The Full Stack Data Scientist Bootcamp®
Last updated 7/2022
MP4 | Video: h264, 1280x720 | Audio: AAC, 44.1 KHz
Language: English | Size: 67.69 GB | Duration: 123h 1m

Full Stats, Python, SQL| Machine Learning & Cloud| Deep Learning| A.I | Computer Vision & NLP | Virtual Internship

What you'll learn
Full Python For Data Science Course
Full Statistics For Data Science Course
Full Machine Learning Course
Full Cloud Deployment Course
Natural Language Processing(NLP)
Full Deep Learning Course
Computer Vision(CV)
Guide to Hackathons and Virtual Internship Projects
Learn Model Deployment on Amazon Web Service(AWS), Google Cloud(GCP), Microsoft Azure, Heroku, Flask API, Streamlit
Hands-On Exercises, Projects, Assignements
Microsoft Power BI
Requirements
This is a Beginner to Advanced course and you do not need to have a prior knowledge or any prerequisites.
The Instructor takes you right from the scratch till mastery.
Your laptop and internet connection is required
Your dedication to start and complete the course is highly recommended

Description
By far the most comprehensive, up-to-date, and credible Data Science course. The Full-Stack Data Scientist BootCamp® is the ONLY course on Udemy that covers A to Z of lessons that will make you a Data Scientist.Created by Dr. Bright, a Ph.D. in Data Science holder, former Microsoft Senior Data Scientist, and a Visiting Faculty at Worcester Institute, this course covers everything that you need to know to become a Full Stack Data Scientist.The instructors and advisors of the course spent over 13 months creating and vetting the course to make sure it meets the industry and academic standards.With 100 hours of quality course curriculum, this course is the same as we use for our 18 months MS in Data Science program on campus and even more exciting are the Projects in the course to make you more efficient and confident in building Data Science and Artificial Intelligence (AI) products. The motivation is to bring Quality Data Science education to every serious learner at affordable cost. Everyone who cannot to spend $30,000 plus on attaining a data science degree at a top tier institute or anyone who cannot spend considerable amount of time on campus away from their busy schedule.This course is meant for students and working professionals who wish to become Data scientists, Machine Learning Engineers, and AI professionals.Included in this course are:Full SQL Course from A-ZFull Python Course from A-ZFull Statistics for Data Science course from A-ZFull Machine Learning course from A-ZFull ML Model Cloud Deployment course A-ZFull Deep Learning course from A-ZFull Artificial Intelligence course from A-ZFull Computer Vision course from A-ZFull Natural Language Processing course from A-ZFull Microsoft Power BI course from A-ZReading Scientific Research PaperGithub for Data ScienceRecommendation SystemA guide to do Virtual InternshipThe instructors and research assistants who created this course have done thorough research in developing this course and making sure to break the concepts down for your understanding taking into consideration people with different backgrounds and experience levels to enroll in this course.We understand the diversity of the audience that will enroll in this course, some with experience in the field and some completely new to the field, we understand that and we kept that in mind while creating the course. So don't worry, you are covered.The very instructors who created the course are going to be your MENTORS throughout the course so you will have someone to come to your aid whenever you get stuck or need help or any form of guidance.If you are interested in becoming a Full Stack Data Scientist, then this course is the right spot for you, and the ALL-in-ONE course to get you there.

Overview

Section 1: CURRICULUM

Lecture 1 Course Curriculum

Lecture 2 download скачать Course Curriculum

Section 2: Data Science Overview

Lecture 3 Lecture resources

Lecture 4 The Big Picture

Lecture 5 Part 1: Data Science Overview

Lecture 6 What Is Data Science?

Lecture 7 DA vs DS vs AI vs ML

Lecture 8 Industries That Use and Hire Data Scientist

Lecture 9 Applications of Data Science

Lecture 10 Data Science Lifecycle and the Maturity Framework

Lecture 11 Who is a Data Scientist?

Lecture 12 Career Opportunities In Data Science

Lecture 13 Typical Backgrounds of Data Scientists

Lecture 14 The Ultimate Path To become a Data Scientist(Skills you need to develop)

Lecture 15 Typical Salary of a Data Scientist

Section 3: FULL SQL FOR DATA SCIENCE COURSE

Lecture 16 Lecture resources

Lecture 17 Overview

Section 4: SQL : BEGINNER LEVEL

Lecture 18 Introduction To SQL for Data Science

Lecture 19 Types of Databases

Lecture 20 What is a Query?

Lecture 21 What is SQL?

Lecture 22 SQL or SEQUEL?

