What are the important topics in Machine Learning?

The very first question raised in mind is, What is Machine Learning? A computer scientist, Arthur Samuel, in 1959, who initiated the study of artificial intelligence characterized machine learning as, ‘the study that gives computers the ability to learn without being explicitly programmed’. Machine Learning is a facet of artificial intelligence where a computer/machine learns from past experiences and makes future foretellings and a device has to be intelligent and responsive in a way that cannot be characterized from that of a human being. Machine learning is one protocol to accomplish AI by using algorithms, rather than the ancestral hand-coded rules-based decision trees. 

Life Cycle of the Machine Learning Process-

Define Project Objective 🡪Acquire and Explore Data🡪 Model Building🡪 Model Validation🡪 Interpret and Communicate🡪 Data Visualization, Implement, Document and Maintain

Machine Learning is an application of Artificial Intelligence that is dynamic and not based on hard-coded rule-based instructions.

Important Topics in Machine Learning

Machine learning is mainly categorized into three types- Supervised Learning, Un-supervised Learning and Reinforcement Learning. Aside from these categories, there are the number of topics in machine learning like Cross-Validation, Linear regression, Logistic regression and so on. For mastering the topics of Machine Learning you should go for ‘Artificial Intelligence course in Noida, ML course in Delhi NCR’, are the best institute in Noida and Delhi NCR, which can help you to fulfil your exotic journey of ML and AI Engineering. Now, we will discuss ML topics one by one-

  • Supervised Learning- In supervised learning, the task-driven technique is used. The algorithm comprehends on the labeled dataset, where the Cross-Validations for future or unseen data. Examples of supervised learning are handwriting detection, forecasting the sales price of products, spam filtering, etc. 

  • Semi-Supervised Learning-  Semi-supervised learning uses both the labeled and unlabeled data for training. The system attempts to uncover patterns from data, where the labeled data is typically a small amount, and the unlabeled data is large in number. This learning is useful for speech analysis, web content classification, photo tagging and text documentation.

  • Reinforcement Learning- Reinforcement learning is based on the feedback loop and goal-oriented algorithms. This system is behaviour-driven which authorizes machine and software agents to automatically specify the ideal behaviour within a specific context to maximize the performance.

  • Neural Networks and Gradient Descent- Neutral Networks or Artificial Neural Networks(ANN), is inspired by the functioning of human brain cells, called neurons. This part of machine learning can think and learn the same way humans do. ANN authorizes the machine to enlighten itself to accomplish a task by exposing the multi-layered neural network to vast amounts of data.

  • Linear Regression- In linear regression, the goal is to predict the real-value variable from a given pattern. In this regression, the output is a linear function of the input.

  • Logistic Regression- In some cases, the response variable is not equally distributed. In logistic regression, the response variable interprets the probability that the conclusion is the positive case. If the response variable is proportional to or exceeds a discrimination threshold, the positive class is foreseen; contrarily, the negative class is predicted. The response variable is modeled as a function of a linear combination of the input variables operating the logistic function.

  • Cross-Validation- The method to select the optimal values of hyperparameters is called model selection. If we reuse the precise test data set over and over again during model selection, It will become a component of our training data and consequently, the model will be better inclined to overfit. Cross-validation authorizes us to tune hyper-parameters with only our training set. Cross-validation permits us to keep the test set as a truly unseen data-set for selecting the final sample.

  • Machine Learners should study data processing, classification algorithms like Decision Trees, Naive Bayes Classifier, Gaussian Bayes Classifier.

  • The learner also has to study classification evaluation techniques like k-fold cross-validation, boosting, bagging and Computational Learning Theory, Probabilistic Graphical Model.

Note: Best Machine Learning Training Institute In Noida

Applications of Machine Learning 

  • Prediction- Used to predict and forecast various aspects.

  • Medical Diagnosis- Used to predict terminal and non-terminal diseases.

  • Financial Industry and Trading- Used to detect fraudulent transactions, customers, make credit checks, credit defaults, optimize trading strategies using algorithms.

  • Image Recognition- Used for image segmentation techniques, machines can identify objects, persons, places, digital images.

  • Speech Recognition – Used to convert the voice instructions and searches to text.

  • Automatic Language Translation and Auto-Corrections- Used to translate text from one language to another, auto-correct the spelling errors.

Some Important Points in Machine Learning 

  • Machine Learning signifies learning from Data.

  • Machine Learning is finding patterns from Data.

  • Data+ Algorithms= Machine Learning, but data is more important.

  • Feature Extraction is key.

  • Overfitting is when your algorithm instead of learning, starts memorizing.

  • To avoid overfitting, one should always use regularization.

  • Machines aren’t capable of taking decisions but people take them.

  • The most important point of Machine Learning is ‘Data Cleaning’.


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