Data Science and Machine Learning Using Python
Learn Data Science & Machine Learning, Deep Learning with Python Language
Trainer : Experienced Data Science Consultant
Duration : 3 Months
Become a Data Scientist
One time registration fee 1000/ Pay Now
Data Science, Deep Learning, & Machine Learning with Python & R Language With Live Machine Learning & Deep Learning Projects
 Project 1 Build your own image recognition model with TensorFlow
 Project 2 Predict fraud with data visualization & predictive modeling!
 Project 3 Spam Detection
 Project 4 Build your own Recommendation System
 Project 5 Build your own Python predictive modeling, regression analysis & machine learning Model
 Getting Started
 Course Introduction
 Course Material & Lab Setup
 Installation
 Python Basic – Part – 1
 Python Basic – Part – 2
 Advance Python – Part – 1
 Advance Python – Part – 2
● Statistics and Probability Refresher, and Python Practice
 Types of Data
 Mean, Median, Mode
 Using mean, median, and mode in Python
 Variation and Standard Deviation
 Probability Density Function; Probability Mass Function
 Common Data Distributions
 Percentiles and Moments
 A Crash Course in matplotlib
 Covariance and Correlation
 Conditional Probability
 Exercise Solution: Conditional Probability of Purchase by Age
 Bayes’ Theorem
● Predictive Models
 Linear Regression
 Polynomial Regression
 Multivariate Regression, and Predicting Car Prices
 MultiLevel Models
● Machine Learning with Python
 Supervised vs. Unsupervised Learning, and Train/Test
 Using Train/Test to Prevent Overfitting a Polynomial Regression
 Bayesian Methods: Concepts
 Implementing a Spam Classifier with Naive Bayes
 KMeans Clustering
 Clustering people based on income and age
 Measuring Entropy
 Install GraphViz32. Decision Trees: Concepts
 Decision Trees: Predicting Hiring Decisions
 Ensemble Learning
 Support Vector Machines (SVM) Overview
 Using SVM to cluster people using scikitlearn
● Recommender Systems
 UserBased Collaborative Filtering
 ItemBased Collaborative Filtering
 Finding Movie Similarities
 Improving the Results of Movie Similarities
 Making Movie Recommendations to People
 Improve the recommender’s results
● More Data Mining and Machine Learning Techniques
 KNearestNeighbors: Concepts
 Using KNN to predict a rating for a movie
 Dimensionality Reduction; Principal Component Analysis
 PCA Example with the Iris data set
 Data Warehousing Overview: ETL and ELT
 Reinforcement Learning
● Dealing with RealWorld Data
 Bias/Variance Tradeoff
 KFold CrossValidation to avoid overfitting
 Data Cleaning and Normalization
 Cleaning web log data
 Normalizing numerical data
 Detecting outliers
● Experimental Design
 A/B Testing Concepts
 TTests and PValues
 Handson With TTests
 Determining How Long to Run an Experiment
 A/B Test Gotchas
● Deep Learning and Neural Network
● Statistics and Data Science in R
● Introduction
 Introduction to R
 R and R studio Installation & Lab Setup
 Descriptive Statistics
● Descriptive Statistics
 0Mean, Median, Mode
 Our first foray into R : Frequency Distributions
 Draw your first plot : A Histogram
 Computing Mean, Median, Mode in R
 What is IQR (Interquartile Range)?
 Box and Whisker Plots
 The Standard Deviation
 Computing IQR and Standard Deviation in R
● Inferential Statistics
 Drawing inferences from data
 Random Variables are ubiquitous
 The Normal Probability Distribution
 Sampling is like fishing
 Sample Statistics and Sampling Distributions
● Case studies in Inferential Statistics
● Diving into R
 Harnessing the power of R
 Assigning Variables
 Printing an output
 Numbers are of type numeric
 Characters and Dates
 Logicals
● Vectors
 Data Structures are the building blocks of R
 Creating a Vector
 The Mode of a Vector
 Vectors are Atomic
 Doing something with each element of a Vector
 Aggregating Vectors
 Operations between vectors of the same length
 Operations between vectors of different length
 Generating Sequences
 Using conditions with Vectors
 Find the lengths of multiple strings using Vectors
 Generate a complex sequence (using recycling)
 Vector Indexing (using numbers)
 Vector Indexing (using conditions)
 Vector Indexing (using names)
● Arrays
 Creating an Array
 Indexing an Array
 Operations between 2 Arrays
 Operations between an Array and a Vector
 Outer Products
● Matrices
 A Matrix is a 2Dimensional Array
 Creating a Matrix
 Matrix Multiplication
 Merging Matrices
 Solving a set of linear equations
● Factors
 What is a factor?
 Find the distinct values in a dataset (using factors)
 Replace the levels of a factor
 Aggregate factors with table()
 Aggregate factors with tapply()
● Lists and Data Frames
 Introducing Lists
 Introducing Data Frames
 Reading Data from files
 Indexing a Data Frame
 Aggregating and Sorting a Data Frame
 Merging Data Frames
● Regression quantifies relationships between variables
 Linear Regression in Excel : Preparing the data.
