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.
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, simulationbased optimization, multiagent 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 preprocessing 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.
Models
• Artificial neural networks
• Support vector machines
• Bayesian networks
• Genetic algorithms
• Decision trees
Opensource software
• CNTK
• Deeplearning4j
• ELKI
• MXNet
• Neural Lab
Proprietary software
• Amazon Machine Learning
• Angoss KnowledgeSTUDIO
• Azure Machine Learning
• KXEN Modeler
• LIONsolver
Machine Learning Course Content
Statistics and Machine Learning
Introduction to Statistics
Gaussian Distribution and Descriptive Stats
Correlation Between Variables
Statistical Hypothesis Tests
Estimation Statistics
Nonparametric Statistics
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 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 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
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
NumPy
SciPy
Pandas
Matplotlib
Seaborn
Methods for Machine Learning
Data Loading for ML Projects
Understanding Data with Statistics
Understanding Data with Visualization
Preparing Data
Box plot
Histogram
Pie graph
Line chart
Scatterplot
Probability
Hypothesis Testing – Null Hypothesis and Alternate Hypothesis
Standard deviation and Variance
Outliers – Detection and Replacement method
Correlation
T test Unpaired and Paired
Chi square
Anova
Linear Regression
Multiple Regression
Logisitic Regression
Naïve Bayes Classifier
Variable selection on model
K means Clustering
Decision Tree
SVM
Time series forecasting
Logistic Regression
Support Vector Machine(SVM)
Decision Tree
Naïve Bayes
Random Forest
Overview
Linear Regression
Overview
KMeans Algorithm
Mean Shift Algorithm
Hierarchical Clustering
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.
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