Data Science | Python Classroom Training

Trainer Name : Ganapathi Raju 7+ yrs exp

Advanced 0(0 Ratings) 0 Students Enrolled
Created By Harsha Trainings Last Updated Wed, 12-Feb-2020 English

About The Instructor
  • 0 Reviews
  • 17 Students
  • 11 Courses
+ View More
Harsha Trainings is the Best Leader in providing PEGA Training. Our harsha has 6+ years of experience working with all types of Rules in PEGA.
Best pega training institute in Hyderabad which provides Classroom Online and Corporate training across the globe.
Course Schedules
Batch Course Days Class Timings Enroll
Weekday Batch

Online

17 Feb 2020 MON - FRI
(90 Days)
06:30 AM - 07:30 AM IST
(01:00 Hours)
What Will I Learn?
Requirements
+ View More
Description

Curriculum For This Course
414 Lessons
  • • What is Data Science
  • • Why Data Science
  • • A feature selection case study
  • • Types of Analytics
  • • Analytics Lifecycle
  • • What is BigData’
  • • n Vs of Big Data
  • • Big Data Architecture
  • • Big Data Technologies
  • • Hadoop Ecosystem
  • • Types of Machine Learning
  • Types of Data
  • • Data Collection Types
  • • Forms of Data & Sources
  • • What is Data Architecture
  • • Components of Data Architecture
  • • OLTP vs OLAP
  • Central Tendenc y (mean, median and mode)
  • • Interquartile Ran_e. Variance, Standard Deviation
  • Z—Score/T—Score
  • • Co-variance. Correlation
  • • Binomial D istribution, Normal Distribution
  • • Bar Chart, Histogram, B ox whisker pt ot
  • • D ot-pt ot, Line plot, Scatter Pt ot
  • • Central Limit Theorem
  • • Confidence Interval and z-distribution table
  • Statistical Sip•nificance. Hypothesis testing
  • • P-value, One-tailed and Two-tailed Tests
  • • Chi-Square Goodness of Fit Test
  • F- Statistic. Kurtosi s. Skewness
  • • Sanipling. Why Sampling. Sampling Methods
  • • Preprocessing Introduction
  • How to preprocess your data
  • • Data Generation Techniques (Custom data)
  • Data Imputation techniques (Dealing with Missin_• x'alues)
  • • Outlier detection methods
  • Encoding= and Decodinp• techniques
  • Label encoding
  • One hot encoding
  • Data Normalization/ Transformation/Scaling methods
  • Z—score
  • Min Max
  • Dimensionality Reduction methods
  • Advantages of dimensionality reduction methods
  • Principle Component Analysis
  • Linear Discrimination Analysis
  • Singular Valued Decomposition
  • How to select the right data
  • Which are the best features to use
  • Filter Methods
  • F test
  • Mutual Information
  • Variance threshold
  • Wrapper Methods
  • Forward Search
  • Recursive feature elimination
  • Embedded Methods
  • Lasso linear regression
  • Tree based methods
  • Python Overview, About Interpreted Languages
  • Using Variables, Keywords, Built-in Functions, Data Types
  • Strings Different Liberals
  • Math Operators and Expressions
  • String Formatting
  • • flow Control and Loops
  • • Lists
  • • Tuples
  • • Indexing and Slicing
  • • Iterating through a Sequence
  • • Functions for all Sequences
  • • Operators and functions for Sequences
  • • List Comprehensions
  • • Generator Expressions
  • • Dictionaries and Sets
  • • Dictionary Comprehension
  • • Functions
  • • Modules
  • • Regular Expressions
  • • File I/O operators
  • Creating a Database with MySql
  • • CRUD Operations
  • Creating a Database Object.
  • • Introduction to NumPy
  • • Mathematical Functions
  • • Copies & Views
  • • Creating and Printing Ndarray
  • • Class and Attributes of Ndarray
  • • Basic Operations
  • • Introduction to Pandas
  • • Understanding DataFrame
  • • Data Operations
  • • Creating Data Frames
  • • Grouping Sorting=
  • • Plotting Data
  • • Creating Functions
  • • Conx'ertin_ Different Formats
  • • Combining Data from Various Formats
  • • Combining Data from Various Formats
  • • Slicing/Dicing Operations.
  • • File Read and Write Support
  • • Plotting using Matplotlib and Seaborn
  • • Statistical Data Analysis
  • • Fixing missing values
  • Finding outliers
  • • Data quality check
  • Feature transformation
  • • Data Visualization (J'viatplotlib, Seaboarn)
  • o Categorical to Cate_•orical, Catep=orical to Quantitative. Quantitative to Quantitatix'e
  • • Bi-Variate data analysis (Hypothesis Testing)
  • o Categorical and Quantitative, Categorical to Categorical. Quantitative to Categorical, Quantitative to Quantitative
  • • Matplotlib and Seaborn
  • Supervised Learning + Unsupervised Learning
  • • Reinforce Learning
  • • What is Classification/Re_•ression and its u se cases'?