Lecture 23 SQL Installation

Lecture 24 SQL Installation Guide For MacOS

Lecture 25 SQL Installation Guide For Windows

Lecture 26 Extra Help in Installing SQL

Lecture 27 Overview of SQL workbench

Section 5: SQL Commands

Lecture 28 Introduction To SQL Commands

Lecture 29 SQL CRUD Commands

Section 6: Understanding and Creating SQL Databases

Lecture 30 SQL Schema

Lecture 31 Inserting Comments in SQL

Lecture 32 Creating Databases

Section 7: Understanding and Creating SQL Tables

Lecture 33 Overview of SQL Table

Section 8: Types Of SQL KEYS

Lecture 34 Primary Key

Lecture 35 Foreign Key

Lecture 36 Composite Key

Lecture 37 Super Key

Lecture 38 Alternate Key

Section 9: Data Types In SQL

Lecture 39 SQL Data Types

Section 10: CREATE Table and INSERT Data into Table

Lecture 40 CREATE Table

Lecture 41 INSERT Data

Section 11: SQL Constraints

Lecture 42 Understanding SQL Constraints

Lecture 43 NOT NULL & UNIQUE Constraints

Lecture 44 DEFAULT Constraints

Lecture 45 PRIMAY KEY Constraint

Lecture 46 Alter SQL Constraint

Lecture 47 Adding and Dropping SQL Constraint

Lecture 48 Foreign Key Constraint

Section 12: SQL : INTERMEDIATE LEVEL

Lecture 49 Creating Exiting Databases

Lecture 50 Overview Of Existing Databases

Lecture 51 The SELECT Statement in Details

Lecture 52 The ORDER BY Clause

Lecture 53 The WHERE Clause

Lecture 54 Operation with SELECT statement

Lecture 55 Aliasing in SQL

Lecture 56 Exercise 1 and solution

Lecture 57 The DISTINCT Keyword

Lecture 58 WHERE Clause with SQL Comparison operators

Lecture 59 Exercise 2 and Solution

Lecture 60 The AND, OR and NOT Operators

Lecture 61 Exercise 3 and Solution

Lecture 62 The IN Operator

Lecture 63 Exercise 4 and Solution

Lecture 64 The BETWEEN Operator

Lecture 65 Exercise 5 and Solution

Lecture 66 The LIKE Operator

Lecture 67 Exercise 6 and Solution

Lecture 68 The REGEXP Operator

Lecture 69 Exercise 7 and Solution

Lecture 70 IS NULL & IS NOT NULL Operator

Lecture 71 Exercise 8 and Solution

Lecture 72 The ORDER BY Clause in Details

Lecture 73 The LIMIT Clause

Lecture 74 Exercise 9 and Solution

Section 13: SQL JOINS

Lecture 75 Introduction To SQL JOINS

Lecture 76 Exercise 10 and Solution

Lecture 77 Joining Across Multiple Databases

Lecture 78 Exercise 11 and Solution

Lecture 79 Joining Table to Itself

Lecture 80 Joining Across Multiple SQL Tables

Lecture 81 LEFT and RIGHT JOIN

Lecture 82 Exercise 12 and Solution

Lecture 83 Exercise 13 and Solution

Section 14: Working With Existing SQL Table

Lecture 84 INSERTING Into Existing Table

Lecture 85 INSERTING Multiple Data Into Existing Table

Lecture 86 Creating A Copy of a Table

Lecture 87 Updating Existing Table

Lecture 88 Updating Multiple Records In Existing Table

Section 15: SQL VIEW

Lecture 89 Create SQL VIEW

Lecture 90 Using SQL VIEW

Lecture 91 Alter SQL VIEW

Lecture 92 Drop SQL View

Section 16: SQL Data Summarisation: Aggregation Functions

Lecture 93 COUNT () Function

Lecture 94 SUM() Function

Lecture 95 AVG() Function

Lecture 96 SQL Combined Functions

Section 17: Advance SQL Functions

Lecture 97 Count Function in Details

Lecture 98 The HAVING() Function

Lecture 99 LENGTH() Function

Lecture 100 CONCAT() Function

Lecture 101 INSERT() Function

Lecture 102 LOCATE() Function

Lecture 103 UCASE() & LCASE() Function

Section 18: SQL : ADVANCED LEVEL

Lecture 104 Overview

Section 19: SQL Stored Procedure

Lecture 105 Create a Stored Procedure

Lecture 106 Stored Procedure with Single Parameter

Lecture 107 Stored Procedure with Multiple Parameter

Lecture 108 Alter Stored Procedure

Lecture 109 Drop Stored Procedure

Section 20: Triggers

Lecture 110 Introduction to Triggers

Lecture 111 BEFORE Insert Triggers

Lecture 112 AFTER Insert Trigger

Lecture 113 DROP Triggers

Section 21: Transactions

Lecture 114 Creating Transactions

Lecture 115 Rollback Transactions

Lecture 116 Savepoint Transactions

Section 22: FULL PYTHON FOR DATA SCIENCE COURSE

Lecture 117 Overview

Section 23: BEGINNER : Python For Data Science

Lecture 118 Install and Write Your First Python Code

Lecture 119 Python Course Datasets

Section 24: Introduction To Jupyter Notebook

Lecture 120 Introduction to Jupyter Notebook And Jupyter Lab

Lecture 121 Working with Code Vs Markdown

Section 25: Introduction To Google Colab

Lecture 122 Google Colab

Section 26: Getting Hands-On With Python

Lecture 123 Introduction

Lecture 124 Keywords And Identifiers

Lecture 125 Python Comments

Lecture 126 Python Docstring

Lecture 127 Python Variables

Lecture 128 Rules and Naming Conventions for Python Variables

Section 27: Python Output() | Input() | Import() Functions

Lecture 129 Python Output() Function

Lecture 130 Input() Function In Python

