Data Science Masters Course by PW Skills in Hindi
₹3,500.00
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The “Data Science Masters course in Hindi” is designed by Pw Skills. This program instills in students the skills essential to knowledge discovery efforts to identify standard, novel, and truly differentiated solutions and decision-making, including skills in managing, querying, analyzing, visualizing, and extracting meaning from large data sets.
Data Science Masters Course Overview:-
The “Data Science Masters course in Hindi” is designed by Pw Skills. This program instills in students the skills essential to knowledge discovery efforts to identify standard, novel, and truly differentiated solutions and decision-making, including skills in managing, querying, analyzing, visualizing, and extracting meaning from large data sets. This trending program provides students with the statistical, mathematical, and computational skills needed to meet today’s professional world’s large-scale data science challenges. You will learn all the stack required to work in data science, data analytics, and the big data industry including cloud infrastructure and real-time industry projects.
Pw Skills Course Introductory Video:-
Key Outcomes of this Course:-
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- Python
- Statistics
- Machine learning
- Deep learning
- Computer vision
- Natural language processing
- Big data
- Apache Spark
- Apache Kafka
- Data Analytics
- PowerBI
- Tableau
- Databases
- Data Science Workflow
- Real-Time Data Science Projects
This Course is Designed For:-
-
- Students
What do you get
Here is the list of what you can get with this course!
| No. | Duration | |
|---|---|---|
| Videos | – | 200+ Hour |
| Quiz | – | In every module |
| Reading Material | – | After every module |
| Live Sessions | – | 20% of course |
| “Data Science Masters“ Course by PW Skills is Explained in Hindi | ||
Course Content
Course Introduction
• Course Induction
• Preview
• Course Overview and Dashboard Description
• Introduction Of the Data Industry
• Lab Introduction
• Support System Introduction
• Community Introduction
Python Basic
• Introduction Of Python and Comparison with Other Programming Language
• Python Objects, Number & Booleans, Strings.
• Container Objects, Mutability of Objects
• Operators - Arithmetic, Bitwise, Comparison, and Assignment Operators, Operator's
Precedence and Associativity
• Conditions (If Else, If-Elif-Else), Loops (While For)
• Break And Continue Statement and Range Function
String Objects
• Basic Data Structure In Python
• String Object Basics
• String Inbuilt Methods
• Splitting And Joining Strings
• String Format Functions
List Object Basics
• List Methods
• List As Stack and Queues
• List Comprehensions
Tuples, Set, Dictionaries & Its Function
• Tuples, Sets & Dictionary Object Methods
• Dictionary Comprehensions
• Dictionary View Objects
Function
• Functions Basics, Parameter Passing, Iterators.
• Generator Functions
• Lambda Functions
• Map, Reduce, Filter Functions
Oops Concepts
• Oops Basic Concepts.
• Creating Classes
• Pillars Of Oops
• Inheritance
• Polymorphism
• Encapsulation
• Abstraction
• Decorator
• Class Methods And Static Methods
• Special (Magic/Dunder) Methods
• Property Decorators - Getters, Setters, And Deletes
Files
• Working With Files
• Reading And Writing Files
• Buffered Read And Write
• Other File Methods.
• Logging, Debugger
• Modules And Import Statements
Exception Handling
• Exceptions Handling with Try-Except
• Custom Exception Handling
• List Of General Use Exceptions
• Best Practice Exception Handling
Memory Management
• Multithreading
• Multiprocessing
Database
• MySQL
• Mongo Db
Web API
• What Is Web API
• Difference between API and Web API
• Rest And Soap Architecture
• Restful Services
Flask
• Flask Introduction
• Flask Application
• Open Link Flask
• App Routing Flask
• URL Building Flask
• Http Methods Flask
• Templates Flask
• Flask Project: Food App
• Postman
Pandas Basic
• Python Pandas - Series
• Python Pandas – Data Frame
• Python Pandas – Panel
• Python Pandas - Basic Functionality
• Reading Data from Different File Systems
Pandas Advance
• Python Pandas – Re Indexing Python
• Pandas – Iteration
• Python Pandas – Sorting.