 Linear Regression in Excel : Using LINEST()
● Linear Regression in R
 Linear Regression in R : Preparing the data
 Linear Regression in R : lm() and summary()
 Multiple Linear Regression
 Adding Categorical Variables to a linear mode
 Robust Regression in R : rlm()
 Parsing Regression Diagnostic Plots
○ Predictive Models
 Linear Regression
 Polynomial Regression
 Multivariate Regression, and Predicting Car Prices
 MultiLevel Models
○ Machine Learning with R
 Supervised vs. Unsupervised Learning, and Train/Test
 Using Train/Test to Prevent Overfitting a Polynomial Regression
 Bayesian Methods: Concepts
 Implementing a Spam Classifier with Naive Bayes
 KMeans Clustering
 Clustering people based on income and age
 Measuring Entropy
 Install GraphViz32. Decision Trees: Concepts
 Decision Trees: Predicting Hiring Decisions
 Ensemble Learning
 Support Vector Machines (SVM) Overview
 Using SVM to cluster people using scikitlearn
○ Recommender Systems
 UserBased Collaborative Filtering
 ItemBased Collaborative Filtering
 Finding Movie Similarities
 Improving the Results of Movie Similarities
 Making Movie Recommendations to People
 Improve the recommender’s results
○ More Data Mining and Machine Learning Techniques
 KNearestNeighbors: Concepts
 Using KNN to predict a rating for a movie
 Dimensionality Reduction; Principal Component Analysis
 PCA Example with the Iris data set
 Data Warehousing Overview: ETL and ELT
 Reinforcement Learning
○ Dealing with RealWorld Data
 Bias/Variance Tradeoff
 KFold CrossValidation to avoid overfitting
 Data Cleaning and Normalization
 Cleaning web log data
 Normalizing numerical data
 Detecting outliers
○ Experimental Design
 A/B Testing Concepts
 TTests and PValues
 Handson With TTests
 Determining How Long to Run an Experiment
 A/B Test Gotchas
● Data Visualization in R
 Data Visualization
 The plot() function in R
 Control color palettes with RColorbrewer
 Drawing bar plots
 Drawing a heatmap
 Drawing a Scatterplot Matrix
 Plot a line chart with ggplot
We Will Be Updated Soon.
Machine Learning Using Python
Basic Python
1 Introduction
1.1 What is Python..?
1.2 A Brief history of Python
1.3 Installing Python
1.4 How to execute Python program
 Using the Python Interpreter
 1. Invoking the Interpreter
 1.1. Argument Passing
 1.2. Interactive Mode
 2. The Interpreter and Its Environment
 2.1. Source Code Encoding
 1. Invoking the Interpreter
 An Informal Introduction to Python
 1. Using Python as a Calculator
 1.1. Numbers
 1.2. Strings
 1.3. Lists
 2. First Steps Towards Programming
 1. Using Python as a Calculator
 More Control Flow Tools
 1. if Statements
 2. for Statements
 3. The range() Function
 4. break and continue Statements, and else Clauses on Loops
 5. pass Statements
 6. Defining Functions
 7. More on Defining Functions
 7.1. Default Argument Values
 7.2. Keyword Arguments
 7.3. Arbitrary Argument Lists
 7.4. Unpacking Argument Lists
 7.5. Lambda Expressions
 7.6. Documentation Strings
 7.7. Function Annotations
 8. Intermezzo: Coding Style
 Data Structures
 1. More on Lists
 1.1. Using Lists as Stacks
 1.2. Using Lists as Queues
 1.3. List Comprehensions
 1.4. Nested List Comprehensions
 2. The del statement
 3. Tuples and Sequences
 4. Sets
 5. Dictionaries
 6. Looping Techniques
 7. More on Conditions
 8. Comparing Sequences and Other Types
 1. More on Lists
 Modules
 1. More on Modules
 1.1. Executing modules as scripts
 1.2. The Module Search Path
 1.3. “Compiled” Python files
 2. Standard Modules
 3. The dir() Function
 4. Packages
 4.1. Importing * From a Package
 4.2. Intrapackage References
 4.3. Packages in Multiple Directories
 1. More on Modules
 Input and Output
 1. Fancier Output Formatting
 1.1. Formatted String Literals
 1.2. The String format() Method
 1.3. Manual String Formatting
 1.4. Old string formatting
 2. Reading and Writing Files
 2.1. Methods of File Objects
 2.2. Saving structured data with json
 1. Fancier Output Formatting
Data Science with Python
 Install Anaconda Distribution as per OS from https://www.anaconda.com/distribution/ (Python 3.7 version)
 Sign Up for account creation on https://www.hackerrank.com/
 Sign up for account creation on https://www.kaggle.com/
 Sign up for account creation on https://github.com/
 Git Bash Utility – https://gitscm.com/downloads
Module 1: Statistics and Probability
 Descriptive Statistics:
 Central tendency: Mean, Median, Mode
 Sample variance
 Standard deviation
 Random Variables: Discrete, Continuous
 Probability density functions
 Binomial distribution
 Expected Value, E(X)
 Poisson Process
 Law of large numbers
 Standard normal distribution and empirical rule
 Zscore
 Inferential Statistics:
 Central limit theorem
 Sampling distribution of the sample mean
 Standard error of the mean
 Mean and variance of Bernoulli distribution
 Margin of error 1
 Margin of error 2
 Confidence interval
 Hypothesis testing and pvalue
 Onetailed and two tailed tests
 Zstatistics and Tstatistics
 Type 1 error
 Squared error of regression line
 Coefficient of determination
 Chisquare distribution
 Pearson’s chi square test (goodness of fit)
 Corelation and casualty.