Lecture 131 Import() Function In Python

Section 28: Python Operators

Lecture 132 Arithmetic Operators

Lecture 133 Comparison Operators

Lecture 134 Logical Operators

Lecture 135 Bitwise Operators

Lecture 136 Assignment Operators

Lecture 137 Special Operators

Lecture 138 Membership Operators

Section 29: Python Flow Control

Lecture 139 If Statement

Lecture 140 If...Else Statement

Lecture 141 ELif Statement

Lecture 142 For loop

Lecture 143 While loop

Lecture 144 Break Statement

Lecture 145 Continue Statement

Section 30: INTERMEDIATE : Python Functions

Lecture 146 User Define Functions

Lecture 147 Arbitrary Arguments

Lecture 148 Function With Loops

Lecture 149 Lambda Function

Lecture 150 Built-In Function

Section 31: Python Global and Local Variables

Lecture 151 Global Variable

Lecture 152 Local Variable

Section 32: Working With Files In Python

Lecture 153 Python Files

Lecture 154 The Close Method

Lecture 155 The With Statement

Lecture 156 Writing To A File In Python

Section 33: Python Modules

Lecture 157 Python Modules

Lecture 158 Renaming Modules

Lecture 159 The from...import Statement

Section 34: Python Packages and Libraries

Lecture 160 Python Packages and Libraries

Lecture 161 PIP Install Python Libraries

Section 35: Data Types In Python

Lecture 162 Integer & Floating Point Numbers

Lecture 163 Complex Numbers & Strings

Lecture 164 LIST

Lecture 165 Tuple & List Mutability

Lecture 166 Tuple Immutability

Lecture 167 Set

Lecture 168 Dictionary

Section 36: Extra Content

Lecture 169 LIST

Lecture 170 Working On List

Lecture 171 Splitting Function

Lecture 172 Range In Python

Lecture 173 List Comprehension In Python

Section 37: ADVANCED: Python NUMPY

Lecture 174 Lecture Resources

Lecture 175 Introduction To Numpy

Lecture 176 Creating Multi-Dimensional Numpy Arrays

Lecture 177 Numpy: Arange Function

Lecture 178 Numpy: Zeros, Ones and Eye functions

Lecture 179 Numpy: Reshape Function

Lecture 180 Numpy: Linspace

Lecture 181 Numpy: Resize Function

Lecture 182 Numpy:Generating Random Values With random.rand

Lecture 183 Numpy:Generating Random Values With random.randn

Lecture 184 Numpy:Generating Random Values With random.randint

Lecture 185 Numpy: Indexing & Slicing

Lecture 186 Numpy: Broadcasting

Lecture 187 Numpy: How To Create A Copy Dataset

Lecture 188 Numpy: DataFrame Introduction

Lecture 189 Numpy: Creating Matrix

Section 38: Numpy Assignment

Section 39: Python PANDAS

Lecture 190 Pandas Lecture resources

Lecture 191 Pandas- Series 1

Lecture 192 Pandas- Series 2

Lecture 193 Pandas- Loc & iLoc

Lecture 194 Pandas- DataFrame Introduction

Lecture 195 Pandas- Operations On Pandas DataFrame

Lecture 196 Pandas- Selection And Indexing On Pandas DataFrame

Lecture 197 Pandas- Reading A Dataset Into Pandas DataFrame

Lecture 198 Pandas- Adding A Column To Pandas DataFrame

Lecture 199 Pandas- How To Drop Columns And Rows In Pandas DataFrame

Lecture 200 Pandas- How To Reset Index In Pandas Dataframe

Lecture 201 Pandas- How To Rename A Column In Pandas Dataframe

Lecture 202 Pandas- Tail(), Column and Index

Lecture 203 Pandas- How To Check For Missing Values or Null Values(isnull() Vs Isna())

Lecture 204 Pandas- Pandas Describe Function

Lecture 205 Pandas- Conditional Selection With Pandas

Lecture 206 Pandas- How To Deal With Null Values

Lecture 207 Pandas- How To Sort Values In Pandas

Lecture 208 Pandas- Pandas Groupby

Lecture 209 Pandas- Count() & Value_Count()

Lecture 210 Pandas- Concatenate Function

Lecture 211 Pandas- Join & Merge(Creating Dataset)

Lecture 212 Pandas-Join

Lecture 213 Pandas- Merge

Section 40: Data Visualisation: MatplotIib And Seaborn

Lecture 214 Lecture resources

Lecture 215 Matplotlib | Subplots

Lecture 216 Seborn | Scatterplot | Correlation | Boxplot | Heatmap

Lecture 217 Univariate | Bivariate | Multivariate Data Visualisation

Section 41: PROJECT 1:Top Movie Streaming | NETFLIX | Amazon Prime | Hulu | Disney

Lecture 218 Project files

Lecture 219 Top Movie Streaming | NETFLIX | Amazon Prime | Hulu | Disney

Section 42: PROJECT 2: Analysis of UBER Data

Lecture 220 Project files

Lecture 221 Analysis of UBER Data

Section 43: Python Project Assignment

Lecture 222 Assignment resources

Section 44: FULL STATISTICS FOR DATA SCIENCE

Lecture 223 Overview

Section 45: Master Statistics For Data Science

Lecture 224 Lecture resources

Lecture 225 Statistics For Data Science Curriculum

Lecture 226 Why Statistics Is Important For Data Science

Lecture 227 How Much Maths Do I Need To Know?

Section 46: Statistical Methods Deep Dive

Lecture 228 Statistical Methods Deep Dive

Lecture 229 Types Of Statistics

Lecture 230 Common Statistical Terms

Section 47: Data

Lecture 231 What Is Data?

