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Machine Learning

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admin
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Course Description

Machine Learning

Machine Learning (ML) is the science of enabling computers to act without any programming. The world has invented so many innovations since 2010 with Machine Learning in producing self-driving cars, practical speech recognition, effective web-search and finally fully understanding the human genome. It may sound astonishing that the Machine Learning pervaded the world today as every individual is using it number of times in a day even without aware of it. It is found that Machine Learning is the best way to make progress in Artificial Intelligence (AI) at human-level. The course offers most effective machine learning techniques, practices and implementation to make them work.  

The course topics are:   

  • Supervised learning of parametric, non-parametric algorithms, support vector machines, kernels and neural networks. 
  • Unsupervised learning of clustering, dimensionality reduction, recommended systems, deep learning. 
  • Best practices in machine learning are bias or variance theory; innovation process in machine learning and AI. 

Machine Learning at Version IT 

Version IT Machine Learning training institute in Hyderabad offers the course from basic to advanced level in Machine Learning. Version IT is one among the topmost Machine Learning training institutes in Ameerpet of Hyderabad. At the end of the course, the candidates will learn applying algorithms to build smart robots with perception and control, text understands with web-search and anti-spam, computer vision, medical informatics, audio, data-mining and other relevant subjects. You can easily find Machine learning training courses near me in Ameerpet.  

Who can go for Machine Learning Course? 

It is for everyone who wants to study ML using Python. Students who are not convenient with coding besides having skills datasets and knowledge of mathematics of intermediate education can go for Machine Learning course. Otherwise, people who want to begin a career as a Machine Learning Engineer or Data Scientist or Artificial Intelligence (AI) programmer can opt for the course.      

What are the advantages of Machine learning Course? 

If you study the Machine Learning course, you will have plenty of job opportunities across the globe. Even at the basic learning stage, the students will get a basic understanding of the smart systems and tools which very often we get into in daily life. The course at Version IT starts from scratch Python as the basic program and covers the advanced level of training. 

SECTION 1 : Machine Learning Basics and its Life Cycle

  • Machine Learning introduction Difference Between Business Intelligence Team, Data Analyst , and Data Scientist,
  • Purpose of Machine Learning, Deep Learning, NLP,AI
  • What is Machine Learning ?
  • Introduction to Supervised Learning and Unsupervised Learning
  • Introduction to Reinforcement learning .
  • How traffic board , bank fraud transaction systems can use machine learning.
  • Machine Learning Life cycle
  • Introduction to data extractions
  • More details on online, batch, data streaming systems.
  • Introduction to NOSQL database sources
  • Types of NoSql databases
  • Overview of Key value store databases
  • Document store databases
  • Columner Store Databases
  • Graph Store Databases
  • 3 types of data sets used in Machine Learning
  • 3 approaches to create Train , Validation, test data sets as part of data preparation
  • Types of data extacting techniques
  • Data Cleansing and Transormations .
  • How to clean missed values. if problem is Regression Problem.
  • How to clean missed values. if problem is Classification Problem.
  • Data cleansing and Transformations
  • How to transform if input variable is character(string) continues value.
  • How to transform if input variable is character(string) categorical value.
  • Develop function for cleansing
  • Develop function for transformations
  • Need of Scaling data and Scaling Techniques.
  • When to use what type of scaling technique.
  • Develop functions for scaling techniques with python
  • Introduction to Training model
  • Evaluating model
  • Model Selection
  • Deployment of model
  • Rebuilding a model
  • Summary of Machine Learning Life Cycle
  • More on supervised, unsupervised, Reinforcement Learning
  • Preparing Train and Test sets Using Python and numpy
  • Cleaning missed values in continuous variables with python for Regression models
  • How to transform if input variable is character categorical(classifier) for regression model
  • Transforming string continuous variables into numerical scores Using Python and numpy
  • Transforming String categorical values into probabilities with random noise using Python and numpy for regression models
  • Scaling input features and labels for Regression models Using Python and Numpy

Section 2 : Machine Learning Models introduction, Tensorflow Basics , Pytorch Basics 

  • Introduction to predictive models
  • Introduction to clustering models
  • Introduction to Recommender Systems models
  • Difference between predictions and forcasting
  • what are descriminative model
  • what are generative model
  • Introduction to Linear Regression.
  • How to Derive coefficients which will correlate input features and target labels
  • More discussion on Linear Regression.
  • How to Deal non linear regression using Polynomial techniques such as quadratic models and cubic models
  • Implementation of Linear, Qudratic polynomial, Cubic Polynomial Regression Using R language
  • Developing predict function and Accuracy testing function using R Language
  • Statistical approach of Tuning Co-efficients . Problems in Statistical approach . And a Little Introduction to Gradient Descent Algorithm
  • How to prepare Linear input Matrix And How to Convert Linear Matrix into n-degree-polynomial matrix using Python Numpy
  • Implementation of Linear and polynomial regressions . And accuracy testing. And predicting target labels of new data Using Python Numpy
  • Introduction to Tensorflow
  • A sample code explanation with Tensorflow and Keras
  • Step by Step explanation of Tensorflow code part 1
  • Step By Step explanation of Tensorflow code Part 2
  • Step by Step Explanation of Tensorflow code Part 3
  • Low Level Api of Tensorflow Part 1
  • Low Level Api of Tensorflow Part 2
  • How to derive Weight matrix if you have multiple target variables
  • Tensorflow Computations over matrices
  • Extracting Weight matrix using Tensorflow for Linear Regression
  • Introductin to pytorch