• Working With Text Data Options & Customization
• Indexing & Selecting
• Data Statistical Functions
• Python Pandas - Window Functions
• Python Pandas - Date Functionality
• Python Pandas –Time Delta
• Python Pandas - Categorical Data
• Python Pandas – Visualization
• Python Pandas - Tools
Python NumPy
• NumPy - Nd Array Object.
• NumPy - Data Types.
• NumPy - Array Attributes.
• NumPy - Array Creation Routines.
• NumPy - Array from Existing.
• Data Array from Numerical Ranges.
• NumPy - Indexing & Slicing.
• NumPy – Advanced Indexing.
• NumPy – Broadcasting.
• NumPy - Iterating Over Array.
• NumPy - Array Manipulation.
• NumPy - Binary Operators.
• NumPy - String Functions.
• NumPy - Mathematical Functions.
• NumPy - Arithmetic Operations.
• NumPy - Statistical Functions.
• Sort, Search & Counting Functions.
• NumPy - Byte Swapping.
• NumPy - Copies &Views.
• NumPy - Matrix Library.
• NumPy - Linear Algebra
Visualization
• Matplotlib
• Seaborn
• Plotly
• Bokeh
Statistics Basic
• Introduction To Basic Statistics Terms
• Types Of Statistics
• Types Of Data
• Levels Of Measurement
• Measures Of Central Tendency
• Measures Of Dispersion
• Random Variables
• Set
• Skewness
• Covariance And Correlation
Statistics Advance
• Probability Density/Distribution Function
• Types Of the Probability Distribution
• Binomial Distribution
• Poisson Distribution
• Normal Distribution (Gaussian Distribution)
• Probability Density Function and Mass Function
• Cumulative Density Function
• Examples Of Normal Distribution
• Bernoulli Distribution
• Uniform Distribution
• Z Stats
• Central Limit Theorem
• Estimation
• A Hypothesis
• Hypothesis Testing’s Mechanism
• P-Value
• T-Stats
• Student T Distribution
• T-Stats Vs. Z-Stats: Overview
• When To Use A T-Tests Vs. Z-Tests
• Type 1 & Type 2 Error
• Bayes Statistics (Bayes Theorem)
• Confidence Interval (Ci)
• Confidence Intervals and The Margin Of Error
• Interpreting Confidence Levels and Confidence Intervals
• Chi-Square Test
• Chi-Square Distribution Using Python
• Chi-Square For Goodness of Fit Test
• When To Use Which Statistical Distribution?
• Analysis Of Variance (Anova)
• Assumptions To Use Anova
• Anova Three Type
• Partitioning Of Variance in The Anova
• Calculating Using Python
• F-Distribution
• F-Test (Variance Ratio Test)
• Determining The Values Of F
• F Distribution Using Python
Introduction to Machine Learning
• Ai Vs Ml Vs Dl Vs Ds
• Supervised, Unsupervised, Semi-Supervised, Reinforcement Learning
• Train, Test, Validation Split
• Performance
• Overfitting, Under Fitting
• Bias Vs Variance
Feature Engineering
• Handling Missing Data
• Handling Imbalanced Data
• Up-Sampling
• Down-Sampling
• Smote
• Data Interpolation
• Handling Outliers
• Filter Method
• Wrapper Method
• Embedded Methods
• Feature Scaling
• Standardization
• Mean Normalization
• Min-Max Scaling
• Unit Vector
• Feature Extraction
• PCA (Principle Component Analysis)
• Data Encoding
• Nominal Encoding
• One Hot Encoding
• One Hot Encoding with Multiple Categories
• Mean Encoding
• Ordinal Encoding
• Label Encoding
• Target Guided Ordinal Encoding
• Covariance
• Correlation Check
• Pearson Correlation Coefficient
• Spearman’s Rank Correlation
• VIF
Feature Selection
• Feature Selection
• Recursive Feature Elimination
• Backward Elimination
• Forward Elimination
Exploratory Data Analysis
• Feature Engineering and Selection.