Lecture 232 Data Types

Lecture 233 Data Attributes and Data Sources

Lecture 234 Structured Vs Unstructured Data

Section 48: Frequency Distribution

Lecture 235 Frequency Distribution

Section 49: Central Tendency

Lecture 236 Central Tendency

Lecture 237 Mean,Median, Mode

Section 50: Measures of Dispersion

Lecture 238 Measures of Dispersion

Lecture 239 Variance and Standard Deviation

Lecture 240 Example of Variance and Standard Deviation

Lecture 241 Variance and Standard Deviation In Python

Section 51: Coefficient of Variations

Lecture 242 Coefficient of Variations

Section 52: The Five Number Summary & The Quartiles

Lecture 243 The Five Number Summary

Lecture 244 The Quartiles: Q1 | Q2 | Q3 | IQR

Section 53: The Normal Distribution

Lecture 245 Introduction To Normal Distribution

Lecture 246 Skewed Distributions

Lecture 247 Central Limit Theorem

Section 54: Correlation

Lecture 248 Introduction to Correlation

Lecture 249 Scatterplot For Correlation

Lecture 250 Correlation is NOT Causation

Section 55: Probability

Lecture 251 Why Probability In Data Science?

Lecture 252 Probability Key Concepts

Lecture 253 Mutually Exclusive Events

Lecture 254 Independent Events

Lecture 255 Rules For Computing Probability

Section 56: Baye's Theorem

Lecture 256 Baye's Theorem Overview

Section 57: Hypothesis Testing

Lecture 257 Introduction To Hypothesis

Lecture 258 Null Vs Alternative Hypothesis

Lecture 259 Setting Up Null and Alternative Hypothesis

Lecture 260 One-tailed Vs Two-tailed test

Lecture 261 Key Points On Hypothesis Testing

Lecture 262 Type 1 vs Type 2 Errors

Lecture 263 Process Of Hypothesis testing

Lecture 264 P-Value

Lecture 265 Alpha-Value or Alpha Level

Lecture 266 Confidence Level

Section 58: PROJECT: Statistics For Data Science

Lecture 267 Project resources

Lecture 268 Project Solution Code

Section 59: GITHUB For Data Science

Lecture 269 Lecture resources

Lecture 270 Introduction to Github for Data Science

Lecture 271 Setting up Github account for Data Science projects

Lecture 272 Create Github Profile for Data Science

Lecture 273 Create Github Project Description for Data Science

Section 60: ARTIFICIAL INTELLIGENCE(AI) and MACHINE LEARNING(ML)

Lecture 274 Overview

Section 61: FULL MACHINE LEARNING COURSE

Lecture 275 Introduction To Machine Learning

Lecture 276 Overview of Machine Learning Curriculum

Lecture 277 Practical Understanding Of Machine Learning (PART 1)

Lecture 278 Practical Understanding Of Machine Learning (PART 2)

Lecture 279 Applications of Machine Learning

Lecture 280 Machine Learning Life Cycle

Section 62: USE CASE

Lecture 281 The Microsoft Data Science Project

Lecture 282 Setting Up Your Environment for Machine Learning

Section 63: Machine Learning Algorithms

Lecture 283 How Machine Learning Algorithms Learn

Lecture 284 Difference Between Algorithm and Model

Lecture 285 Supervised vs Unsupervised ML

Lecture 286 Dependent vs Independent Variables

Section 64: Working with Machine Learning Data

Lecture 287 Lecture Resources

Lecture 288 Considerations When Loading Data

Lecture 289 Loading Data from a CSV File

Lecture 290 Loading Data from a URL

Lecture 291 Loading Data from a Text File

Lecture 292 Loading Data from an Excel File

Lecture 293 Skipping Rows while Loading Data

Lecture 294 Peek at your Data

Lecture 295 Dimension of your Data

Lecture 296 Checking Data Types of your Dataset

Lecture 297 Descriptive Statistics of your Dataset

Lecture 298 Class Distribution of your Dataset

Lecture 299 Correlation of your Dataset

Lecture 300 Skewness of your Dataset

Lecture 301 Missing Values in your Dataset

Lecture 302 Histogram of Dataset

Lecture 303 Density Plot of Dataset

Lecture 304 Box and Whisker Plot

Lecture 305 Correlation Matrix

Lecture 306 Scatter Matrix(Pairplot)

Section 65: SUPERVISED MACHINE LEARNING ALGORITHMS

Lecture 307 Overview

Section 66: Regression

Lecture 308 What is Regression?

Section 67: Linear Regression

Lecture 309 Introduction to Linear Regression

Lecture 310 Conceptual Understanding of Linear Regression

Lecture 311 Planes and Hyperplane

Lecture 312 MSE vs RMSE

Section 68: LAB SESSION: Linear Regression

Lecture 313 Training Data vs Validation Data vs Testing Data

Lecture 314 Splitting Dataset into Training and Testing

Lecture 315 Linear Regression LAB 1

Lecture 316 Linear Regression LAB 2(PART 1)

Lecture 317 Linear Regression LAB 2(PART 2)

Section 69: Logistic Regression Algorithm

Lecture 318 Regressor Algorithm Vs Classifier Algorithm

Lecture 319 Introduction To Logistic Regression Algorithm

Lecture 320 Limitations of Linear Regression

Lecture 321 PART 2: Intuitive Understanding Of Logistic Regression

Lecture 322 The Mathematics Behind Logistic Regression Algorithm

Lecture 323 LAB SESSION 1: Practical Implementation of Logistic Regression Algorithm

Lecture 324 LAB SESSION 2: Practical Implementation of Logistic Regression Algorithm

Lecture 325 LAB SESSION 3: Building Logistic Regression Model

Section 70: Naive Bayes Algorithm (NB)