Section 3 : Gradient Descent Algorithm

  • What is a model
  • How brainless model will learn from data
  • What is brain of a model?
  • Optimizers and how they work
  • Gradient descent algorithm as an Optimizer
  • Mathematics of Gradient
  • Linear algebra for Gradient
  • How you make sure training of model is completed?

  • What is convergence? and why you should use convergence?
  • Importance of scaling features
  • Types of scaling techniques
  • When to use what type of scaling
  • importance of bias for the feature matrix
  • Preparation of feature and label matrices
  • Gradient descent algorithm for regression problems
  • Gradient descent algorithm for classification problems
  • How you measure loss of a model
  • Types of loss functions
  • When to use what type of loss function.
  • Mean square error as a loss fucntion
  • Cross entropy as a loss function
  • How gradients work to reduce loss of model
  • What is learning rate and how it helps to improve learning speed of model
  • How much learning rate to be defined.
  • Dangers with learning rate
  • what is overlapping and underlapping
  • Python : scaling data
  • How to decide scaling of features required or not
  • Develop python function for regression prediction
  • Develop python function for classification prediction
  • Develop python function for MSE
  • Develop python fucntion for Cross Entropy
  • Develop python function for gradients with derivative for MSE
  • Develop python function for gradients with derivatives for Cross Entropy
  • Adjust weights for Regression models
  • Adjust weights for Classification models
  • A full implementation of Gradient Descent algorithm with python for regression.
  • A full implementation of Gradient Descent algorithm with python for classification.
  • Types of classifications as binary and multinomial
  • How to convert string labels into numeric labels with python
  • How to convert numeric labels in to binary array.
  • why to convert into binary array
  • How to train models for multiple target variables for regression with python
  • Upscaling predictions
  • How to train models for multiple target variables for classifications with python
  • How to transform predicted probabilities into binary array with python
  • How to transform binary array into numeric labels.
  • how to do accuracy testing on multiple target variables(all target variables are continues)
  • How to do accuracy testing on multiple target variables(all target variables are binary classifiers)
  • How to do accuracy testing on multiple target variables(all target variables are combination of binary and multinomial classifiers)
  • How to do accuracy testing on multiple target variables(all target variables are combination of continues and classification variables)
  • What is linearity and non linearity in data
  • How to transform non linear data to linear data
  • How polynomial techniques are used to transform non linear data to linear data
  • Develop function to transform non linear data to linear data with python
  • How to train models for nonlinear data
  • libraries used for train machine learning models
  • What is scikit-learn
  • What is tensorflow
  • What is keras
  • What is pytorch
  • Building end to end model with scikit-learn
  • Building end to end model with tensorflow
  • Building end to end model with keras
  • Building end to end model with pytorch
  • How to save trained model
  • How to load saved model
  • How to predict target labels of new data

SECTION 4 : Types of Gradient Descent Algorithm

  • Gradient descent algorithm-revision
  • Problem with basic gradient descent algorithm
  • Solution as batch gradient algorithm
  • What is a batch?
  • What is an epoch?
  • What is training iteration?
  • What is global minimum?
  • How to select weights of global minimum as knowledge of model
  • Problem with batch gradient algorithm
  • Solution as mini batch gradient
  • Problem with mini-batch gradient algorithm
  • Solution as stochastic gradient
  • What is stochastic process?
  • Types of sample sets
  • Sampling with replacement
  • Sampling with out replacement
  • when to use replacement and without replacement
  • Python implementation of batch gradient algorithm with numpy
  • Python implementation of batch gradient algorithm with scikit-learn
  • Python implementation of batch gradient algorithm with tensorflow
  • Python implementation of batch gradient algorithm with keras
  • Python implementation of mini-batch gradient algorithm with numpy
  • Python implementation of mini-batch gradient algorithm with scikit-learn
  • Python implementation of mini-batch gradient algorithm with tensorflow
  • Python implementation of mini-batch gradient algorithm with keras
  • Python implementation of stochastic gradient algorithm with numpy
  • Python implementation of stochastic gradient algorithm with scikit-learn
  • Python implementation of stochastic gradient algorithm with tensorflow
  • Python implementation of stochastic gradient algorithm with keras