• Analyzing Bike Sharing Trends.
• Analyzing Movie Reviews Sentiment.
• Customer Segmentation and Effective Cross Selling.
• Analyzing Wine Types and Quality.
• Analyzing Music Trends and Recommendations.
• Forecasting Stock and Commodity Prices
Regression
• Linear Regression
• Gradient Descent
• Multiple Linear Regression
• Polynomial Regression
• R Square and Adjusted R Square
• Rmse, Mse, Mae Comparison
• Regularized Linear Models
• Ridge Regression
• Lasso Regression
• Elastic Net
• Complete End-To-End Project with Deployment On Cloud And Ui
Logistics Regression
• Logistics Regression In-Depth Intuition
• In-Depth Mathematical Intuition
• In-Depth Geometrical Intuition
• Hyper Parameter Tuning
• Grid Search Cv
• Randomize Search Cv
• Data Leakage
• Confusion Matrix
• Precision, Recall, F1 Score, Roc, Auc
• Best Metric Selection
• Multiclass Classification in Lr
• Complete End-To-End Project with Deployment In Multi-Cloud Platform
Decision Tree
• Decision Tree Classifier
• In-Depth Mathematical Intuition
• In-Depth Geometrical Intuition
• Confusion Matrix
• Precision, Recall, F1 Score, Roc, Auc
• Best Metric Selection
• Decision Tree Repressor
• In-Depth Mathematical Intuition
• In-Depth Geometrical Intuition
• Performance Metrics
• Complete End-To-End Project with Deployment In Multi-Cloud Platform
Support Vector Machines
• Linear Svm Classification
• In-Depth Mathematical Intuition
• In-Depth Geometrical Intuition
• Soft Margin Classification
• Nonlinear Svm Classification
• Polynomial Kernel
• Gaussian, Rbf Kernel
• Data Leakage
• Confusion Matrix
• Precision, Recall, F1 Score, Roc, Auc
• Best Metric Selection
• Svm Regression
• In-Depth Mathematical Intuition
• In-Depth Geometrical Intuition
• Complete End-To-End Project with Deployment
Naïve Bayes
• Bayes Theorem
• Multinomial Naïve Bayes
• Gaussian Naïve Bayes
• Various Type Of Bayes Theorem And Its Intuition
• Confusion Matrix
• Precision, Recall, F1 Score, Roc, Auc
• Best Metric Selection
• Complete End-To-End Project with Deployment
Ensemble Techniques and Its Types
• Definition Of Ensemble Techniques
• Bagging Technique
• Bootstrap Aggregation
• Random Forest (Bagging Technique)
• Random Forest Repressor
• Random Forest Classifier
• Complete End-To-End Project with Deployment
Boosting
• Boosting Technique
• Ada Boost
• Gradient Boost
• Xgboost
• Complete End-To-End Project with Deployment
KNN
• Knn Classifier
• Knn Repressor
• Variants Of Knn
• Brute Force Knn
• K-Dimension Tree
• Ball Tree
• Complete End-To-End Project With Deployment
Dimensionality Reduction
• The Curse of Dimensionality
• Dimensionality Reduction Technique
• PCA (Principal Component Analysis)
• Mathematics Behind PCA
• Scree Plots
• Eigen-Decomposition Approach
• Practical’s
Clustering
• Clustering And Their Types
• K-Means Clustering
• K-Means++
• Batch K-Means
• Hierarchical Clustering
• Dbscan
• Evaluation Of Clustering
• Homogeneity, Completeness, And V-Measure
• Silhouette Coefficient
• Davies-Bouldin Index
• Contingency Matrix
• Pair Confusion Matrix
• Extrinsic Measure
• Intrinsic Measure
• Complete End-To-End Project with Deployment
Anomaly Detection
• Anomaly Detection Types
• Anomaly Detection Applications
• Isolation Forest Anomaly Detection Algorithm
• Density-Based Anomaly Detection (Local Outlier Factor) Algorithm
• Support Vector Machine Anomaly Detection Algorithm
• Dbscan Algorithm for Anomaly Detection
• Complete End-To-End Project with Deployment
Time Series
• What Is a Time Series?
