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Machine Learning Training in Chennai

Machine learning (ML) is the scientific study of algorithms and statistical models that computer systems use in order to perform a specific task effectively without using explicit instructions, relying on patterns and inference instead.
Machine learning is also a core part of AI. Mathematical analysis of machine learning algorithms and their performance is often referred to as computational learning theory.

Types of learning algorithms in ML

Supervised learning

Supervised learning algorithms build a mathematical model of a set of data that contains both the inputs and the desired outputs. supervised learning algorithms include classification and regression.

Unsupervised learning

Unsupervised learning algorithms take a set of data that contains only inputs, and find structure in the data, like grouping or clustering of data points. The algorithms therefore learn from test data that has not been labeled, classified or categorized.

Reinforcement learning

Reinforcement learning is an area of machine learning concerned with how software agents ought to take actions in an environment so as to maximize some notion of cumulative reward.It includes disciplines like game theory, control theory, operations research, information theory, simulation-based optimization, multi-agent systems, swarm intelligence, statistics and genetic algorithms.

Feature learning

Feature learning algorithms, also called representation learning algorithms, often attempt to preserve the information in their input but also transform it in a way that makes it useful, often as a pre-processing step before performing classification or predictions.

Sparse dictionary learning

Sparse dictionary learning is a feature learning method where a training example is represented as a linear combination of basis functions, and is assumed to be a sparse matrix.

Anomaly detection

It is also known as outlier detection, which is the identification of rare items, events or observations which raise suspicions by differing significantly from the majority of the data.


• Artificial neural networks

• Support vector machines

• Bayesian networks

• Genetic algorithms

• Decision trees

Open-source software


• Deeplearning4j


• MXNet

• Neural Lab

Proprietary software

• Amazon Machine Learning

• Angoss KnowledgeSTUDIO

• Azure Machine Learning

• KXEN Modeler

• LIONsolver

Statistics for Machine Learning

Statistics and Machine Learning

Introduction to Statistics

Gaussian Distribution and Descriptive Stats

Correlation Between Variables

Statistical Hypothesis Tests

Estimation Statistics

Nonparametric Statistics

Python Basics

How to Install Python on Windows with Pycharm IDE

Hello World: Creat your First Python Program

Python Main Function with Examples: Understand __main__

Python Variables: Declare, Concatenate, Global & Local

Python Data Structure

Python Strings: Replace, Join, Split, Reverse, Uppercase & Lowercase

Python TUPLE - Pack, Unpack, Compare, Slicing, Delete, Key

Python Dictionary(Dict): Update, Cmp, Len, Sort, Copy, Items, str Example

Python Operators: Arithmetic, Logical, Comparison, Assignment

Python Functions Examples: Call, Indentation, Arguments & Return Values

Python Fundamentals

Python IF, ELSE, ELIF, Nested IF & Switch Case Statement

Python For & While Loops: Enumerate, Break, Continue Statement

Python OOPs: Class, Object, Inheritance and Constructor with Example

Must Know Stuff!

Python Regex Tutorial: re.match(),re.search(), re.findall(), re.split()

Python DateTime, TimeDelta, Strftime(Format) with Examples

Python CALENDAR Tutorial with Example

Python List: Comprehension, Apend, Sort, Length, Reverse EXAMPLES

Python File Handling: Create, Open, Append, Read, Write

Python Check If File or Directory Exists

Python COPY File using shutil.copy(), shutil.copystat()

Python Rename File and Directory using os.rename()

Python SciPy Tutorial: Learn with Example

Reading and Writing CSV Files in Python using CSV Module & Pandas

Core Libraries & Statistics







Machine Learning with Python

Methods for Machine Learning

Data Loading for ML Projects

Understanding Data with Statistics

Understanding Data with Visualization

Preparing Data

Exploratory Data Analysis

Box plot


Pie graph

Line chart


Statistics and Machine Learning – Regression and Classification
Statistics Topics


Hypothesis Testing – Null Hypothesis and Alternate Hypothesis

Standard deviation and Variance

Outliers – Detection and Replacement method


T test- Unpaired and Paired

Chi square


Machine Learning Topics

Linear Regression

Multiple Regression

Logisitic Regression

Naïve Bayes Classifier

Variable selection on model

K means Clustering

Decision Tree


Time series forecasting

ML Algorithms − Classification

Logistic Regression

Support Vector Machine(SVM)

Decision Tree

Naïve Bayes

Random Forest

ML Algorithms − Regression


Linear Regression

ML Algorithms − Clustering


K-Means Algorithm

Mean Shift Algorithm

Hierarchical Clustering

ML Algorithms − KNN Algorithm

Finding Nearest Neighbors

Performance Metrics

Automatic Workflows

Improving Performance of ML Models

Improving Performance of ML Model(contd..)

  • What is the difference between supervised and unsupervised machine learning?

    Supervised learning requires training labeled data. Unsupervised learning, in contrast, does not require labeling data explicitly.

  • What’s the difference between a generative and discriminative model?

    A generative model will learn categories of data while a discriminative model will simply learn the distinction between different categories of data. Discriminative models will generally outperform generative models on classification tasks.

  • What are the five popular algorithms of Machine Learning?

    • Decision Trees

    • Probabilistic networks

    • Neural Networks (back propagation)

    • Support vector machines

    • Nearest Neighbor

  • Define What Is Fourier Transform In A Single Sentence?

    A process of decomposing generic functions into a superposition of symmetric functions is considered to be a Fourier Transform.

  • What Is Deep Learning?

    Deep learning is a process where it is considered to be a subset of machine learning process.

  • What Is The Difference Between Bias And Variance?

    Bias can be defined as a situation where an error has occurred due to use of assumptions in the learning algorithm;Variance is an error caused because of the complexity of the algorithm that is been used to analyze the data.