SECTION 5 : Naive Bayes Classifier

  • Introduction to naive bayes
  • Probability basics
  • Mathematics behind naive bayes
  • What is posterior probability
  • Posterior probability for single input variable
  • Posterior probability for multiple input variables
  • How to test predictions
  • Python implementation of naive bayes classifier with numpy
  • Python implementation of naive bayes classifier with scikit-learn
  • Python implementation of naive bayes classifier with tensorflow
  • Python implementation of naive bayes classifier with keras

SECTION 6 : Decision Tree Classifier

  • Introduction to decision tree classifier
  • When to use decision tree classifier
  • Conditional probability basics
  • How to construct decision tree
  • Components of decision tree
  • What is a root node
  • What is a branch
  • What is a leaf node
  • What is a terminal node
  • How to select variables for the nodes
  • Mathematics behind decision tree classifier
  • What is entropy
  • Entropy of target variable
  • Entropy of input variable on target variable
  • What is information gain
  • What is gini index
  • How to compute information gain with python numpy
  • How to compute gini index with python numpy
  • How to construct decision tree with python numpy
  • Python implementation of decision tree classifier with numpy
  • Python implementation of decision tree classifier with scikit-learn
  • Python implementation of decision tree classifier with tensorflow
  • How to test predictions

SECTION 7 : Decision Tree Regressor

  • Introduction to decision tree regressor
  • When to use decision tree regressor
  • Conditional probability basics
  • How to construct decision tree
  • Components of decision tree
  • what is a root node
  • what is a branch
  • what is a leaf node
  • what is a terminal node
  • How to select variables for the nodes
  • Mathematics behind decision tree regressor
  • what is entropy
  • Entropy of target variable
  • Entropy of input variable on target variable
  • What is information gain
  • What is gini index
  • How to compute information gain with python numpy
  • How to compute gini index with python numpy
  • How to construct decision tree with python numpy
  • Python implementation of decision tree regressor with numpy
  • Python implementation of decision tree regressor with scikit-learn
  • Python implementation of decision tree regressor with tensorflow
  • How to test predictions

SECTION 8 : Random Forest

  • Introduction to ensemble models
  • When to use Random forest
  • How to construct Random forest
  • Decision tree vs Random forest
  • Need of multiple trees in Random forest
  • Sampling with replacement
  • Sampling with out replacement
  • Why Random forest opts sampling with replacement method
  • How to choose number of trees in Random forest and guide lines
  • Python implementation of Random forest with scikit-learn
  • How to test predictions
  • Implementation of Random forest for regression problems with python scikit-learn

SECTION 9 : SVM(Support Vector Machine )Classifier

  • Introduction to SVM
  • When to use SVM
  • Problem with probabilistic models
  • How svm solves this problem
  • What is hyperplane
  • What is optimized hyperplane
  • What is marzin of hyperplane
  • What are support vectors
  • How to construct SVM
  • Mathematics behind SVM
  • Python implementation of SVM
  • How to test predictions

SECTION 10 : Ensemble Methods

  • Bagging meta estimator
  • Single estimetors Vs bagging : bias-varience decomposition
  • Forest of Randomised Trees:
    • Random forest’s
    • Extremely Randomized Trees
    • Parameters
    • Parallelization
    • Examples:
    • Plot the decision surfaces of ensembles of trees on the iris dataset
    • Pixel importances with a parallel forest of trees
    • Face completion with a multi-output estimators
    • Feature importance evaluation
    • Examples :

      • Pixel importances with a parallel forest of trees
      • Feature importances with forests of trees
    • Totally Random Trees Embedding :
    • Examples
      • Hashing feature transformation using Totally Random Trees
      • Manifold learning on handwritten digits:
      • Feature transformations with ensembles of trees

    SECTION 11 : AdaBoost

    • Usage
    • Examples:
      • Discrete versus Real AdaBoost
      • Multi-class AdaBoosted Decision trees
      • Two-class AdaBoost
      • Decision Tree Regression with AdaBoost

    SECTION 12 : Gradient Tree Boosting:

    • Classification
    • Regression
    • Examples:
      • Gradient Boosting regression
      • Gradient Boosting Out-of-Bag estimates
      • Controlling the tree size
      • Mathematical formulation
      • Loss Functions

    SECTION 13 : Histogram-Based Gradient Boosting

    • Examples:
      • Partial Dependence Plots
    • Usage
    • Missing values support
    • Low-level parallelism
    • Why it’s faster

    SECTION 14 : Voting Classifier

    • Majority Class Labels
    • Usage
    • Weighted Average Probabilities
    • Using the VotingClassifier with GridSearchCV :
    • Usage

    SECTION 15 : Voting Regressor

    • usage
    • Examples:
      • Plot individual and voting regression predictions

    SECTION 16 : Stacked Generalization

    SECTION 17 : Recommendation Systems

    • Graph Based Recommendations
    • Pattern Based Recommendations with FPGrowth
    • Collaberative filtering using IBCF and UBCF

About Instructor

  • admin

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