• Old Techniques
• Arima
• Acf And Pacf
• Time-Dependent Seasonal Components.
• Autoregressive (Ar),
• Moving Average (Ma) And Mixed Arma- Modeler.
Neural Network a Simple Perception
• Neural Network Overview and Its Use Case.
• Detail Mathematical Explanation
• Various Neural Network Architect Overview.
• Use Case of Neural Network In Nlp And Computer Vision.
• Activation Function -All Name
• Multilayer Network.
• Loss Functions. - All 10
• The Learning Mechanism.
• Optimizers. - All 10
• Forward And Backward Propagation.
• Weight Initialization Technique
• Vanishing Gradient Problem
• Exploding Gradient Problem
• Visualization Of Neural Network
TensorFlow Installation Environment Setup for
Deep Learning
• Colab Pro Setup
• TensorFlow Installation 2.0.
• TensorFlow 2.0 Function.
• TensorFlow 2.0 Neural Network Creation.
TensorFlow Network Building & Tracking
• Mini Project in TensorFlow.
• Tensor space
• Tensor Board Integration
• TensorFlow Playground
• Netron
Pytorch Fundamentals
• Pytorch Installation.
• Pytorch Functional Overview.
• Pytorch Neural Network Creation.
Convolution Neural Networks
• CNN Fundamentals
• CNN Explained In Detail - CNN explainer, Tensor space
• Various CNN Based Architecture
• Training CNN from Scratch
• Building Webapps for CNN
• Deployment In Aws, Azure & Google Cloud
Image Classification Architectures
• Various CNN Architecture with Research Paper And Mathematics
• Lenet-5 Variants with Research Paper and Practical
• Alexnet Variants with Research Paper And Practical
• Googlenet Variants with Research Paper and Practical
• Transfer Learning
• Vggnet Variants with Research Paper and Practical
• Resnet Variants with Research Paper and Practical
• Inception Net Variants with Research Paper And Practical
Object Detection Architectures RCNN Family
• FASTER RCNN
• YOLO
Yolo V5 Custom Training
• Introduction To Yolov5
• Installation Of Yolov5
• Data Annotation & Preparation
• Download Data & Configure Path
• Download & Configure Pretrained Weight
• Start Model Training
• Evaluation Curves Yolov5
• Inferencing Using a Trained Model
Yolo V6 Custom Training
• Introduction To Yolov6
• Installation Of Yolov6
• Data Annotation & Preparation
• Download Data & Configure Path
• Download & Configure Pretrained Weight
• Start Model Training
• Evaluation Curves Yolov6
• Inferencing Using a Trained Model
Yolo V7 Custom Training
• Introduction To Yolov7
• Installation Of Yolov7
• Data Annotation & Preparation
• Download Data & Configure Path
• Download & Configure Pretrained Weight
• Start Model Training
• Evaluation Curves Yolov7
• Inferencing Using Trained Mode
Detecron2 Custom Training
• Introduction To Detecron2
• Installation Of Detecron2
• Data Annotation & Preparation
• Download Data & Configure Path
• Download & Configure Pretrained Weight
• Start Model Training
• Evaluation Curves Detecron2
• Inferencing Using a Trained Model
TFOD2 Custom Training
• Introduction To TFOD2
• Installation Of TFOD2
• Data Annotation & Preparation
• Download Data & Configure Path
• Download & Configure Pretrained Weight
• Start Model Training
• Evaluation Curves TFOD2
• Inferencing Using a Trained Model
Image Segmentation
• Scene Understanding
• More To Detection
• Need Accurate Results
• Segmentation
• Types Of Segmentation
• Understanding Masks
• Maskrcnn
• From Bounding Box to Polygon Masks
• Mask RCNN Architecture
Maskrcnn Practical with Detectron2
• Introduction To Detectron2
• Our Custom Dataset
• Doing Annotations Or Labeling Data
• Registering Dataset For Training
• Selection Of Pretrained Model from Model Zoo
• Let's Start Training
• Stop Training or Resume Training
• Inferencing Using the Custom Trained Model In Colab
• Evaluating The Model
Face Recognition
• What Is Face Recognition?