Module 2: Data Analysis using Python
 Numpy
 Numpy Vector and Matrix
 Functions – arange(), zeros(), ones(), linspace(), eye(),
 reshape(), random(), max(), min(),
 argmax(), argmin(), shape and dtype attribute
 Indexing and Selection
 Numpy Operations – Array with Array, Array with Scalars,
 Universal Array Functions
 Pandas
 Pandas Series
 Pandas DataFrame
 Missing Data (Imputation)
 Group by Operations
 Merging, Joining and Concatenating DataFrame.
 Pandas Operations
 Data Input and Output from wide variety of formats like csv, excel, db and html etc.
Module 3: Data Visualization using python Matplotlib, Seaborn, Pandasin built, Plotly and Cufflinks
 Matplotlib
 plot() using Functional approach
 multiplot using subplot()
 figure() using OO API Methods
 add_axes(), set_xlabel(), set_ylabel(), set_title() Methods
 Customization – figure size, impoving dpi, Plot appearance,
 Markers, Control over axis appearance and special Plot Types
 Seaborn
 Distribution Plots using distplot(), jointplot(), pairplot(), rugplot(), kdeplot()
 Categorical Plots using barplot(), countplot(), boxplot(), violinplot(), stripplot(), swarmplot(), factorplot()
 Matrix Plots using heatmap(), clustermap()
 Grid Plots using PairGrid(), FacetGrid()
 Regression Plots using lmplot()
 Styles and Colors customization.
 Plotly and Cufflinks
 Interactive Plotting using Plotly and Cufflinks
 Pandas Builtin
 Histogram, Area Plot, Bar Plot, Scatter Plot, Boxplot, Hexplot, Kdeplot, Density Plot e. Choropleth Maps
 Interactive World Map and US Map using Plotly and Cufflinks Module
Module 4: GIT
 Distribution Version Control System
 How internally, GIT Manages Version Control on Changesets.
 Creating Repository
 Basic Commands like, git status, git add, git remove, git branch, git checkout, git log, git catfile, git pull, git push, git commit
 Managing Configuration – System Level, User Level, Repository level
Module 5: Jupyter Notebook
 Introduction, Basic Commands, Keyboard Shortcut and Magic Functions
Module 6: Linear Algebra and Calculus
 Vector and Matrix, basic operations
 Trigonometry
 Derivatives
Module 7: SQL
 MySQL Server and Client Installation
 SQL Queries
 CRUD Operations
 Types of tables(Fact and dimension)
Module 8: Big Data
 What is big data?
 What is distributed computing?
 What is parallel processing?
 Why data scientist require big data?
Module 9: Machine Learning Introduction
 What is Machine Learning?
 Machine Learning Process FlowDiagram
 Different Categories of Machine Leaning – Supervised, Unsupervised and Reinforcement
 ScikitLearn Overview
 ScikitLearn cheatsheet
Module 10: Regression
 Linear Regression
 Robust Regression (RANSAC Algorithm)
 Exploratory Data Analysis (EDA)
 Correlation Analysis and Feature Selection
 Performance Evaluation – Residual Analysis, Mean Square Error (MSE), Coefficient of
 Determination R^2, Mean Absolute Error (MAE), Root Mean Square Error (RMSE)
 Polynomial Regression
 Regularized Regression – Ridge, Lasso and Elastic Net Regression
 BiasVariance TradeOff
 Cross Validation – Hold Out and KFold Cross Validation
 Data PreProcessing – Standardization, MinMax, Normalization and Binarization
 Gradient Descent
Module 11: Classification – Logistic Regression
 Sigmoid function
 Logistic Regression learning using Stochastic Gradient Descent (SGD)
 SGDClassifier
 Measuring accuracy using CrossValidation, Stratified kfold
 Confusion Matrix – True Positive (TP), False Positive (FP), False
 Negative (FN), True Negative (TN)
 Precision, Recall, F1 Score, Precision/Recall TradeOff
 Receiver Operating Characteristics (ROC) Curve.
Module 12: Classification – kNearest Neighbor(KNN)
 Classification and Regression
 Application, Advantages and Disadvantages
 Distance Metric – Euclidean, Manhattan, Chebyshev, Minkowski
 Measuring accuracy using CrossValidation, Stratified kfold, Confusion Matrix, Precision, Recall, F1score.
