What is Machine Learning

Machine learning is an application or progams of artificial intelligence that provides Machines the ability to automatically learn and improve from experience without being explicitly programmed for the improvement. Developing Machine learning algorithms, the focus is on the development of computer programs that can access data and use it learn for itself. The process of learning begins with observations or data, such as direct experience, instruction or examples in order to look for patterns in data and make better decisions in the future based on the examples that is provided. The primary goal is to allow the computers learn automatically without human intervention or assistance and adjust actions accordingly.

In Machine Learning Training, what does P-value signify about the statistical data?

In Machine Learning Training, P-value is used to determine the significance of results after a hypothesis test in statistics. P-value helps the readers to draw conclusions and is always between 0 and 1. P- Value > 0.05 denotes weak evidence against the null hypothesis which means the null hypothesis cannot be rejected . P-value <= 0.05 denotes strong evidence against the null hypothesis which means the null hypothesis can be rejected . P-value=0.05 is the marginal value indicating it is possible to go either way .

In Machine Learning Training, what is K-means Clustering?

K-means is a common algorithm to group similar the data set of n observations into k clusters. Each observations belong to belongs to the cluster with the nearest mean. Typically, unsupervised algorithms make inferences from datasets using only input vectors without referring to known, or labelled, outcomes. In the context of Machine Learning Training, the K-means algorithm identifies k number of centroids, and then allocates every data point to the nearest cluster. This is done keeping the centroids as small as possible. The ‘means’ in the K-means refers to averaging of the data; that is, finding the centroid.

In Machine Learning, how is KNN different from k-means clustering?

K-Nearest Neighbors is a supervised classification algorithm, while k-means clustering is an unsupervised clustering algorithm. While the mechanisms may seem similar at first, what this really means is that in order for K-Nearest Neighbors to work, you need labeled data you want to classify an unlabeled point into (thus the nearest neighbor part). K-means clustering requires only a set of unlabeled points and a threshold: the algorithm will take unlabeled points and gradually learn how to cluster them into groups by computing the mean of the distance between different points. The critical difference here is that KNN needs labeled points and is thus supervised learning, while k-means doesn’t — and is thus unsupervised learning.

Machine Learning Training in Bangalore

Name: Machine Learning Training in Bangalore. Course, Certification
Author: devU.in

Teacher

devU.in

Why take a Training in Machine Learning in Bangalore?

Machine Learning is providing machines the capability to learn from Data and Experience. This is the field of study, wherein, Computer programs are developed, which when provided the data and the Outcome extract the rules and learn for themselves. Learning Machine Learning helps you understand this area of Complex Algorithms builds on Mathematics & Statistics. You could either avail Machine Learning Certification Training from devU.in or avail an Online Training in Machine Learning Certification from devU.in. You can also learn Machine Learning using some Machine Learning Tutorials.

How does Machine Learning work?

Machine learning Algorithms directly learn from data. They do not reply upon pre-determined rules to make their decisions. The Machine Learning Algorithms adaptively improve their outcomes with an increase number of samples available for learning increases. Deep learning is a specialised form of machine learning.

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What are the types of Machine Learning?

Machine Learning Algorithms can be broadly classified in the following 3 categories:

Supervised Machine Learning
- Regression
  - Decision Trees
  - Linear Regression
  - Logistics Regression
- Classification
  - Naïve Bayes
  - Support Vector Machines
  - K-Nearest Neighbour
Unsupervised Machine Learning
- Clustering
  - K-Means Clusters
  - Mean -Shift
  - K-Mediods
- Dimensionality Reduction
  - Principal Component Analysis (PCA)
  - Feature Selection
  - Linear Discriminant Analysis (LDA)
Reinforcement Learning

Is getting training on machine learning difficult?

Learning Machine Learning on your own could be intimidating for some people. There are a lot of resources available for you to learn on Machine Learning. You could Learn Machine Learning Online, You could join a Course in Machine Learning Certification and Training in Bangalore with devU.in. You could also learn Machine Learning using Machine Learning tutorials. It depends upon one’s appetite and ability to learn Machine Learning.

However, we will like to add, if you decide to write the Algorithms in Machine Learning in Python, you should have a thorough understanding of Python and underlying libraries.

Why take a Training in Machine Learning in Bangalore?

How to get Trained on Machine Learning

Machine Learning is a Complex Field, which is constantly evolving. The knowledge required goes beyond just knowing the algorithms, across to learning the application of the algorithms. In fact, Data Scientists spend a significant amount of time in Data Pre-processing, Feature Selection, which is akin to art. If you are confused about how to learn machine learning, you are not alone. We come across a number of students, who are confused between the options on Learning Machine Learning Online in the Classroom. More so, on students, who contemplate Learning Machine Learning via Machine Learning tutorials. In either case, you should consider taking a Machine Learning Certification. The most preferred route to becoming a Machine Learning Expert is to avail the Machine Learning Training and Certification Course in Bangalore with devU.in. Devu.in provides Certification in Machine Learning Course via ClassRoom Coaching in HSR Layout, Bangalore and provides an opportunity to learning Machine Learning Online. The Live Online course in Machine Learning can also be availed in their ClassRooms in their Centers in Rammurthy Nagar and Noida. There are, of course, other options for one to become a Machine Learning expert via Self-Study, MOOC and Youtube videos, You could see the comparison and benefits of each of these modes of access in a section of devu.in. Click here to download the upcoming batch details.

