Machine Learning Certification Training

What is MachineLearning
Who is a Data Scientist
How ML is Different from Programming
Languages
Use cases of ML
Techniques used in ML

Data Collection
Data Cleansing
Data Visualization
Web Scrapping

Supervised Learning

Classification
Regression

Un-Supervised Learning

Association Rules
Clustering

Time Series Analysis

Understanding classification using Nearest Neighbours
The KNN algorithm
Different Distance Metrics
Choosing an appropriate k
Preparing data for use with KNN
Strengths And Weaknesses of KNN
Collecting data
Exploring and preparing the data

normalizing numeric data
creating training and test datasets

Training a model on the data
Evaluating model performance
Improving model performance
z-score standardization
Testing alternative values of k
Why KNN is Lazy Learner?
Case Study

Understanding naive Bayes

Basic concepts of Bayesian methods
Probability
Joint probability
Conditional probability with Bayes theorem

The Naive Bayes algorithm

The naive Bayes classification
The Laplace estimator
Using numeric features with naive Bayes
Strengths and Weakness

Spam Filtering Case Study Machine Learning

Collecting data
Exploring and preparing the data
processing text data for analysis
creating training and test datasets
Visualizing text data-word clouds
creating indicator features for frequent words

Training a model on the data
Evaluating model performance
Improving model performance
Case Study

Understanding decision trees
Divide conquer
The Decision tree algorithm

Choosing the best split
Pruning the decision tree

Entropy
Information Gain.
Strengths And Weakness
Identifying risky bank loans using Decision trees

Collect data
Exploring and preparing the data
Data preparation-creating random training and test datasets
Training a model on the data
Evaluating model performance
Improving model performance
Boosting the accuracy of decision trees
Making some mistakes more costly than others

Understanding Regression
Simple linear Regression
Least-squares Estimation
Correlations
Multiple linear regression
Predicting medical expenses using Linear Regression

Collecting data
Exploring and preparing data
Exploring relationships among features- the correlation matrix
Visualizing relationships among features –the scatterplot matrix
Training a model on the data
Evaluating model performance

ODD’s Ratio
Applying Logistic Regression
Training a model on the data
Evaluating the Model Performance
Improving of Model Performance
Other Types of Regressions

Polynomial Regression
Ridge Regression
Lasso Regression
Quantile Regression

Understanding association rules
The Apriori algorithm for association rule learning
Measuring rule interest –support and confidence
Building a set of rules with the Apriori
Identifying frequently purchased groceries with association rules
Creating a sparse matrix for transaction data
Visualizing item support –item frequency plots
Visualizing transaction data-plotting the sparse matrix
Training a model on the data
Evaluating model performance
Improving model performance
Sorting the set of association rules
Taking subsets of association rules
Saving association rules to a file or data frame

Understanding clustering
Clustering as a machine learning task
The K-means algorithm for clustering
Using distance to assign and update cluster
Choosing the appropriate number of cluster
Finding Teen market segments using
K-means clustering
Collecting data
Exploring and preparing the data
Dummy coding missing values
Imputing missing values
Training a model on the data
Evaluating model performance
Improving model performance

Working with classification prediction data
Using confusion matrices to measure performance
Beyond accuracy – another measure of performance
The kappa statistic
Sensitivity and specificity
Precision and recall
The F – measure
Visualizing performance ROC curves
Precision and Recall Curves
Estimating future performance

The holdout method
Cross-validation
Bootstrap sampling

Tuning models for better performance
Hyper Parameters
Creating a simple tuned model
Customizing the tuning process
Improving model performance with meta-learning

Understanding ensembles
Bagging
Boosting
Random forests
Training random forests
Evaluating random forests Performance

Extracting the Data from Twitter/Facebook using API
Cleaning the Data
Performing Text Analysis
Performing Sentimental Analysis

Deployment of Models
Practical Issues
Project Implementation
Case Studies
Cloud Based API’s