Lecture 326 Introduction to Naive Bayes Algorithm

Lecture 327 The Mathematics Behind Naive Bayes Algorithm

Lecture 328 LAB SESSION: Building Naive Bayes Model

Section 71: K-Nearest Neighbor Algorithm (KNN)

Lecture 329 Introduction to K-Nearest Neighbor Algorithm

Lecture 330 Distance Measures In K-Nearest Neighbor

Lecture 331 Exploratory Data Analysis In K-NN

Lecture 332 LAB SESSION: Building A K-Nearest Neighbor

Lecture 333 Choosing K In K-NN

Section 72: Support Vector Machine Algorithm (SVM)

Lecture 334 Introduction to Support Vector Machine (SVM) algorithm

Lecture 335 Mathematics of SVM and Intuitive Understanding of SVM Algorithm

Lecture 336 Non-Linearly Separable Vectors

Lecture 337 SVM Data Pre-processing

Lecture 338 Building an SVM Model

Section 73: Machine Learning Algorithm Performance Metrics

Lecture 339 Lecture Resources

Lecture 340 Overview

Lecture 341 Confusion Matrix: True Positive | False Positive | True Negative | False Neg.

Lecture 342 Accuracy

Lecture 343 Precision

Lecture 344 Recall

Lecture 345 The Tug of War between Precision and Recall

Lecture 346 F 1 Score

Lecture 347 Classification Report

Lecture 348 ROC and AUC

Lecture 349 LAB SESSION: AUC and ROC

Section 74: Overfitting and Underfitting

Lecture 350 Overfitting and Underfitting

Lecture 351 LAB SESSION: Preventing Overfitting (PART 1)

Lecture 352 LAB SESSION: Preventing Overfitting (PART 2)

Lecture 353 Preventing Underfitting

Section 75: Bias vs Variance

Lecture 354 Bias vs Variance

Lecture 355 The Bias Variance Tradeoff

Section 76: Decision Tree Algorithm

Lecture 356 Decision Tree Overview

Lecture 357 CART: Introduction To Decision Tree

Lecture 358 Purity Metrics: Gini Impurity | Gini Index

Lecture 359 Calculating Gini Impurity (PART 1)

Lecture 360 Calculating Gini Impurity (PART 2)

Lecture 361 Information Gain

Lecture 362 Overfitting in Decision Trees

Lecture 363 Prunning

Lecture 364 LAB SESSION: Prunning

Section 77: Ensemble Techniques

Lecture 365 Lecture Resources

Lecture 366 Introduction To Ensemble Techniques

Lecture 367 Understanding Ensemble Techniques

Lecture 368 Difference b/n Random Forest & Decision Tree

Lecture 369 Why Random Forest Algorithm

Lecture 370 More on Random Forest Algorithm

Lecture 371 Introduction to Bootstrap Sampling | Bagging

Lecture 372 Understanding Bootstrap Sampling

Lecture 373 Diving Deeper into Bootstrap Sampling

Lecture 374 Bootstrap Sampling summary

Lecture 375 Bagging

Lecture 376 Boosting

Lecture 377 Adaboost : Introduction

Lecture 378 The Maths behind Adaboost algorithm

Lecture 379 Gradient Boost: Introduction

Lecture 380 Gradient Boosting : An Intuitive Understanding

Lecture 381 The Mathematics behind Gradient Boosting Algorithm

Lecture 382 XGBoost: Introduction

Lecture 383 Maths of XGBoost (PART 1)

Lecture 384 Maths of XGBoost (PART 2)

Lecture 385 LAB SESSION 1: Ensemble Techniques

Lecture 386 LAB SESSION 2: Ensemble Techniques

Lecture 387 Stacking: An Introduction

Lecture 388 LAB SESSION: Stacking

Section 78: UNSUPPERVISED MACHINE LEARNING ALGORITHMS

Lecture 389 Overview

Section 79: K-Means Clustering Algorithm

Lecture 390 Difference between K-NN and K-Means

Lecture 391 Introduction to K-Means Clustering algorithm

Lecture 392 The Llyod's Method-Shifting the Centroids

Lecture 393 LAB SESSION: K-Means Algorithm

Lecture 394 Choosing K in Kmeans-The Elbow Method

Section 80: Hierarchical Clustering Algorithm

Lecture 395 Introduction to Hierarchical Clustering

Lecture 396 Dendrograms(Cophenetic correlation)

Lecture 397 LAB SESSION: Building Hierarchical Clustering Model

Section 81: Principal Component Analysis (PCA)

Lecture 398 Overview of Principal Component Analysis (PCA)

Section 82: Feature Engineering : Model Selection & Optimisation

Lecture 399 Lecture Resources

Lecture 400 KFold Cross Validation

Lecture 401 LAB SESSION: KFold Cross Validation

Lecture 402 Bootstrap Sampling

Lecture 403 Leave One Out Cross Validation(LOOCV)

