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MSR Trainings provides Machine Learning Online Training through worldwide. MSR Trainings is an excellent Machine Learning Training center in Hyderabad. After course, we will give support for certification, Resume preparation, and how to prepare for interviews
ML Fundamentals
ML Common Use Cases
Understanding Supervised and Unsupervised Learning Techniques
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
What is Association Rules & its use cases?
What is Recommendation Engine & it’s working?
Recommendation Use-case
Case study
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
What is Random Forests
Features of Random Forest
Out of Box Error Estimate and Variable Importance
Case study
Case study
Various approaches to solve a Data Science Problem
Pros and Cons of different approaches and algorithms.
Case study
Introduction to Predictive Modeling
Linear Regression Overview
Simple Linear Regression
Multiple Linear Regression
Case study
Logistic Regression Overview
Data Partitioning
Univariate Analysis
Bivariate Analysis
Multicollinearity Analysis
Model Building
Model Validation
Model Performance Assessment AUC & ROC curves
Scorecard
Case Study
Introduction to SVMs
SVM History
Vectors Overview
Decision Surfaces
Linear SVMs
The Kernel Trick
Non-Linear SVMs
The Kernel SVM
Describe Time Series data
Format your 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
Various machine learning algorithms in Python
Apply machine learning algorithms in Python
How to select the right data
Which are the best features to use
Additional feature selection techniques
A feature selection case study
Preprocessing
Preprocessing Scaling Techniques
How to preprocess your data
How to scale your data
Feature Scaling Final Project
Highly efficient machine learning algorithms
Bagging Decision Trees
The power of ensembles
Random Forest Ensemble technique
Boosting – Adaboost
Boosting ensemble stochastic gradient boosting
A final ensemble technique
Introduction Model Tuning
Parameter Tuning GridSearchCV
A second method to tune your algorithm
How to automate machine learning
Which ML algo should you choose
How to compare machine learning algorithms in practice
Sentimental Analysis
Case study
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