Online Trainings Course:

INTRODUCTION TO DATA SCIENCE:

• What is Data Science?
• Who is Data Scientist and who can become a Data Scientist?
• Real time process of Data Science
• Data Science Applications
• Technologies used in Data Science
• Prerequisites knowledge to learn Data Science

INTRODUCTION TO MACHINE LEARINING:

• What is Machine Learning?
• How Machine will learn like Human Learning?
• Traditional Programming vs. machine learning
• Machine Learning engineer responsibilities
• Types of learning
  • Supervised learning
  • Un-supervised learning
• Machine learning algorithms: KNN, Naïve-bayes, Decision trees, Classification rules, Regression (Linear Regression, Logistic Regression), K-means clustering, Association rules, Support Vector Machine, Random Forest.

PYTHON PROGRAMMING:

• What is Python? History of Python
• Python Features, Applications of Python
• Downloading and Installing Python
• Python IDE: Jupyter Notebook & Spyder
• What is Anaconda Navigator?
• Downloading and Installing Anaconda, Jupyter Notebook & Spyder
• Python Programming vs. Existing Programming
• Interactive Mode Programming & Script Mode Programming
• Python Identifiers, Reserved Words
• Lines and Indentations, Quotations, Comments
• Assigning values to variables
• Operators - Arithmetic Operators, Comparison (Relational) Operators, Assignment Operators, Logical Operators, Bitwise Operators, Membership Operators, Identity Operators
• Flavors in Python, Python Versions
• Data Types: int, float, complex, bool, str
• List, Tuple, Range, Bytes & Bytearray
• Set, Frozenset, Dict, None
• Inbuilt Functions in Python, Slice operator - Indexing
• Mutable vs. Immutable, Modules and Packages
• Database Connection - PyMySQL, Defining & Manipulating

NumPy with Python:

• NumPy Environment setup in Python, Features of NumPy
• Array Creation, Indexing & Slicing, Array Manipulation
• Mathematical Functions, Statistical Functions

Pandas with Python:

• Pandas Environment setup in Python
• Features of Pandas, Data Structures
• Series - Create Series, Accessing Data from Series with Position
• DataFrame - Features of DataFrame, Create DataFrame, DataFrame from List, Dict, Row & Column Selecting, Adding & Deleting
• Panel - Create and select data from Panel
• Indexing & Selecting Data, Statistical Functions
• Merging / Joining, Categorical Data

R PROGRAMMING:

• R Programming Introduction
• R Programming vs. Existing Programming
• Downloading and Installing R, What is CRAN?
• R Programming IDE: RStudio, Downloading and Installing RStudio
• Variable Assignment - Displaying & Deleting Variables
• Comments – Single Line and Multi Line Comments
• Data Types – Logical, Integer, Double, Complex, Character
• Operators - Arithmetic Operators, Relational Operators, Logical Operators, Assignment Operators, R as Calculator, Performing different Calculations
• Functions – Inbuilt Functions and User Defined Functions
• STRUCTURES – Vector, List, Matrix, Data frame, Array, Factors
• Inbuilt Constants & Functions

Setting Environment:

• Search Packages in R Environment
• Search Packages in Machine with inbuilt function and manual searching
• Attach Packages to R Environment
• Install Add-on Packages from CRAN
• Detach Packages from R Environment
• Functions and Packages Help

Vectors:

• Vector Creation, Single Element Vector, Multiple Element Vector
• Vector Manipulation, Sub setting & Accessing the Data in Vectors

Lists:

• Creating a List, Naming List Elements, Accessing List Elements
• Manipulating List Elements, Merging Lists, Converting List to Vector

Matrix:

• Creating a Matrix, Accessing Elements of a Matrix
• Matrix Manipulations, Dimensions of Matrix, Transpose of Matrix

Data Frames:

• Create Data Frame, Vector to Data Frame
• Why Characters are Converting into Factors? – stringsAsFactors
• Convert the columns of a data frame to characters
• Extract Data from Data Frame
• Expand Data Frame, Column Bind and Row Bind
• Merging / Joining Data Frames – Inner Join, Outer Join & Cross Join