Lecture 404 Hyper-parameter Tuning: An Introduction

Lecture 405 GridSearchCV: An Introduction

Lecture 406 RandomSearchCV: An Introduction

Lecture 407 LAB SESSION 1: GridSearchCV

Lecture 408 LAB SESSION 2: GridSearchCV

Lecture 409 LAB SESSION: RandomSearchCV

Lecture 410 Reguralization

Lecture 411 Lasso(L1) and Ridge (L2) Regression

Section 83: Saving and Loading ML Model

Lecture 412 Saving and Loading ML Model

Section 84: WEB SCRAPING

Lecture 413 Lecture resources

Lecture 414 Introduction To Web Scraping Libraries

Lecture 415 Library- Requests

Lecture 416 Library- BeautifulSoup

Lecture 417 Library- Selenium

Lecture 418 Library- Scrapy

Section 85: Web Scraping On Wikipedia

Lecture 419 Web Scraping On Wikipedia

Section 86: Online Book Store Web Scraping

Lecture 420 Lecture resources

Lecture 421 Critical Analysis Of Web Pages

Lecture 422 PART 1- Examining And Scraping Individual Entities From Source Page

Lecture 423 PART 2- Examining And Scraping Individual Entities From Source Page

Lecture 424 Data Preprocessing On Scraped Data

Section 87: Job Board Data Web Scrapping and Automation with Python

Lecture 425 lecture resources

Lecture 426 Indian Institute Of Business(ISB)- Project Introduction

Lecture 427 Problem Statement & Dataset

Lecture 428 Demystify The Structure Of Web Page URLs

Lecture 429 Formulating Generic Web Page URLs

Lecture 430 Forming The Structure Of Web Page URLs

Lecture 431 Creating A DataFrame For Scraped Data

Lecture 432 Creating A Generic Auto Web Scraper

Section 88: RECOMMENDATION SYSTEMS

Lecture 433 Lecture Resources

Lecture 434 Recommendation System: An Overview

Lecture 435 Where Recommender Systems came from

Lecture 436 Applications of Recommendation Systems

Lecture 437 Why Recommender Systems?

Lecture 438 Types of Recommender Systems

Lecture 439 Popularity based Recommender Systems

Lecture 440 LAB SESSION: Popularity based Recommender

Lecture 441 Content-based Filtering: An Overview

Lecture 442 Cosine Similarity

Lecture 443 Cosine Similarity with Python

Lecture 444 Document Term Frequency Matrix

Lecture 445 LAB SESSION: Building Content-based Recommender Engine

Lecture 446 Collaborative Filtering: An Introduction

Lecture 447 LAB SESSION: Collaborative Filtering

Lecture 448 Evaluation Metrics for Recommender Systems

Section 89: STREAMLIT TUTORIAL

Lecture 449 Overview

Lecture 450 Part 1

Lecture 451 Part 2

Lecture 452 Part 3

Lecture 453 PART 1 : Building Your First Streamlit App

Lecture 454 PART 2 : Building Your First Streamlit App

Lecture 455 PART 3 : Building Your First Streamlit App

Lecture 456 PART 4 : Building Your First Streamlit App

Section 90: FLASK TUTORIAL

Lecture 457 Introduction

Lecture 458 Installation and Initializing Flask

Lecture 459 Linking HTML files

Lecture 460 Linking CSS files.mp4

Section 91: End-to-End Machine Learning with DEPLOYMENT : Predict Restaurant Rating

Lecture 461 Predict Restaurant Rating

Lecture 462 Dataset overview

Lecture 463 Exploratory Data Analysis (EDA)

Lecture 464 ML Model Building

Lecture 465 Key Flask Concepts

Lecture 466 Creating Folders

Lecture 467 Creating Folder Contents

Lecture 468 Final Deployment

Section 92: CLOUD: Heroku Deployment

Lecture 469 Predict Flight Price

Lecture 470 Part 1

Lecture 471 Part 2

Lecture 472 Part 3

Lecture 473 Part 4

Lecture 474 Part 5

Lecture 475 Part 6 : Final Deployment

Section 93: CLOUD Deployment: Amazon Web Service

Lecture 476 Lecture Resources

Lecture 477 Introduction: AWS Deployment

Lecture 478 Dataset Overview

Lecture 479 Creating App.py File

Lecture 480 PART 1: AWS Deployment

Lecture 481 PART 1.1: AWS Deployment

Lecture 482 PART 2: AWS Deployment

Section 94: CLOUD Deployment: Microsoft Azure

Lecture 483 Lecture resources

Lecture 484 Azure Cloud Deployment

Section 95: PROJECTS SESSION: MACHINE LEARNING

Lecture 485 Overview

Section 96: ML PROJECTS: Building a Netflix Recommendation System

Lecture 486 Project files

Lecture 487 Building a Netflix Recommendation System

Lecture 488 Data Preparation (PART 1)

Lecture 489 Data Preparation (PART 2)

Lecture 490 Data Preparation (PART 3&4)

Lecture 491 Data Preparation (PART 5)

Lecture 492 Main.py (PART 1)

Lecture 493 Main.py (PART 2)

Lecture 494 Preparing HTML Files 1

Lecture 495 Preparing HTML Files 2

Lecture 496 Final Heroku Cloud Deployment

Lecture 497 Optional: How to Fix Errors when deploying

Section 97: ML PROJECTS: Building CRUD App

Lecture 498 project files

Lecture 499 CRUD Project Overview

Lecture 500 Building CRUD App

Section 98: ML PROJECT: Building Covid-19 Report Dashboard for Berlin City

Lecture 501 Project files

Lecture 502 Project Overview: Building Covid-19 Report Dashboard App for Berlin City