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Partial List of Topics to be covered in Machine Learning Certification Training Course in Bangalore

Data Science Overview

Introduction to Data Science

Different Sectors Using Data Science

Purpose and Components of Python

Statistical Analysis

Introduction to Statistics

Statistical and Non-statistical Analysis

Major Categories of Statistics

Statistical Analysis Considerations

Population and Sample

Statistical Analysis Process

Data Distribution

Hypothesis testing

Numpy

Pandas

Machine Learning

Machine Learning Approach

Supervised Learning Model

Regression Linear regression

Multiple Linear Regression

Classification

Logistic regression

SVM

Naive Bayes

Decision Tree

Random Forest

K Nearest Neighbors

Unsupervised Learning Models

K-means Clustering

Apriori association rules

Sklearn for machine learning model

Data Visualisation with MatPlotLib

Introduction to Data Visualisation

Line Properties

Scatter plot

Bar graphs and histograms

Pie charts

Subplots

FAQ

What is Machine Learning
Machine learning is an application or progams of artificial intelligence that provides Machines the ability to automatically learn and improve from experience without being explicitly programmed for the improvement.
Developing Machine learning algorithms, the focus is on the development of computer programs that can access data and use it learn for itself.
The process of learning begins with observations or data, such as direct experience, instruction or examples in order to look for patterns in data and make better decisions in the future based on the examples that is provided.
The primary goal is to allow the computers learn automatically without human intervention or assistance and adjust actions accordingly.
In Machine Learning Training, what does P-value signify about the statistical data?
In Machine Learning Training, P-value is used to determine the significance of results after a hypothesis test in statistics. P-value helps the readers to draw conclusions and is always between 0 and 1.
- P- Value > 0.05 denotes weak evidence against the null hypothesis which means the null hypothesis cannot be rejected.
- P-value <= 0.05 denotes strong evidence against the null hypothesis which means the null hypothesis can be rejected.
- P-value=0.05 is the marginal value indicating it is possible to go either way.
In Machine Learning Training, what is K-means Clustering?
K-means is a common algorithm to group similar the data set of n observations into k clusters. Each observations belong to belongs to the cluster with the nearest mean.
Typically, unsupervised algorithms make inferences from datasets using only input vectors without referring to known, or labelled, outcomes.
In the context of Machine Learning Training, the K-means algorithm identifies k number of centroids, and then allocates every data point to the nearest cluster. This is done keeping the centroids as small as possible.
The ‘means’ in the K-means refers to averaging of the data; that is, finding the centroid.
In Machine Learning, how is KNN different from k-means clustering?
K-Nearest Neighbors is a supervised classification algorithm, while k-means clustering is an unsupervised clustering algorithm. While the mechanisms may seem similar at first, what this really means is that in order for K-Nearest Neighbors to work, you need labeled data you want to classify an unlabeled point into (thus the nearest neighbor part). K-means clustering requires only
a set of unlabeled points and a threshold: the algorithm will take unlabeled points and gradually learn how to cluster them into groups by computing the mean of the distance between different points.

The critical difference here is that KNN needs labeled points and is thus supervised learning, while k-means doesn’t — and is thus unsupervised learning.

Reviews, Feedback and Testimonials

I attended the Machine Learning Certification Course in Bangalore with Devu.in. The course was very good. Covered the Concepts very well

Ramit P

Data Scientist

The Machine Learning Training Course in Bangalore is one of the best Courses offered by devU.in It is a great value for money. Other Institutes are offer much lesser content for almost double the price.

Ravikanth Srivastava

Sr. Data Scientist

The Machine Learning Certification Course in Bangalore covered end to end of the subject. The Trainer, Ram, has a knack of making a subject interesting for the students to understand

Mahesha H

Sr. Data Scientist

When I was looking for Machine Learning Training Course in Bangalore, devu.in was recommended by some of my Colleagues, who had attended the training there. The instructor was very knowledgeable and he explained the concepts very well.

Akshay Anand

Data Scientist

Machine Learning Training Course Offered by Devu.in helped me excel in my career. From UK, I could attend the courses which were offered early in the morning my time.

Sridhar P

Sr. Data Analyst

The Machine Learning Certification Course offered by devu.in is good. The concepts were covered with real life examples, which made the understanding of difficult topics simple. I will definitely recommend this Course in Machine Learning to my friends and colleagues

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