Module 13: Classification – SVM (Support Vector Machine)
 Classification and Regression
 Separating line, Margin and Support Vectors
 Linear SVC Classification
 Polynomial Kernel – Kernel Trick
 Gaussian Radial Basis Function (rbf)
 Grid Search to tune hyperparameters.
 Support Vector Regression.
Module 14: Classification –Decision Trees
 CART (Classification and Regression Tree)
 Advantages and Disadvantages and its applications.
 Decision Tree Learning algorithms – ID3, C4.5, C5.0 and CART.
 Gini Impurity, Entropy and Information Gain
 Decision Tree Regression
 Visualizing a Decision Tree using graphviz module.
 Regularization using tuning hyperparameters using GridSearch CV.
Module 15: Classification – Ensemble Methods
 Bootstrap Aggregating or Bagging
 Random Forest algorithm
 Extremely Randomized (ExtraTrees) Ensemble
 Boosting – AdaBoost (Adaptive Boosting), Gradient Boosting
 Machine (GBM), XGBoost (Extreme Gradient Boosting)
Module 16: Unsupervised Learning – Clustering
 Connectivity based Clustering using Hierarchical Clustering.
 Ward’s Agglomerative Hierarchical Clustering
 KMeans Clustering
 Elbow Method and Solhouette Analysis
Module 17: Unsupervised Learning – Dimensionality Reduction
 Linear Principal Component Analysis (PCA) reduction.
 Kernel PCA
 Linear Discriminant Analysis (LDA) on Supervised Data.
Module 18: Model Deployment On AWS Cloud
 What is cloud computing?
 What is AWS?
 How to store data in AWS S3?
 Create deep learning instance on EC2.
 Amazon sagemaker to train, tune, build and deploy on production.
Module 19: Tableau
 What is tableau? Its Application
 Installing tableau public
 Tableau Application and use
 Tableau tool introduction
 Tableau UIDimensions and measures
 Connecting to data
 Filter and its types
 Groups
 Set
 Hierarchy
 Graphs
 Table calculation
 LOD Expression
 Data Blending
 How we are Different from Others : Our Teachers covers each topics with Real Time Examples . They take 8 Real time project and more than 72+ assignments for almost every topic. We have Trainer from Real Time Industry with 15 years experience in DS. They are working as Data Science Machine Learning and AI consultant having 10+ years in ML & AI real time implementation and migrations.
This is completely Practical oriented training , Means everything you learn you will be able to code for the same . We have students who get confident in coding within 1 week of joining the training. that is our success and method of teaching. Here in Yess InfoTech , we always take prerequisite sessions also. Also we start from basic installation of the IDEs and other required softwares. Our way of teaching is that student will gain the confidence that , they got upskilled to a different level. Also our student got many great positions and salary ranges in many great organizations.

 5 DS Domain Based Project With Real Time Data ( with one trainer – two project.
 9 Moc interviews(Monthly 3)
 Unlimited Assignments
 28 Real Time Scenarios and Major topics
 Basic Python
 Machine Learning with Python
 Installation
 Data Visualization in R
 19 Modules on Basics
60 Hours Online Sessions
12 Hours of assignments
10 hours for One Project and 50 Hrs for 2 Project ( Candidates should prepare with mentor support . 50 hours mentioned is total hours spent on project by each trainer )
Unlimited Interview Questions
Administration and Manual Installation of python with other Domain based projects will be done on regular basis apart from our normal batch schedule .
We do take projects

 Training By 15+ Years experienced Real Time Trainer
 A pool of 60+ real time Practical Sessions on Data Science
 Scenarios and Assignments to make sure you compete with current Industry standards
 World class training methods
 Training until the candidate get satisfed
 Certification and Placement Support until you get certified and placed for 4 years
 All training in reasonable cost
 10000+ Satisfied candidates
 5000+ Placement Records
 Corporate and Online Training in reasonable Cost
 Complete EndtoEnd Project with Each Course
 World Class Lab Facility which facilitates I3 /I5 /I7 computers
 Wifi available in Lab

 Resume And Interview preparation with 100% Handson Practical sessions
 Doubt clearing sessions any time after the course till 1 year
 Happy to help you any time after the course also
Trainer is having 15 year experience in Data Science with 10 years in Data Science Machine Learning and AI. It has been 15 years now that he has been working extensively in the top level Software company. He is having different kind of certifications in DS. He also have done corporate sessions and seminars both in India and abroad. Recently he was engaged by Yess InfoTech for sessions and professional motivator for working processionals to achieve their day to day targets.
All trainers at our organization are currently working on the technologies in reputed organization. The curriculum is not just some theory or some PPTs. We have all practical sessions and that to we ask our student to implement the same in the session only. We provide notes for the same. We use simple easy language and the contents are well absorbed by the candidates. The always give assignment. Also that the faculties are industry experienced so we give real time projects and practice. We also provide recorded sessions but that will be costing differently. Also we provide result oriented training.
