Machine Learning
Course Description
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 Coefficients . 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 ndegreepolynomial 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 scikitlearn
What is tensorflow
What is keras
What is pytorch
Building end to end model with scikitlearn
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 algorithmrevision
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 minibatch 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 scikitlearn
Python implementation of batch gradient algorithm with tensorflow
Python implementation of batch gradient algorithm with keras
Python implementation of minibatch gradient algorithm with numpy
Python implementation of minibatch gradient algorithm with scikitlearn
Python implementation of minibatch gradient algorithm with tensorflow
Python implementation of minibatch gradient algorithm with keras
Python implementation of stochastic gradient algorithm with numpy
Python implementation of stochastic gradient algorithm with scikitlearn
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 scikitlearn
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 scikitlearn
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 scikitlearn
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 scikitlearn
How to test predictions
Implementation of Random forest for regression problems with python scikitlearn
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 : biasvarience 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 multioutput 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
> Multiclass AdaBoosted Decision trees
> Twoclass AdaBoost
> Decision Tree Regression with AdaBoost
SECTION 12 : Gradient Tree Boosting:
> Classification
> Regression
> Examples:
> Gradient Boosting regression
> Gradient Boosting OutofBag estimates
> Controlling the tree size
> Mathematical formulation
> Loss Functions
> Regularization
> Shrinkage
> Subsampling
> Examples:
> Gradient Boosting regularization
> Gradient Boosting OutofBag estimates
> OOB Errors for Random Forests
> Interpretation
> Feature importance
> Examples:
> Gradient Boosting regression
SECTION 13 : HistogramBased Gradient Boosting
> Examples:
> Partial Dependence Plots
> Usage
> Missing values support
> Lowlevel 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
Course Info
 Batch Capacity: 50
 Certificate: No
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