Who can take this course

Course contents are designed to make the student job ready for machine learning positions. The course material has been designed by industry experts.  Knowledge of Python is required to take the course.

Course Contents

Machine Learning Basics

• What is the difference between AI, Machine Learning and Deep Learning
• Approach to machine learning
• How do machines really learn?
• Regression and classification
• How do machine really think - some mathematics
• Labelled Data , Unlabelled Data
• Hypothesis and Weights
• Machine Learning Framework
• Why split data into training and testing
• Why is cross validation required

Feature Engineering

• What are good features
• Cleaning Data
• Normalization
• Standardization
• Feature Crosses

Statistics

• Mean and Median
• Standard Deviation

Linear Regression

• Linear Regression Theory
• Linear Regression Code using sklearn
• What do linear Regression scores mean
• Using Cross Validation In Linear Regression
• Which model to select from CV for production
• Taking Model To Production

Classification Problems

• True Positive and True Negative.
• False Positive And False Negative
• Sensitivity
• Specificity
• TP, TN, FP, FN via graph
• Sensitivity Via Graph
• Specificity Via Graph
• Sensitivity - Specificity Relationship
• Specificity Is Not Precision
• ROC - Area Under Curve
• Different ROC curves
• Confusion Matrix - Accuracy
• Precision
• Recall

KNN - K Nearest Neighbour algorithm

• KNN For Classification
• KNN For Regression
• How do we measure distances
• Deciding value of K - Hyper Parameter Tuning
• KNN Theory Summary
• KNN - Code Implementation
• Visualising Data Using Pandas

Overfitting -Underfitting

• What is overfitting and underfitting?
• What is Regularization
• Regularisation Rate Lambda

Decision Tree

• What are decision trees
• Decision Tree Example -2
• How a decision tree decides to split
• Decision Tree - Split Example
• Decision Tree Information Gain
• Entropy Of Parent Node
• Information Gain For Measurement One
• Information Gain On Measurement Two
• Information gain on Measurement three
• Decision Tree Code Using SkLearn

Ensembling - Combination Of Models

• What is Ensembling
• Ensemble Code
• Bagging
• Bagging Code
• Random Forest
• Random Forest Code
• Boosting
• Ada Boost Code

Logistic Regression

• Logistic Regression Theory
• Logistic Regression Code using SKLearn

SVM

• SVM Theory
• SVM Kernel Trick
• SVM Code using SKLearn
• Bayesian Classifer
• Bayesian Theory
• Bayesian Code using SKLearn

Deep Learning

• What is Deep Learning
• How is deep learning different from shallow learning algorithm
• Deep Learning Layers as representation
• Deep Learning weights and bias
• Deep Learning Activation Function
• Deep Learning Forward Propagation
• Deep Learning Backward Propagation
• What are Tensors
• Deep Learning Using Keras
• Deep Learning Binary Classification
• Deep Learning Multi Classification
• Deep Learning Regression using Keras
• Deep Learning Convolutional Neural Network (CNN)â??
• CNN Filters
• CNN Pooling
• Layers in CNN
• Convolution Operation