Arrays:

• Create Array with Multiple Dimensions, Naming Columns and Rows
• Accessing Array Elements, Manipulating Array Elements
• Calculations across Array Elements

Factors:

• Factors in Data Frame, Changing the Order of Levels
• Generating Factor Levels, Deleting Factor Levels

Loading and Reading Data:

• DATA EXTRACTION FROM CSV
  • Getting and Setting the Working Directory
  • Input as CSV File, Reading a CSV File
  • Analyzing the CSV File, Writing into a CSV File
• DATA EXTRACTION FROM URL
• DATA EXTRACTION FROM CLIPBOARD
• DATA EXTRACTION FROM EXCEL
  • Install “xlsx” Package
  • Verify and Load the "xlsx" Package, Input as “xlsx” File
  • Reading the Excel File, Writing the Excel File
• DATA EXTRACTION FROM DATABASES
  • RMySQL Package, Connecting to MySql
  • Querying the Tables, Query with Filter Clause
  • Updating Rows in the Tables, Inserting Data into the Tables
  • Creating Tables in MySql, Dropping Tables in MySql
  • Using dplyr and tidyr package

STATISTICS:

• Mean, Median and Mode
• Data Variability: Range, Quartiles, IQR, Calculating Percentiles
• Variance, Standard Deviation, Statistical Summaries
• Types of Distributions – Normal, Binomial, Poisson
• Probability Distributions, Skewness, Outliers
• Data Distribution, 68–95–99.7 rule (Empirical rule)
• Descriptive Statistics and Inferential Statistics
• Statistics Terms and Definitions, Types of Data
• Data Measurement Scales, Normalization
• Measure of Distance, Euclidean Distance
• Probability Calculation – Independent & Dependent
• Hypothesis Testing, Analysis of Variance

DATA VISUALIZATION:

• Data Visualization with MatPlotLib and Seaborn
• Data Visualization with Graphics and GrDevices
• High Level Plotting and Low Level Plotting
• Pie Charts - Title, Colors, Slice Percentages, Chart Legend
• 3D Pie Charts
• Box Plots - Outliers, Ranges, IQR, Quantiles, Median, Data Distribution Analysis, 68–95–99.7 rule (Empirical rule)
• Bar Charts - Label, Title, Colors, Group Bar, Stacked Bar Charts
• Histograms - Range of X and Y Values
• Line Graphs - Types: Points, Lines, Both, Overplotted, Steps
• Scatterplots
• Combining Plots - Par and Layout

LAZY LEARNING – CLASSIFICATION USING NEAREST NEIGHBORS:

Understanding Classification Using Nearest Neighbors

• The KNN algorithm
• Calculating distance
• Choosing an appropriate k
• Preparing data for use with KNN
• Why is the KNN algorithm lazy?

Diagnosing breast cancer with the KNN algorithm

• Collecting data
• Exploring and preparing the data
  • Transformation-normalizing numeric the data
  • Data preparing –creating training and test datasets
• Training a model on the data
• Evaluating model performance
• Improving model performance
  • Transformation –z-score standardization
  • Testing alternative values of k

PROBABILISTIC LEARNING – CLASSIFICATION USING NAÏVE BAYES:

Understanding Naïve-Bayes

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

The Naïve Bayes Algorithm

• The Naïve Bayes classification
• The Laplace estimator
• Using numeric features with Naïve Bayes

Filtering Mobile Phone Spam with the Naïve-Bayes Algorithm

• Collecting data
• Exploring and preparing the data
• Data preparation –processing text data for analysis
  • Data preparation –creating training and test datasets
  • Visualizing text data-word clouds
  • Data preparation-creating indicator features for frequent words
• Training a model on the data
• Evaluating model performance
• Improving model performance

DIVIDE AND CONQUER – CLASSIFICATION USING DECISION TREES AND RULES:

Understanding decision trees

• Divide conquer
• The C5.0 decision tree algorithm
  • Choosing the best split
  • Pruning the decision tree

Identifying risky bank loans using C5.0 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 classification rules

