Data Science Certification Training

✔ What is Machine learning
✔ 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

✔ Why Python for Machine Learning
✔ Installing Python
✔ Python IDE’s
✔ Writing the First Python Programme
✔ Executing of Python programs

✔ Integer
✔ Float
✔ String
✔ Bool

✔ Relational Operators
✔ Arithmetic Operators
✔ Logical Operators
✔ Concatenation Operator

✔ Lists
✔ Tuples
✔ Sets
✔ Dictionaries
✔ Range

✔ If and else if statements
✔ For and while Statements
✔ Iterables and Iterators
✔ List Comprehensions and Generators
✔ Case Study

✔ Writing user-defined functions
✔ Lambda Functions
✔ Using *args and *kwargs

✔ Handling Error in Python
✔ Types of Errors
✔ Using try and except
✔ Raising an Error

✔ Usage of Modules
✔ Datetime
✔ Re
✔ OS
✔ Numpy
✔ Pandas
✔ Matplotlib
✔ Seaborn
✔ scikit-learn
✔ sklearn and many mores

✔ File Input/output
✔ Opening of Files
✔ Reading Information from Files
✔ Writing to a File

✔ CSV Files
✔ Excel Sheets
✔ SAS Files
✔ From Web
✔ Working with Relational Databases
✔ Using API’s

✔ Exploring the data
✔ Tidying Data for Analysis
✔ Combining Data
✔ Cleaning data

✔ Working with Numpy Arrays
✔ Creating and Manipulating Numpy Arrays
✔ Mathematical and Statistical Functions
✔ Subsetting Numpy Arrays
✔ Concatenating Numpy Arrays

✔ Pandas Foundations
✔ Data Ingestion and Inspection
✔ Exploratory Data Analysis
✔ Time Series in Pandas

✔ Extracting and Transforming Data
✔ Advanced Indexing
✔ Re-arranging and Re-shaping of data
✔ Grouping Data

✔ Preparing Data
✔ Concatenating Data
✔ Merging Data

✔ Customizing plots
✔ Plotting 2D arrays
✔ Statistical Plotting using Seaborn
✔ Analyzing Time series and Images

✔ Basic Plotting with Bokeh
✔ Layouts, Interactions, and Annotations
✔ Building Interactive Apps with Bokeh

✔ Basic concepts of String Manipulations
✔ Formatting Strings
✔ Pattern Matching
✔ Advance Regular Expressions

✔ 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?

✔ 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

✔ 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

✔ Understanding decision trees
✔ Divide conquer
✔ The Decision tree algorithm

✔ Choosing the best split
✔ Pruning the decision tree

✔ Entropy
✔ Information Gain.
✔ Strengths And Weaknesses
✔ 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

✔ Improving model performance
✔ Evaluating 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 forest 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 APIs