• Evolution Of Face Recognition
• Face Recognition Pipeline
• Data Preprocessing
• Face Detection
• Face Alignment
• Face Identification
• Exploring Face net
• Exploring MTCNN
• Exploring Arc face
Face Recognition Practical’s
• Data Preprocessing
• Face Detection
• Face Alignment
• Face Identification
• Combining All Pipelines
• Building A Desktop App with Tkinter
Object Tracking
• What Is Object Tracking?
• Localization
• Motion
• Flow Of Optics
• Motion Vector
• Tracking Features
• Exploring Deep Sort
• Bytetrack
practical Object Tracking with Detection
• Data Preprocessing
• Using Yolo for Detection
• Preparing Deep sort With Yolo
• Combining Pipelines for Tracking & Detection
GANs
• Introduction To GANs
• Gan Architecture
• Discriminator
• Generator
• Controllable Generation
• WGANs
• DcGANs
• StyleGANs
• Gan Practical's Implementation
NLP Introduction
• Overview Computational Linguistics.
• History Of NLP.
• Why NLP
• Use Of NLP
Text Processing For NLP
• Web Scrapping.
• Text Processing
• Understanding Regex
• Text Normalization
• Word Count.
• Frequency Distribution
• String Tokenization
• Annotator Creation
• Sentence Processing
• Lemmatization In Text Processing
• Word Embedding
• Co-Occurrence Vectors
• Word2Vec
• Doc2Vec
Useful NLP Libraries
• Nltk
• Text Blob
• Stanford NLP
NLP Networks
• Recurrent Neural Networks.
• Long Short-Term Memory (LSTM)
• Bi LSTM
• Stacked LSTM
• Gru Implementation
Attention Based Model
• Seq 2 Seq.
• Encoders And Decoders.
• Attention Mechanism.
• Attention Neural Networks
• Self-Attention
Transfer Learning In NLP
• Introduction To Transformers.
• BERT Model
• GPT2 Model.
Interview Preparation
• Interview Preparation for Python
• Interview Preparation for Statistics
• Interview Preparation for Machine Learning
• Interview Preparation for Computer Vision
• Interview Preparation for NLP
Profile Building
• GitHub
• LinkedIn
Apply for Jobs
• Naukri
• Monster
Projects Covered in Course
Web Scrapping
• Web Scrapping introduction
• Integration With Web Portal.
• Integration With Rest API, Web Portal, and Mongo Db
• Deployment On Web Portal On Aws, Azure, Aws
Image Scrapper
• Image Scrapping Introduction
• Image Scrapping Deployment
• Integration With Rest API, Web Portal and Mongo Db
• Deployment On Web Portal on AWS, Azure
ML Projects
• Fault detection in wafers based on sensor data.
• Cement strength reg.
• Credit card fraud.
• Forest cover classification
• Fraud detection
• Income prediction
• Mushroom classifier
• Phishing classifier
• Thyroid detection
• Visibility climate
Computer Vision Projects
• Object Tracking Project
• Image Classification with SOTA CNNs
• Image to Text using OCRs
• Vision based Attendance System
• Sign Language Detection
• Shredder Systems
NLP Projects
• Movie Review using BERT
• NER using BERT
• POS Tagging with BERT
• Text Generation GPT2
• Question Answering with SQUAD
• Machine Translation with Transformers
• Paraphrasing with BART
• Text2Speech
• Speech2Text
• SpellCorrector
Instructor of This Course
- Krish Naik (Having 10+ years of experience in Data Science and Analytics with product architecture
design and delivery) - Sudhanshu Kumar (Having 8+ years of experience in Big data, Data Science and Analytics with product
architecture design and delivery)
About PW Skills
PW Skills offers High Quality industry ready courses at affordable price range. PW Skills courses are officered in Hindi & English with relevant project to implement knowledge. PW Skills aims to empower professionals and students alike to either jumpstart their careers or leverage existing skills with new, future-driven upgrades..
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