Lecture 503 Building a Covid Dashboard App for Berlin City

Section 99: ML PROJECTS: Building IPL Score Predictor App

Lecture 504 ML Project: Building IPL Score Predictor App

Lecture 505 Dataset Overview

Lecture 506 Exploratory Data Analysis

Lecture 507 Dealing With Categorical Values

Lecture 508 Model Building

Lecture 509 App.py

Lecture 510 Index.html and style.css

Section 100: ML PROJECTS: BigMart Sales Prediction

Lecture 511 Introduction

Lecture 512 Exploratory Data Analysis

Lecture 513 Feature Engineering

Lecture 514 Model Building

Section 101: ML PROJECTS: Predicting Compressive Strength

Lecture 515 Overview

Lecture 516 Exploratory Data Analysis

Lecture 517 Feature Engineering

Lecture 518 ML Model Building

Section 102: ML PROJECTS: Building a Sales Forcast App

Lecture 519 Project files

Lecture 520 Building A Sales Forecast App

Lecture 521 Exploratory Data Analysis

Lecture 522 Feature Creation

Lecture 523 Feature Correlation and Multicolinearity

Lecture 524 Dealing with Outliers

Lecture 525 Building the ML Model

Lecture 526 Deploy with Flask

Section 103: ML PROJECTS: Building A Breast Cancer Predictor App

Lecture 527 Project resources

Lecture 528 ML Project: Building A Breast Cancer Predictor App

Lecture 529 Dataset Overview

Lecture 530 Exploratory Data Analysis

Lecture 531 EDA With Visualization

Lecture 532 Building ML Model

Lecture 533 Walkthrough Of App.py

Lecture 534 Walkthrough Of Index.html and Static files

Section 104: SCIENTIFIC RESEARCH PAPER

Lecture 535 Lecture resources

Lecture 536 Reading Scientific Paper: An Overview

Lecture 537 What you will learn

Lecture 538 What is a Scientific Research Paper?

Lecture 539 Importance of Reading Research Papers

Lecture 540 Components of a Research Paper

Lecture 541 How to Read Scientific Research Papers

Lecture 542 Where to find Data Science research papers

Lecture 543 Assignment

Section 105: ARTIFICIAL INTELLIGENCE

Lecture 544 Lecture resources

Lecture 545 Artificial Intelligence: An Introduction

Lecture 546 The Big Picture of AI

Section 106: DEEP LEARNING

Lecture 547 Introduction To Deep Learning

Lecture 548 What you will learn

Lecture 549 What is Artificial Neural Network?

Lecture 550 Neurons and Perceptrons

Lecture 551 Machine Learning vs Deep Learning

Lecture 552 Why Deep Learning

Lecture 553 Applications of Deep Learning

Section 107: Artificial Neural Network

Lecture 554 Neural Network: An Overview

Lecture 555 Architecture: Components of the Perceptron

Lecture 556 Fully Connected Neural Network

Lecture 557 Types of Neural Networks

Lecture 558 How Neural Networks work

Lecture 559 Propagation: Forward and Back Propagation

Lecture 560 Understanding Neural Network

Lecture 561 Hands-on of Forward and Back Propagation (PART 1)

Lecture 562 Hands-on of Forward and Back Propagation (PART 2)

Lecture 563 Chain Rule in Backpropagation

Lecture 564 Optimizers In NN

Section 108: Activation Functions

Lecture 565 Activation Functions: An Introduction

Lecture 566 Sigmoid Activation Function

Lecture 567 Vanishing Gradient

Lecture 568 TanH Activation Function

Lecture 569 ReLU Activation Function

Lecture 570 Leaky ReLU Activation Function

Lecture 571 ELU Activation Function

Lecture 572 SoftMax Activation Function

Lecture 573 Activation functions summary

Section 109: Tensorflow and Keras

Lecture 574 Overview

Lecture 575 Introduction to Tensorflow

Lecture 576 Tensors and Dataflows in Tensorflow

Lecture 577 Tensorflow Versions

Lecture 578 Keras

Section 110: LAB SESSION: Deep Learning(ANN)

Lecture 579 Lecture resources

Lecture 580 LAB SESSION : Building your first Neural Network

Lecture 581 LAB SESSION : Building your Second Neural Network

Lecture 582 Handling Overfitting in Neural Network

Lecture 583 L2 Regularisation

Lecture 584 Dropout for Overfitting in Neural Network

Lecture 585 Early Stopping for overfitting in NN

Lecture 586 ModelCheck pointing

Lecture 587 Load best weight

Lecture 588 Tensorflow Playground

Lecture 589 Building Your Third Neural Network with MNIST

Section 111: FULL COMPUTER VISION COURSE

Lecture 590 Lecture resources

Section 112: COMPUTER VISION (CV): Beginner Level

Lecture 591 lecture resources

Lecture 592 Working with Images

Lecture 593 The concept of Pixels

Lecture 594 Gray-Scale Image

Lecture 595 Color Image

Lecture 596 Different Image formats

Lecture 597 Image Transformation: Filtering

Lecture 598 Affine and Projective Transformation

Lecture 599 Image Feature Extraction

Lecture 600 LAB SESSION: working with images

Lecture 601 LAB SESSION 2: Working with Images

Section 113: CPU vs GPU vs TPU

Lecture 602 Introduction to CPUs, GPUs and TPUs

Lecture 603 Accessing GPUs for Deep Learning

Lecture 604 CPU vs GPU speed

Section 114: COMPUTER VISION: Intermediate Level

Lecture 605 Lecture resources

Lecture 606 Introduction to Convolutional Neural Networks(CNN)

Lecture 607 Understanding Convolution (PART 1)

Lecture 608 Understanding Convolution (PART 2)