 + Curriculum

Data Science, Deep Learning, & Machine Learning with Python & R Language With Live Machine Learning & Deep Learning Projects
 Project 1 Build your own image recognition model with TensorFlow
 Project 2 Predict fraud with data visualization & predictive modeling!
 Project 3 Spam Detection
 Project 4 Build your own Recommendation System
 Project 5 Build your own Python predictive modeling, regression analysis & machine learning Model
 Getting Started
 Course Introduction
 Course Material & Lab Setup
 Installation
 Python Basic – Part – 1
 Python Basic – Part – 2
 Advance Python – Part – 1
 Advance Python – Part – 2
● Statistics and Probability Refresher, and Python Practice
 Types of Data
 Mean, Median, Mode
 Using mean, median, and mode in Python
 Variation and Standard Deviation
 Probability Density Function; Probability Mass Function
 Common Data Distributions
 Percentiles and Moments
 A Crash Course in matplotlib
 Covariance and Correlation
 Conditional Probability
 Exercise Solution: Conditional Probability of Purchase by Age
 Bayes’ Theorem
● Predictive Models
 Linear Regression
 Polynomial Regression
 Multivariate Regression, and Predicting Car Prices
 MultiLevel Models
● Machine Learning with Python
 Supervised vs. Unsupervised Learning, and Train/Test
 Using Train/Test to Prevent Overfitting a Polynomial Regression
 Bayesian Methods: Concepts
 Implementing a Spam Classifier with Naive Bayes
 KMeans Clustering
 Clustering people based on income and age
 Measuring Entropy
 Install GraphViz32. Decision Trees: Concepts
 Decision Trees: Predicting Hiring Decisions
 Ensemble Learning
 Support Vector Machines (SVM) Overview
 Using SVM to cluster people using scikitlearn
● Recommender Systems
 UserBased Collaborative Filtering
 ItemBased Collaborative Filtering
 Finding Movie Similarities
 Improving the Results of Movie Similarities
 Making Movie Recommendations to People
 Improve the recommender’s results
● More Data Mining and Machine Learning Techniques
 KNearestNeighbors: Concepts
 Using KNN to predict a rating for a movie
 Dimensionality Reduction; Principal Component Analysis
 PCA Example with the Iris data set
 Data Warehousing Overview: ETL and ELT
 Reinforcement Learning
● Dealing with RealWorld Data
 Bias/Variance Tradeoff
 KFold CrossValidation to avoid overfitting
 Data Cleaning and Normalization
 Cleaning web log data
 Normalizing numerical data
 Detecting outliers
● Experimental Design
 A/B Testing Concepts
 TTests and PValues
 Handson With TTests
 Determining How Long to Run an Experiment
 A/B Test Gotchas
● Deep Learning and Neural Network
● Statistics and Data Science in R
● Introduction
 Introduction to R
 R and R studio Installation & Lab Setup
 Descriptive Statistics
● Descriptive Statistics
 0Mean, Median, Mode
 Our first foray into R : Frequency Distributions
 Draw your first plot : A Histogram
 Computing Mean, Median, Mode in R
 What is IQR (Interquartile Range)?
 Box and Whisker Plots
 The Standard Deviation
 Computing IQR and Standard Deviation in R
● Inferential Statistics
 Drawing inferences from data
 Random Variables are ubiquitous
 The Normal Probability Distribution
 Sampling is like fishing
 Sample Statistics and Sampling Distributions
● Case studies in Inferential Statistics
● Diving into R
 Harnessing the power of R
 Assigning Variables
 Printing an output
 Numbers are of type numeric
 Characters and Dates
 Logicals
● Vectors
 Data Structures are the building blocks of R
 Creating a Vector
 The Mode of a Vector
 Vectors are Atomic
 Doing something with each element of a Vector
 Aggregating Vectors
 Operations between vectors of the same length
 Operations between vectors of different length
 Generating Sequences
 Using conditions with Vectors
 Find the lengths of multiple strings using Vectors
 Generate a complex sequence (using recycling)
 Vector Indexing (using numbers)
 Vector Indexing (using conditions)
 Vector Indexing (using names)
● Arrays
 Creating an Array
 Indexing an Array
 Operations between 2 Arrays
 Operations between an Array and a Vector
 Outer Products
● Matrices
 A Matrix is a 2Dimensional Array
 Creating a Matrix
 Matrix Multiplication
 Merging Matrices
 Solving a set of linear equations
● Factors
 What is a factor?
 Find the distinct values in a dataset (using factors)
 Replace the levels of a factor
 Aggregate factors with table()
 Aggregate factors with tapply()
● Lists and Data Frames
 Introducing Lists
 Introducing Data Frames
 Reading Data from files
 Indexing a Data Frame
 Aggregating and Sorting a Data Frame
 Merging Data Frames
● Regression quantifies relationships between variables
 Linear Regression in Excel : Preparing the data.
