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