Machine Learning

BITS is an IT institute that provide IT Training. Machine learning (ML) is a type of artificial intelligence (AI) that allows software applications to become more accurate at predicting outcomes without being explicitly programmed to do so. Machine learning algorithms use historical data as input to predict new output values. It is important because it provides companies with insight into trends in customer behavior and business operational patterns, as well as supports new product development. 

Many of today’s leading companies, such as Facebook, Google, and User, are making machine learning a core part of their operations. It has become a significant competitive element for many companies. It is use two types of techniques: supervised learning, which trains a model on known input and output data to predict future outputs, and unsupervised learning, which finds hidden patterns or internal structures in the input data.

  • Supervised learning: In this type of machine learning, data scientists provide algorithms with labeled training data and define the variables that the algorithm should evaluate for correlations. The inputs and outputs of the algorithm are specified.
  • Unsupervised learning: This type of machine learning involves algorithms that train on unlabeled data. The algorithm searches the datasets for any meaningful connections. The data the algorithms train on, as well as the predictions or recommendations they make, are predetermined.

Machine Learning Course Details

What is Machine Learning?

Machine Learning (ML) is a subset of artificial intelligence (AI) that enables software applications to grow increasingly effective at predicting outcomes without explicitly programming them to do so. In order to predict new output values, machine learning algorithms use previous data as input.

Recommendation engines are common use for Machine Learning. Popular applications include fraud detection, spam filtering, malware threat identification, business process automation (BPA), and predictive maintenance.

Why is Machine Learning crucial?

Machine learning is significant because it provides organizations with insights into trends in customer behavior and business operating patterns, as well as assisting in the development of new products. Machine learning is fundamental to the operations of many of today’s leading organizations, like Facebook, Google, and Uber. Machine learning has become a crucial competitive difference for many businesses.

What are the different types of Machine Learning?

Traditional machine learning is typically classified based on how an algorithm learns to enhance its prediction accuracy. There are four fundamental techniques to learning: supervised learning, unsupervised learning, semi-supervised learning, and reinforcement learning. The algorithm that data scientists use is determined by the sort of data they wish to predict.

In-depth Guide to Machine Learning in the enterprise

Supervised learning:

In this sort of machine learning, data scientists provide labeled training data to algorithms and specify the variables they want the system to look for connections in. The algorithm’s input and output are both provided.

Unsupervised learning:

Algorithms that train on unlabeled data are used in this sort of machine learning. The algorithm examines data sets for any useful connections. Conventional machine learning algorithms are often classed depending on how they learn to improve their prediction accuracy.

This machine learning approach combines the two preceding categories. Although data scientists may provide mostly labeled training data to an algorithm, the model is allowed to examine the data on its own and establish its own understanding of the data set.

Reinforcement learning:

 Data scientists often use reinforcement learning to teach a machine to execute a multi-step process with precisely stated rules. Data scientists build an algorithm to complete a task and provide it with positive or negative cues as it determines how to finish the task. Yet, for the most part, the algorithm selects what steps to take along the road.

What is the process of supervised machine learning?

Supervised machine learning requires the data scientist to train the system with both labeled inputs and desired outputs.

The following tasks benefit from supervised learning algorithms:

Binary classification: Data is divided into two groups.

Multi-class classification: Selecting between more than two sorts of solutions 

Modeling regression: Continuous value projection.

Ensembling: Mixing the predictions of numerous machine learning models to get an accurate forecast is known as assembling.

How does unsupervised machine learning operate?

 They comb through unlabeled data for patterns that can be used to organize data points into subsets. The vast majority of deep learning methods, including neural networks, are unsupervised. Unsupervised learning techniques are effective for the following tasks:

  • Clustering is the process of dividing a dataset into groups based on similarities.
  • Anomaly detection is the process of locating unexpected data points in a data set.
  • Association mining is the process of identifying groups of things in a data set that commonly appear together.
  • Dimensionality reduction refers to the process of reducing the number of variables in a data source.
  • Introduction to ML
  • Linear Regression
  • Linear Discriminant Analysis
  • Reinforcement Learning
  • Multivariate Regression
  • Linear Classification
  • Unsupervised Learning
  • Partial Least Squares
  • Logistic Regression
  • Supervised Learning
  • Shrinkage Methods
  • Project
  • Support Vector Machines
  • Artificial Neural Networks
  • Regression Trees
  • Hinge Loss Formulation
  • Training and Validation
  • Decision Trees
  • Perceptron Learning
  • Parameter Estimations
  • Decision Trees Examples
  • ROC Curve
  • Random Forests
  • Hidden Markov Models
  • Evaluation Measures
  • Bayesian Networks
  • Tree width and belief
  • Ensemble Methods
  • Gradient Boosting
  • Undirected Graphical Method
  • Minimum Desc. Lgth Analysis
  • Naive Bayes
  • Variable Elimination
  • Clustering
  • Expectation Maximization
  • Reinforcement Learning
  • Birch and Cure Algorithms
  • Gaussian Mixture Models
  • Linear Theory

Machine Learning

Fee: 15,000
Duration: 1 Month
Timing: 9AM-11AM, 11AM-1PM, 1PM-3PM, 3PM-5PM, 5PM-7PM, 7PM-9PM

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