  • • Introduction to Regression/Prediction
  • • Simple Linear Regression algorithm
  • • Multiple Linear Regression
  • • Evaluation metrics (R-Squre. Adj R-S›qure. MSE, RMSE). Hypothesis testing + Multiple linear regression
  • • Train/Test S plit, Hypothesis testing formal z'ay
  • Decision Tree Classifier
  • What is Decision Tree’?
  • Mathematical implementation of decision tree classifier
  • Algorithm for Decision Tree Induction
  • How' to build Decision trees
  • Selecting attribute selecting measures using Information Gain. Gini Index
  • Gain Ratio techniques
  • Tree Pruning
  • Use Case us ink Decision Tree classifier
  • Random Forest Classifier
  • What are Random Forests
  • Features of Random Forest
  • Mathematical implementation of Random Forest Classifier
  • Out of Box Error Estimate and Variable Importance
  • U se Case us ink Random Forest classifier
  • Naive Bayes Classifier.
  • Mathematical implementation of Random Forest Classifier
  • Bayes ian classifier algorithm
  • Use Case us ink Naive Bayes classifier
  • Logistic Regression Overview
  • Algorithm and Mathematical explanation
  • Use Case us ink Logistic Regression for binary classification
  • Support Vector Machines
  • Introduction to S VMs
  • Vectors Overview
  • Decision Surfaces
  • Linear SVNs
  • The Kernel Trick
  • Non-Linear S VMs
  • The Kernel S VM
  • • Accuracy measurements + Precis ion. Recall, Precision — Recall Tread-off
  • • AUC Score, ROC Curve
  • • Trairi/Validation/Test split, K-Fold Cross Validation +The Problem of Gver-fitting (B ias-Variance treat-off
  • • K-Fold Cross Validation
  • • The Problem of (_)ver-f’ittinp (Bias- Variance tread-off)
  • • Learning C url c
  • • Regularization (Ridge, Lasso and Elastic-Set)
  • • Feature selection
  • • lJ›'pcr Paraincicr Tuning (Grid-SearchCV. Randomized-SearchCV)
  • • What is Clustering/Grouping and its u se cases’? + Similarity Metrics
  • • Distance Measure Types: Euclidean. Manhattan, Cosine Measures + Creating predictive models
  • • DB-Scan algorithm
  • • Metrics for Clustering , Silhouette , Elbow method , Homogeneity, completeness ,V-measure
  • • Introduction to Association Rule Mining
  • • Support. Confidence measures + Appriori algorithm for ARM
  • • FP-Growth Algorithm for ARM + Eclat algorithm for ARM
  • • What is Time Series data’? + Time Series variables
  • • Different components of Time Series data + Visualize the data to identify Time Series Components
  • • Implement ARIMA model for forecasting + Exponential smoothing models
  • • Identifying different time series scenario based on which different Exponential Smoothing model can be applied
  • • Implement respective model for forecasting + Visualizing and formatting Time Series data
  • • Plotting decomposeci Time Series data plot + Applying ARIMA and ETS model tor Time Series forecasting
  • • Forecasting for given Time period + Illustrate the working and implementation of different ETS models
  • • Forecast the data using the respective model + Vector Space Model + Ba_ of Words
  • • Tokenization
  • • Stop w'ord Removal
  • • Stemming
  • • POS tagging
  • • I.emmatization
  • • Synset and Wordnet
  • • Topic Modelling
  • • Topic Modellinp• • Document Classification + Document Clustering= + Web Scrapinp• + Word Embedding
  • Introduction to Artificial Neural Networks + Deep Learning Overview
  • • Neural Netw'orks Introduction
  • • Brain ve Neuron
  • • Detailed ANN
  • • Activation functions
  • • How does ANN w'ork and learn'?
  • • Gradient Decent
  • • Stochastic Gradient Decent + Backpropagation
  • Convolutional Neural Networks + Convolution Operation
  • • Rem Layers
  • • What is Pooling and Flattening
  • • Full connection
  • • Soft Max vs Cross Entropy
  • Tensor Flow
  • • Programming a neural network in TensorFlow + Programming a neural network — multilayer perceptron in TensorFlow Keras
  • • Introduction to Keras - a convenient way to code neural netw’orks + What is Convolutional neural netw'ork + How does a CNN w'ork’?
