# Statistics and Machine Learning Online Training

The statistic for Machine Learning is used for searching in the Web. It is very much helpful for placing Ads, Credit scoring. It can be used for stock trading. It is a very vast concept and can be used for many other applications. Learning statistic for Machine learning is helpful in learning algorithms. Machine learning gives detail idea about IN and OUT, Implementing different algorithms in the application of machine learning. You’ll learn the theoretical and practical concepts of Machine learning. It uses computer algorithms to search for any data. Enroll today and attend Statistics and Machine Learning Online Training free demo by real time expert.
Course Objectives
What are the Course Objectives?

After this Statistics and Machine Learning Online Training Course you will able to understand

Complete knowledge of Statistic for Machine learning.
Principal on Statistic.
Get knowledge of Algorithms.
Learn Mathematical and Heuristic concept of Machine learning.
Learn reinforcement and dimensionality reduction.
Who should go for this Course?

Any IT experienced Professional who want to be Machine Learning developer can join Statistics and Machine Learning Online Training.
Any B.E/ B.Tech/ BSC/ M.C.A/ M.Sc Computers/ M.Tech/ BCA/ BCom College Students in any stream.
Pre-requisites:

Statistic
Development Concept
Course Curriculum
Statistics
What is Statistics
Descriptive Statistics
Central Tendency Measures
The Story of Average
Dispersion Measures
Data Distributions
Central Limit Theorem
What is Sampling
Why Sampling
Sampling Methods
Inferential Statistics
What is Hypothesis testing
Confidence Level
Degrees of freedom
what is pValue
Chi-Square test
What is ANOVA
Correlation vs Regression
Uses of Correlation & Regression
Machine Learning
Introduction

ML Fundamentals
ML Common Use Cases
Understanding Supervised and Unsupervised Learning Techniques
Clustering

Similarity Metrics
Distance Measure Types: Euclidean, Cosine Measures
Creating predictive models
Understanding K-Means Clustering
Understanding TF-IDF, Cosine Similarity and their application to Vector Space Model
Case study
Implementing Association rule mining

Similarity Metrics
What is Association Rules & its use cases?
What is Recommendation Engine & it’s working?
Recommendation Use-case
Case study
Understanding Process flow of Supervised Learning Techniques
Decision Tree Classifier
How to build Decision trees
What is Classification and its use cases?
What is Decision Tree?
Algorithm for Decision Tree Induction
Creating a Decision Tree
Confusion Matrix
Case study
Random Forest Classifier
What is Random Forests
Features of Random Forest
Out of Box Error Estimate and Variable Importance
Case study
Naive Bayes Classifier
Case study
Project Discussion
Problem Statement and Analysis
Various approaches to solve a Data Science Problem
Pros and Cons of different approaches and algorithms.
Linear Regression
Case study
Introduction to Predictive Modeling
Linear Regression Overview
Simple Linear Regression
Multiple Linear Regression
Logistic Regression
Case study
Logistic Regression Overview
Data Partitioning
Univariate Analysis
Bivariate Analysis
Multicollinearity Analysis
Model Building
Model Validation
Model Performance Assessment
Scorecard
Text Mining
Case study
Sentimental Analysis
Case study
Support Vector Machines
Case Study
Introduction to SVMs
SVM History
Vectors Overview
Decision Surfaces
Linear SVMs
The Kernel Trick
Non-Linear SVMs
The Kernel SVM
Deep Learning
Case Study
Deep Learning Overview
The Brain vs Neuron
Introduction to Deep Learning
Introduction to Artificial Neural Networks
The Detailed ANN
The Activation Functions
How do ANNs work & learn
Backpropogation
Convolutional Neural Networks
Convolutional Operation
Relu Layers
What is Pooling vs Flattening
Full Connection
Softmax vs Cross Entropy
What are RNNs – Introduction to RNNs
Recurrent neural networks rnn
LSTMs for beginners – understanding LSTMs
long short term memory neural networks lstm in python
Time Series Analysis
Describe Time Series data
List the different components of Time Series data
Discuss different kind of Time Series scenarios
Choose the model according to the Time series scenario
Implement the model for forecasting
Explain working and implementation of ARIMA model
Illustrate the working and implementation of different ETS models
Forecast the data using the respective model
What is Time Series data?
Time Series variables
Different components of Time Series data
Visualize the data to identify Time Series Components
Implement ARIMA model for forecasting
Exponential smoothing models
Identifying different time series scenario based on which different Exponential Smoothing model can be applied
Implement respective model for forecasting
Visualizing and formatting Time Series data
Plotting decomposed Time Series data plot
Applying ARIMA and ETS model for Time Series forecasting
Forecasting for given Time period
Case Study