• Separate and conquer
• The one rule algorithm
• The RIPPER algorithm
• Rules from decision trees

Identifying poisonous mushrooms with rule learners

• Collecting data
• Exploring and preparing data
• Training a model on the data
• Evaluating model performance
• Improving model performance

FORECASTING NUMARIC DATA – REGRESSION METHODS:

Understanding regression

• Simple linear regression
• Ordinary least squares estimation
• Correlations
• Multiple linear regressions

Predicting medical expenses using linear regression

• Collecting data
• Exploring and preparing data
  • Exploring relationships among features- the correlation matrix
  • Visualizing relationships among features –the scatter plot matrix
• Training a model on the data
• Evaluating model performance
• Improving model performance
  • Model specification –adding non-linear relationships
  • Transformation –converting a numeric variable to a binary indicator
  • Model specification –adding interaction effects
  • Putting it all together-an improved regression model

Understanding regression trees and model trees

• Adding regression to trees

Estimating the quality of wines with regression trees and model trees

• Collecting data
• Exploring and preparing the data
• Training a model on the data
  • Visualizing decision trees
• Evaluating model performance
  • Measuring performance with mean absolute error
• Improving model performance

FINDING PATTERNS - MARKET BASKET ANALYSIS USING ASSOCIATION RULES:

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

• Collecting data
• Exploring and preparing the data
  • Data preparation – 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

FINDING GROUPS OF DATA - CLUSTERING WITH K-MEANS:

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
  • Data preparation –dummy coding missing values
  • Data preparing –imputing missing values
• Training a model on the data
• Evaluating model performance
• Improving model performance

EVALUATING MODEL PERFORMANCE:

Measuring Performance for Classification

• Working with classification prediction data in R
• A closer look at confusion matrices
• Using confusion matrices to measure performance
• Beyond accuracy – other measure of performance
  • The kappa statistic
  • Sensitivity and specificity
  • Precision and recall
  • The F- measure
• Visualizing performance TRADEOFFS
  • ROC curves

Estimating future performance

• The holdout method
• Cross-validation
• Bootstrap sampling

IMPROVING MODEL PERFORMANCE:

Tuning Stock Models for Better Performance

• Using caret for automated parameter tuning
  • 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

DEEP LEARNING:

• Installation of Theano, TensorFlow, Keras, OpenCV
• Relating Deep Learning and Traditional Machine Learning
• Basics of Neural Networks
• Artificial Neural Networks
• Deep Neural Networks
• Convolutional Neural Networks
• Recurrent Neural Networks
• Deep learning with Theano
• Deep Learning with TensorFlow
• Deep Learning with Keras
• Deep Learning with OpenCV
• Implementation of Deep learning

ARTIFICIAL INTELLIGENCE:

• AI Introduction
• AI Intelligent Systems
• AI Popular Search Algorithms
• AI Fuzzy Logic Systems
• AI Natural Language Processing
• AI Robotics
• AI Neural Networks

INTRODUCTION TO WEKA

EXPLORE WEKA MACHINE LEARNING TOOLKIT

• Installation of WEKA
• Features of WEKA Toolkit
• Explore & Load data sets in Weka

PERFORM DATA PREPROCESSING TASKS

• Apply Filters on data sets

PERFORMING CLASSIFICATION ON DATA SETS

• J48 Classification Algorithm
• Decision Trees Algorithm
• K-NN Classification Algorithm
• Naive-bayes Classification Algorithm
• Comparing Classification Results

PERFORMING REGRESSION ON DATA SETS

• Simple Linear Regression Model, Multi Linear Regression Model
• Logistic Regression Model, Cross-Validation and Percentage Split

PERFORMING CLUSTERING ON DATA SETS

• Clustering Techniques in Weka
• Simple K-means Clustering Algorithm
• Association Rule Mining on Data Sets
• Apriori Association Rule Algorithm
• Discretization in the Rule Generation Process

GRAPHICAL VISUALIZATION IN WEKA

• Visualization Features in Weka
• Visualize the data in various dimensions
• Plot Histogram, Derive Interesting Insights