  • CNN and RNN.s
  • • Creating a CAN front scratch + What are RNN- Introduction to RNNs
  • • Recurrent Neural Networks RNN in Python + LSTMs for beginners — understanding LSTM s
  • • Long short-term memory N'N LSTM in Python
  • • Introduction to Big Data
  • • Introduction to Hadoop Environment
  • • Architecture of HDFS
  • • Map Reduce Programming
  • • Introduction to PySpark + Spark Clusters and Data Frames
  • • Linear Regression using PjSpark + Building Classification using PySpark
  • • What is Data Science
  • • Why Data Science
  • • A feature selection case study
  • • Types of Analytics
  • • Analytics Lifecycle
  • • What is BigData’
  • • n Vs of Big Data
  • • Big Data Architecture
  • • Big Data Technologies
  • • Hadoop Ecosystem
  • • Types of Machine Learning
  • Types of Data
  • • Data Collection Types
  • • Forms of Data & Sources
  • • What is Data Architecture
  • • Components of Data Architecture
  • • OLTP vs OLAP
  • Central Tendenc y (mean, median and mode)
  • • Interquartile Ran_e. Variance, Standard Deviation
  • Z—Score/T—Score
  • • Co-variance. Correlation
  • • Binomial D istribution, Normal Distribution
  • • Bar Chart, Histogram, B ox whisker pt ot
  • • D ot-pt ot, Line plot, Scatter Pt ot
  • • Central Limit Theorem
  • • Confidence Interval and z-distribution table
  • Statistical Sip•nificance. Hypothesis testing
  • • P-value, One-tailed and Two-tailed Tests
  • • Chi-Square Goodness of Fit Test
  • F- Statistic. Kurtosi s. Skewness
  • • Sanipling. Why Sampling. Sampling Methods
  • • Preprocessing Introduction
  • How to preprocess your data
  • • Data Generation Techniques (Custom data)
  • Data Imputation techniques (Dealing with Missin_• x'alues)
  • • Outlier detection methods
  • Encoding= and Decodinp• techniques
  • Label encoding
  • One hot encoding
  • Data Normalization/ Transformation/Scaling methods
  • Z—score
  • Min Max
  • Dimensionality Reduction methods
  • Advantages of dimensionality reduction methods
  • Principle Component Analysis
  • Linear Discrimination Analysis
  • Singular Valued Decomposition
  • How to select the right data
  • Which are the best features to use
  • Filter Methods
  • F test
  • Mutual Information
  • Variance threshold
  • Wrapper Methods
  • Forward Search
  • Recursive feature elimination
  • Embedded Methods
  • Lasso linear regression
  • Tree based methods
  • Python Overview, About Interpreted Languages
  • Using Variables, Keywords, Built-in Functions, Data Types
  • Strings Different Liberals
  • Math Operators and Expressions
  • String Formatting
  • • flow Control and Loops
  • • Lists
  • • Tuples
  • • Indexing and Slicing
  • • Iterating through a Sequence
  • • Functions for all Sequences
  • • Operators and functions for Sequences
  • • List Comprehensions
  • • Generator Expressions
  • • Dictionaries and Sets
  • • Dictionary Comprehension
  • • Functions
  • • Modules
  • • Regular Expressions
  • • File I/O operators
  • Creating a Database with MySql
  • • CRUD Operations
  • Creating a Database Object.
  • • Introduction to NumPy
  • • Mathematical Functions
  • • Copies & Views
  • • Creating and Printing Ndarray
  • • Class and Attributes of Ndarray
  • • Basic Operations
  • • Introduction to Pandas
  • • Understanding DataFrame
  • • Data Operations
  • • Creating Data Frames
  • • Grouping Sorting=
  • • Plotting Data
  • • Creating Functions
  • • Conx'ertin_ Different Formats
  • • Combining Data from Various Formats
  • • Combining Data from Various Formats
  • • Slicing/Dicing Operations.
  • • File Read and Write Support
  • • Plotting using Matplotlib and Seaborn
  • • Statistical Data Analysis
  • • Fixing missing values
  • Finding outliers
  • • Data quality check
  • Feature transformation
  • • Data Visualization (J'viatplotlib, Seaboarn)
  • o Categorical to Cate_•orical, Catep=orical to Quantitative. Quantitative to Quantitatix'e
  • • Bi-Variate data analysis (Hypothesis Testing)
  • o Categorical and Quantitative, Categorical to Categorical. Quantitative to Categorical, Quantitative to Quantitative
  • • Matplotlib and Seaborn
  • Supervised Learning + Unsupervised Learning
  • • Reinforce Learning
  • • What is Classification/Re_•ression and its u se cases'?
  • • Introduction to Regression/Prediction
  • • Simple Linear Regression algorithm
  • • Multiple Linear Regression
  • • Evaluation metrics (R-Squre. Adj R-S›qure. MSE, RMSE). Hypothesis testing + Multiple linear regression
  • • Train/Test S plit, Hypothesis testing formal z'ay
  • Decision Tree Classifier
  • What is Decision Tree’?