 Linear Regression in Excel : Using LINEST()
● Linear Regression in R
 Linear Regression in R : Preparing the data
 Linear Regression in R : lm() and summary()
 Multiple Linear Regression
 Adding Categorical Variables to a linear mode
 Robust Regression in R : rlm()
 Parsing Regression Diagnostic Plots
○ Predictive Models
 Linear Regression
 Polynomial Regression
 Multivariate Regression, and Predicting Car Prices
 MultiLevel Models
○ Machine Learning with R
 Supervised vs. Unsupervised Learning, and Train/Test
 Using Train/Test to Prevent Overfitting a Polynomial Regression
 Bayesian Methods: Concepts
 Implementing a Spam Classifier with Naive Bayes
 KMeans Clustering
 Clustering people based on income and age
 Measuring Entropy
 Install GraphViz32. Decision Trees: Concepts
 Decision Trees: Predicting Hiring Decisions
 Ensemble Learning
 Support Vector Machines (SVM) Overview
 Using SVM to cluster people using scikitlearn
○ Recommender Systems
 UserBased Collaborative Filtering
 ItemBased Collaborative Filtering
 Finding Movie Similarities
 Improving the Results of Movie Similarities
 Making Movie Recommendations to People
 Improve the recommender’s results
○ More Data Mining and Machine Learning Techniques
 KNearestNeighbors: Concepts
 Using KNN to predict a rating for a movie
 Dimensionality Reduction; Principal Component Analysis
 PCA Example with the Iris data set
 Data Warehousing Overview: ETL and ELT
 Reinforcement Learning
○ Dealing with RealWorld Data
 Bias/Variance Tradeoff
 KFold CrossValidation to avoid overfitting
 Data Cleaning and Normalization
 Cleaning web log data
 Normalizing numerical data
 Detecting outliers
○ Experimental Design
 A/B Testing Concepts
 TTests and PValues
 Handson With TTests
 Determining How Long to Run an Experiment
 A/B Test Gotchas
● Data Visualization in R
 Data Visualization
 The plot() function in R
 Control color palettes with RColorbrewer
 Drawing bar plots
 Drawing a heatmap
 Drawing a Scatterplot Matrix
 Plot a line chart with ggplot
 + What is Next

We Will Be Updated Soon.
 + Introduction

Machine Learning Using Python
Basic Python
1 Introduction
1.1 What is Python..?
1.2 A Brief history of Python
1.3 Installing Python
1.4 How to execute Python program
 Using the Python Interpreter
 1. Invoking the Interpreter
 1.1. Argument Passing
 1.2. Interactive Mode
 2. The Interpreter and Its Environment
 2.1. Source Code Encoding
 1. Invoking the Interpreter
 An Informal Introduction to Python
 1. Using Python as a Calculator
 1.1. Numbers
 1.2. Strings
 1.3. Lists
 2. First Steps Towards Programming
 1. Using Python as a Calculator
 More Control Flow Tools
 1. if Statements
 2. for Statements
 3. The range() Function
 4. break and continue Statements, and else Clauses on Loops
 5. pass Statements
 6. Defining Functions
 7. More on Defining Functions
 7.1. Default Argument Values
 7.2. Keyword Arguments
 7.3. Arbitrary Argument Lists
 7.4. Unpacking Argument Lists
 7.5. Lambda Expressions
 7.6. Documentation Strings
 7.7. Function Annotations
 8. Intermezzo: Coding Style
 Data Structures
 1. More on Lists
 1.1. Using Lists as Stacks
 1.2. Using Lists as Queues
 1.3. List Comprehensions
 1.4. Nested List Comprehensions
 2. The del statement
 3. Tuples and Sequences
 4. Sets
 5. Dictionaries
 6. Looping Techniques
 7. More on Conditions
 8. Comparing Sequences and Other Types
 1. More on Lists
 Modules
 1. More on Modules
 1.1. Executing modules as scripts
 1.2. The Module Search Path
 1.3. “Compiled” Python files
 2. Standard Modules
 3. The dir() Function
 4. Packages
 4.1. Importing * From a Package
 4.2. Intrapackage References
 4.3. Packages in Multiple Directories
 1. More on Modules
 Input and Output
 1. Fancier Output Formatting
 1.1. Formatted String Literals
 1.2. The String format() Method
 1.3. Manual String Formatting
 1.4. Old string formatting
 2. Reading and Writing Files
 2.1. Methods of File Objects
 2.2. Saving structured data with json
 1. Fancier Output Formatting
Data Science with Python
 Install Anaconda Distribution as per OS from https://www.anaconda.com/distribution/ (Python 3.7 version)
 Sign Up for account creation on https://www.hackerrank.com/
 Sign up for account creation on https://www.kaggle.com/
 Sign up for account creation on https://github.com/
 Git Bash Utility – https://gitscm.com/downloads
Module 1: Statistics and Probability
 Descriptive Statistics:
 Central tendency: Mean, Median, Mode
 Sample variance
 Standard deviation
 Random Variables: Discrete, Continuous
 Probability density functions
 Binomial distribution
 Expected Value, E(X)
 Poisson Process
 Law of large numbers
 Standard normal distribution and empirical rule
 Zscore
 Inferential Statistics:
 Central limit theorem
 Sampling distribution of the sample mean
 Standard error of the mean
 Mean and variance of Bernoulli distribution
 Margin of error 1
 Margin of error 2
 Confidence interval
 Hypothesis testing and pvalue
 Onetailed and two tailed tests
 Zstatistics and Tstatistics
 Type 1 error
 Squared error of regression line
 Coefficient of determination
 Chisquare distribution
 Pearson’s chi square test (goodness of fit)
 Corelation and casualty.
