patterns and correlations in the data.

iii) Feature Extraction: The process of identifying and manipulating important data properties (features) that best reflect the situation at hand is known as feature extraction. This stage aids in the reduction of noise and the focus on the most useful components of the data.

iv) Model Selection and Training: Machine learning techniques include decision trees, Bayesian networks, neural networks, and support vector machines. The kind of problem and the characteristics of the data influence the approach selection. The labeled dataset is used to train the algorithm to construct a predictive or decision-making model.

v) After training, the model's performance is assessed on a second dataset known as a validation or test set. Depending on the job, this assessment analyses the model's accuracy, precision, recall, or other relevant metrics.

vi) Prediction/Inference: Once the model has been trained and assessed, it may be used to predict or infer fresh, previously unknown data. The model uses the input data to generate predictions or judgments by applying the learned patterns.

vii) Iteration and Improvement: Machine learning is a process that is iterative the model's performance and accuracy are subpar, adjustments can be made by collecting additional data, enhancing feature selection, or changing the algorithm.

Many different industries, including speech and image recognition, natural language processing, recommendation systems, fraud detection, autonomous vehicles, medical diagnostics, and many more, have incorporated machine learning techniques. By letting computers to learn from massive volumes of data and make precise predictions or judgments without explicit programming for every circumstance, it has revolutionized industries like data analysis and predictive modeling.

Difference between Machine Learning and Human Learning

The difference are:

-Artificial intelligence (AI) is a field that focuses on creating algorithms and models that allow computers to learn from data and make predictions or judgments. This subject is known as machine learning. - Human learning, on the other hand, refers to the cognitive process through which people pick up information, abilities, and understanding via personal experience, formal education, and a variety of mental processes.
- Learning method: A data-driven method is often used in machine learning, where algorithms are trained on big datasets to find patterns and predict the future. - Human learning, in contrast, is a sophisticated cognitive process that integrates existing knowledge with new information while requiring observation, reasoning, and memory.
-Machine learning algorithms are excellent at generalizing from particular training data to generate predictions on new samples. Within the domain they were trained on, they can recognize patterns and make precise predictions. - Humans can apply complex concepts, generalize from a small number of instances, and are usually more adaptable than other species.
- Machine learning algorithms are remarkably good at tasks like picture recognition, language translation, and game playing, but they frequently fall short in terms of creativity and abstract reasoning. - On Contrary, humans are capable of innovative thought, the generation of fresh concepts, and symbolic and abstract thinking.
-Learning enables a person to consciously understand and interpret information. Humans are frequently able to justify their decisions, offer new perspectives, and comprehend how various factors interact in a cause-and-effect manner. -Machine learning models are sometimes referred to as "black boxes" since they may make accurate predictions but may not have straightforward underlying logic.
- To identify patterns and generate precise predictions, machine learning algorithms largely rely on a huge amount of labeled training data. - Humans, on the other hand, may learn from many different sources, including first-hand knowledge, observation, conversation, and teaching.
-The quality and representativeness of the training data, algorithm design, and the available computer resources all have an impact on how effective machine learning algorithms are. They could have trouble doing jobs that call for logical thinking, comprehending context, or working with unclear or sparse material. -Humans are capable of amazing things, yet they also have cognitive biases, and memory problems, are prone to mistakes, and have illogical thoughts.

Advantages and Disadvantages of Machine Learning


1. Automation: Complex jobs and procedures can be automated thanks to machine learning. It is more precise and capable than humans in handling vast volumes of data and repetitive jobs.

2. Scalability: Machine learning models have the capacity to scale to accommodate huge datasets and high data inflow rates. Large volumes of information may be processed and analyzed in real-time or very close to real-time because of its scalability.

3. Personalization: By examining user preferences and behavior, machine learning makes personalized experiences possible. Enhancing user happiness, it drives recommendation systems, customized marketing campaigns, and user interfaces.


1. Data Dependency: For training, machine learning models are very dependent on high-quality, pertinent, and representative data. Predictions may be erroneous or biased if the training data is skewed, lacking, or of poor quality.

2. Overfitting: When machine learning models specialize excessively and are unable to generalize successfully to fresh, untried data, they have overfitted the training data. Poor performance on real-world instances may result from this.

3. Ethics and Privacy Issues: Machine learning poses ethical issues with regard to justice, prejudice, and privacy. The use of personal data for training models may cause privacy problems if handled improperly, and biased training data or biased algorithms might reinforce prejudice or injustices.

Advantages and Disadvantages of Human Learning


1.Flexibility & Adaptability: People are remarkably capable of picking up new skills and adjusting to unfamiliar circumstances, surroundings, and difficulties.

2. Have the capacity to comprehend and analyze data within a wider perspective. When learning and implementing knowledge, they might take into account elements like cultural, social, and emotional dimensions.

3. Moral judgment is a component of human learning that takes ethical issues into account. Humans are capable of weighing the advantages and disadvantages of their options, choosing according to their values, and accepting responsibility for their actions.


1.Limited Capacity and Speed: Working memory and processing speed are only two examples of cognitive constraints that limit human learning. Humans are less efficient than robots in learning because they have shorter attention spans and can only comprehend a limited quantity of information at once.

2. Subjectivity and Bias: Individual viewpoints, prejudices, and subjective experiences all affect how well people learn. This may result in contradictions, mistakes of judgment, and even potential bias or injustice in the decision-making process.

3. Emotional factors: Motivators, distractions, and emotional states all have an impact on how well humans learn. The efficacy and consistency of learning results might be affected by emotional biases or changes in motivation.

4. Learning Has a Limited Lifespan: Human learning has a limited life. Humans can learn throughout their lives, but as they become older, their ability to learn quickly and effectively tends to wane. Additionally, when the world develops or changes, information and abilities may become obsolete.


Despite the fact that both human learning and machine learning contain the acquisition of knowledge, they vary in terms of their biological and artificial origin, goal, data requirements, rapidity, capacity for generalization, and flexibility. Each approach has pros and cons, and combinations of them are routinely used to handle different problems.