  • Mathematical implementation of decision tree classifier
  • Algorithm for Decision Tree Induction
  • How' to build Decision trees
  • Selecting attribute selecting measures using Information Gain. Gini Index
  • Gain Ratio techniques
  • Tree Pruning
  • Use Case us ink Decision Tree classifier
  • Random Forest Classifier
  • What are Random Forests
  • Features of Random Forest
  • Mathematical implementation of Random Forest Classifier
  • Out of Box Error Estimate and Variable Importance
  • U se Case us ink Random Forest classifier
  • Naive Bayes Classifier.
  • Mathematical implementation of Random Forest Classifier
  • Bayes ian classifier algorithm
  • Use Case us ink Naive Bayes classifier
  • Logistic Regression Overview
  • Algorithm and Mathematical explanation
  • Use Case us ink Logistic Regression for binary classification
  • Support Vector Machines
  • Introduction to S VMs
  • Vectors Overview
  • Decision Surfaces
  • Linear SVNs
  • The Kernel Trick
  • Non-Linear S VMs
  • The Kernel S VM
  • • Accuracy measurements + Precis ion. Recall, Precision — Recall Tread-off
  • • AUC Score, ROC Curve
  • • Trairi/Validation/Test split, K-Fold Cross Validation +The Problem of Gver-fitting (B ias-Variance treat-off
  • • K-Fold Cross Validation
  • • The Problem of (_)ver-f’ittinp (Bias- Variance tread-off)
  • • Learning C url c
  • • Regularization (Ridge, Lasso and Elastic-Set)
  • • Feature selection
  • • lJ›'pcr Paraincicr Tuning (Grid-SearchCV. Randomized-SearchCV)
  • • What is Clustering/Grouping and its u se cases’? + Similarity Metrics
  • • Distance Measure Types: Euclidean. Manhattan, Cosine Measures + Creating predictive models
  • • DB-Scan algorithm
  • • Metrics for Clustering , Silhouette , Elbow method , Homogeneity, completeness ,V-measure
  • • Introduction to Association Rule Mining
  • • Support. Confidence measures + Appriori algorithm for ARM
  • • FP-Growth Algorithm for ARM + Eclat algorithm for ARM
  • • What is Time Series data’? + Time Series variables
  • • Different components of Time Series data + Visualize the data to identify Time Series Components
  • • Implement ARIMA model for forecasting + Exponential smoothing models
  • • Identifying different time series scenario based on which different Exponential Smoothing model can be applied
  • • Implement respective model for forecasting + Visualizing and formatting Time Series data
  • • Plotting decomposeci Time Series data plot + Applying ARIMA and ETS model tor Time Series forecasting
  • • Forecasting for given Time period + Illustrate the working and implementation of different ETS models
  • • Forecast the data using the respective model + Vector Space Model + Ba_ of Words
  • • Tokenization
  • • Stop w'ord Removal
  • • Stemming
  • • POS tagging
  • • I.emmatization
  • • Synset and Wordnet
  • • Topic Modelling
  • • Topic Modellinp• • Document Classification + Document Clustering= + Web Scrapinp• + Word Embedding
  • Introduction to Artificial Neural Networks + Deep Learning Overview
  • • Neural Netw'orks Introduction
  • • Brain ve Neuron
  • • Detailed ANN
  • • Activation functions
  • • How does ANN w'ork and learn'?
  • • Gradient Decent
  • • Stochastic Gradient Decent + Backpropagation
  • Convolutional Neural Networks + Convolution Operation
  • • Rem Layers
  • • What is Pooling and Flattening
  • • Full connection
  • • Soft Max vs Cross Entropy
  • Tensor Flow
  • • Programming a neural network in TensorFlow + Programming a neural network — multilayer perceptron in TensorFlow Keras
  • • Introduction to Keras - a convenient way to code neural netw’orks + What is Convolutional neural netw'ork + How does a CNN w'ork’?
  • CNN and RNN.s
  • • Creating a CAN front scratch + What are RNN- Introduction to RNNs
  • • Recurrent Neural Networks RNN in Python + LSTMs for beginners — understanding LSTM s
  • • Long short-term memory N'N LSTM in Python
  • • Introduction to Big Data
  • • Introduction to Hadoop Environment
  • • Architecture of HDFS
  • • Map Reduce Programming
  • • Introduction to PySpark + Spark Clusters and Data Frames
  • • Linear Regression using PjSpark + Building Classification using PySpark
Student Feedback
0
Average Rating
  • 0%
  • 0%
  • 0%
  • 0%
  • 0%
Rs.5000 Rs.30000
Includes:
  • On Demand Videos
  • 414 Lessons
  • Full Lifetime Access
  • Access On Mobile And Tv