Module 2: Data Analysis using Python
 Numpy
 Numpy Vector and Matrix
 Functions – arange(), zeros(), ones(), linspace(), eye(),
 reshape(), random(), max(), min(),
 argmax(), argmin(), shape and dtype attribute
 Indexing and Selection
 Numpy Operations – Array with Array, Array with Scalars,
 Universal Array Functions
 Pandas
 Pandas Series
 Pandas DataFrame
 Missing Data (Imputation)
 Group by Operations
 Merging, Joining and Concatenating DataFrame.
 Pandas Operations
 Data Input and Output from wide variety of formats like csv, excel, db and html etc.
Module 3: Data Visualization using python Matplotlib, Seaborn, Pandasin built, Plotly and Cufflinks
 Matplotlib
 plot() using Functional approach
 multiplot using subplot()
 figure() using OO API Methods
 add_axes(), set_xlabel(), set_ylabel(), set_title() Methods
 Customization – figure size, impoving dpi, Plot appearance,
 Markers, Control over axis appearance and special Plot Types
 Seaborn
 Distribution Plots using distplot(), jointplot(), pairplot(), rugplot(), kdeplot()
 Categorical Plots using barplot(), countplot(), boxplot(), violinplot(), stripplot(), swarmplot(), factorplot()
 Matrix Plots using heatmap(), clustermap()
 Grid Plots using PairGrid(), FacetGrid()
 Regression Plots using lmplot()
 Styles and Colors customization.
 Plotly and Cufflinks
 Interactive Plotting using Plotly and Cufflinks
 Pandas Builtin
 Histogram, Area Plot, Bar Plot, Scatter Plot, Boxplot, Hexplot, Kdeplot, Density Plot e. Choropleth Maps
 Interactive World Map and US Map using Plotly and Cufflinks Module
Module 4: GIT
 Distribution Version Control System
 How internally, GIT Manages Version Control on Changesets.
 Creating Repository
 Basic Commands like, git status, git add, git remove, git branch, git checkout, git log, git catfile, git pull, git push, git commit
 Managing Configuration – System Level, User Level, Repository level
Module 5: Jupyter Notebook
 Introduction, Basic Commands, Keyboard Shortcut and Magic Functions
Module 6: Linear Algebra and Calculus
 Vector and Matrix, basic operations
 Trigonometry
 Derivatives
Module 7: SQL
 MySQL Server and Client Installation
 SQL Queries
 CRUD Operations
 Types of tables(Fact and dimension)
Module 8: Big Data
 What is big data?
 What is distributed computing?
 What is parallel processing?
 Why data scientist require big data?
Module 9: Machine Learning Introduction
 What is Machine Learning?
 Machine Learning Process FlowDiagram
 Different Categories of Machine Leaning – Supervised, Unsupervised and Reinforcement
 ScikitLearn Overview
 ScikitLearn cheatsheet
Module 10: Regression
 Linear Regression
 Robust Regression (RANSAC Algorithm)
 Exploratory Data Analysis (EDA)
 Correlation Analysis and Feature Selection
 Performance Evaluation – Residual Analysis, Mean Square Error (MSE), Coefficient of
 Determination R^2, Mean Absolute Error (MAE), Root Mean Square Error (RMSE)
 Polynomial Regression
 Regularized Regression – Ridge, Lasso and Elastic Net Regression
 BiasVariance TradeOff
 Cross Validation – Hold Out and KFold Cross Validation
 Data PreProcessing – Standardization, MinMax, Normalization and Binarization
 Gradient Descent
Module 11: Classification – Logistic Regression
 Sigmoid function
 Logistic Regression learning using Stochastic Gradient Descent (SGD)
 SGDClassifier
 Measuring accuracy using CrossValidation, Stratified kfold
 Confusion Matrix – True Positive (TP), False Positive (FP), False
 Negative (FN), True Negative (TN)
 Precision, Recall, F1 Score, Precision/Recall TradeOff
 Receiver Operating Characteristics (ROC) Curve.
Module 12: Classification – kNearest Neighbor(KNN)
 Classification and Regression
 Application, Advantages and Disadvantages
 Distance Metric – Euclidean, Manhattan, Chebyshev, Minkowski
 Measuring accuracy using CrossValidation, Stratified kfold, Confusion Matrix, Precision, Recall, F1score.
Module 13: Classification – SVM (Support Vector Machine)
 Classification and Regression
 Separating line, Margin and Support Vectors
 Linear SVC Classification
 Polynomial Kernel – Kernel Trick
 Gaussian Radial Basis Function (rbf)
 Grid Search to tune hyperparameters.
 Support Vector Regression.
Module 14: Classification –Decision Trees
 CART (Classification and Regression Tree)
 Advantages and Disadvantages and its applications.
 Decision Tree Learning algorithms – ID3, C4.5, C5.0 and CART.
 Gini Impurity, Entropy and Information Gain
 Decision Tree Regression
 Visualizing a Decision Tree using graphviz module.
 Regularization using tuning hyperparameters using GridSearch CV.
Module 15: Classification – Ensemble Methods
 Bootstrap Aggregating or Bagging
 Random Forest algorithm
 Extremely Randomized (ExtraTrees) Ensemble
 Boosting – AdaBoost (Adaptive Boosting), Gradient Boosting
 Machine (GBM), XGBoost (Extreme Gradient Boosting)
Module 16: Unsupervised Learning – Clustering
 Connectivity based Clustering using Hierarchical Clustering.
 Ward’s Agglomerative Hierarchical Clustering
 KMeans Clustering
 Elbow Method and Solhouette Analysis
Module 17: Unsupervised Learning – Dimensionality Reduction
 Linear Principal Component Analysis (PCA) reduction.
 Kernel PCA
 Linear Discriminant Analysis (LDA) on Supervised Data.
Module 18: Model Deployment On AWS Cloud
 What is cloud computing?
 What is AWS?
 How to store data in AWS S3?
 Create deep learning instance on EC2.
 Amazon sagemaker to train, tune, build and deploy on production.
Module 19: Tableau
 What is tableau? Its Application
 Installing tableau public
 Tableau Application and use
 Tableau tool introduction
 Tableau UIDimensions and measures
 Connecting to data
 Filter and its types
 Groups
 Set
 Hierarchy
 Graphs
 Table calculation
 LOD Expression
 Data Blending
 Using the Python Interpreter
 + Why Yess Infotech

 How we are Different from Others : Our Teachers covers each topics with Real Time Examples . They take 8 Real time project and more than 72+ assignments for almost every topic. We have Trainer from Real Time Industry with 15 years experience in DS. They are working as Data Science Machine Learning and AI consultant having 10+ years in ML & AI real time implementation and migrations.
This is completely Practical oriented training , Means everything you learn you will be able to code for the same . We have students who get confident in coding within 1 week of joining the training. that is our success and method of teaching. Here in Yess InfoTech , we always take prerequisite sessions also. Also we start from basic installation of the IDEs and other required softwares. Our way of teaching is that student will gain the confidence that , they got upskilled to a different level. Also our student got many great positions and salary ranges in many great organizations.

 5 DS Domain Based Project With Real Time Data ( with one trainer – two project.
 9 Moc interviews(Monthly 3)
 Unlimited Assignments
 28 Real Time Scenarios and Major topics
 Basic Python
 Machine Learning with Python
 Installation
 Data Visualization in R
 19 Modules on Basics
60 Hours Online Sessions
12 Hours of assignments
10 hours for One Project and 50 Hrs for 2 Project ( Candidates should prepare with mentor support . 50 hours mentioned is total hours spent on project by each trainer )
Unlimited Interview Questions
Administration and Manual Installation of python with other Domain based projects will be done on regular basis apart from our normal batch schedule .
We do take projects

 Training By 15+ Years experienced Real Time Trainer
 A pool of 60+ real time Practical Sessions on Data Science
 Scenarios and Assignments to make sure you compete with current Industry standards
 World class training methods
 Training until the candidate get satisfed
 Certification and Placement Support until you get certified and placed for 4 years
 All training in reasonable cost
 10000+ Satisfied candidates
 5000+ Placement Records
 Corporate and Online Training in reasonable Cost
 Complete EndtoEnd Project with Each Course
 World Class Lab Facility which facilitates I3 /I5 /I7 computers
 Wifi available in Lab

 Resume And Interview preparation with 100% Handson Practical sessions
 Doubt clearing sessions any time after the course till 1 year
 Happy to help you any time after the course also
 + Trainer Profile

Trainer is having 15 year experience in Data Science with 10 years in Data Science Machine Learning and AI. It has been 15 years now that he has been working extensively in the top level Software company. He is having different kind of certifications in DS. He also have done corporate sessions and seminars both in India and abroad. Recently he was engaged by Yess InfoTech for sessions and professional motivator for working processionals to achieve their day to day targets.
All trainers at our organization are currently working on the technologies in reputed organization. The curriculum is not just some theory or some PPTs. We have all practical sessions and that to we ask our student to implement the same in the session only. We provide notes for the same. We use simple easy language and the contents are well absorbed by the candidates. The always give assignment. Also that the faculties are industry experienced so we give real time projects and practice. We also provide recorded sessions but that will be costing differently. Also we provide result oriented training.
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Testimonial
It was great learning at Yess InfoTech. I have attended python Course under the guidance of very good trainer. He started from the very basic and covered and shared everything he knew about python . It’s not only about learning but he makes learning interesting and fun as well. It was a great experience. Worth the time and money too.