Lecture 609 Convolution Operation

Lecture 610 Understanding : Filter/Kernel | Feature Map | Input Volume | Receptive Field

Lecture 611 Filter vs Kernel

Lecture 612 Stride and Step Size

Lecture 613 Padding

Lecture 614 Pooling

Lecture 615 Understanding CNN Architecture

Lecture 616 LAB SESSION: CNN Lab 1

Lecture 617 LAB SESSION: CNN Lab 2

Section 115: COMPUTER VISION: Advanced Level

Lecture 618 Overview

Lecture 619 Lecture resources

Section 116: CNN Architectures

Lecture 620 State-of-the-Art CNN architecture

Lecture 621 LeNet Architecture

Lecture 622 LAB SESSION: LeNet LAB

Lecture 623 AlexNet Architecture

Lecture 624 LAB SESSION: AlexNet LAB

Lecture 625 VGG Architecture and LAB

Lecture 626 GoogleNet or Inception Net

Section 117: Transfer Learning

Lecture 627 Understanding Transfer Learning

Lecture 628 Steps to perform transfer learning

Lecture 629 When to use Transfer learning and when NOT to use.

Lecture 630 LAB SESSION: Transfer Learning with VGG-16

Section 118: Object Detection

Lecture 631 Overview and Agenda

Lecture 632 Computer Vision Task

Lecture 633 Datasets Powering Object Detection

Lecture 634 Image Classification vs Image Localisation

Lecture 635 Challenges of Object Detection

Section 119: Performance Metrics for Object Detection

Lecture 636 Intersection Over Union(IoU)

Lecture 637 Precision and Recall

Lecture 638 Mean Average Precision(mAP)

Section 120: Objection Detection Techniques

Lecture 639 Lecture resources

Lecture 640 Overview

Lecture 641 Brute Force Approach

Lecture 642 Sliding Window

Lecture 643 Region Proposal

Lecture 644 R-CNN

Lecture 645 Fast R-CNN

Lecture 646 ROI Pooling

Lecture 647 Faster R-CNN

Lecture 648 State-of-the-Art Algorithms

Lecture 649 YOLO

Lecture 650 LAB SESSION 1: YOLO LAB Overview

Lecture 651 LAB SESSION 2: YOLO

Lecture 652 LAB SESSION 3: YOLO

Lecture 653 SSD

Section 121: OPENCV FULL TUTORIAL

Lecture 654 Introduction To OpenCV

Lecture 655 Opencv Installation

Lecture 656 Opencv Setup

Lecture 657 Reading Images

Lecture 658 Reading Video

Lecture 659 Stacking Images together

Lecture 660 OpenCV Join

Lecture 661 IMAGE: Face Detection with OpenCV

Lecture 662 VIDEO: Face Detection with OpenCV

Lecture 663 Live Streaming with OpenCV

Lecture 664 OpenCV Functions

Lecture 665 Image Detection Techniques

Lecture 666 Edge Detection

Lecture 667 Dilation and Erode

Lecture 668 OpenCV Conventions

Lecture 669 Adding Shapes

Lecture 670 Creating Lines

Lecture 671 Creating Shapes(Rectangle)

Lecture 672 Creating Shapes(Circle)

Lecture 673 Warp Perspective

Lecture 674 Adding Text

Section 122: PROJECTS: COMPUTER VISION PROJECTS

Lecture 675 Overview

Section 123: CV PROJECT: Car Parking Space Counter Using OpenCV

Lecture 676 Car Park Counter with OpenCV: Project Overview

Lecture 677 PART 1: Building Car Park Counter With OpenCV

Lecture 678 PART 2: Building Car Park Counter With OpenCV

Section 124: CV PROJECT(Kaggle): Fruit and Vegetable Classification

Lecture 679 Lecture resources

Lecture 680 PROJECT: Fruit and Vegetable Classification Overview

Lecture 681 Setup your First Kaggle Code Notebook

Lecture 682 Building Fruit and Vegetable Classifier with Kaggle Notebooks

Lecture 683 Deploy a Computer Vision Classifier App

Section 125: CV PROJECT: Predicting Lung Disease with Computer Vision

Lecture 684 Predicting Lung Disease

Section 126: CV PROJECT: Nose Mask Detection with Computer Vision

Lecture 685 Project files

Lecture 686 Data Preprocessing

Lecture 687 Training the CNN

Lecture 688 Detecting Face Mask

Section 127: CV PROJECT: Pose Detection

Lecture 689 Building a Pose Detector

Lecture 690 LAB: Building a Pose Detector

Section 128: CV PROJECT: Building a Face Detector with Computer vision

Lecture 691 Building a Face Detector with AI

Lecture 692 LAB: Building a Face Detector

Section 129: CV PROJECT: Building a virtual AI Keyboard

Lecture 693 CV Project : Building AI Virtual Keyboard

Lecture 694 Building AI Virtual Keyboard (PART 1)

Lecture 695 Building AI Virtual Keyboard (PART 2)

Lecture 696 Building AI Virtual Keyboard (PART 3)

Lecture 697 Building AI Virtual Keyboard (PART 4)

Lecture 698 Building AI Virtual Keyboard (PART 5)

Section 130: CV PROJECT: Yolov4 Object Detection Using Webcam

Lecture 699 Yolov4 Object Detection Using Webcam

Section 131: NATURAL LANGUAGE PROCESSING(NLP)

Lecture 700 Lecture resources

Lecture 701 Overview

Lecture 702 Recapitulation

Lecture 703 What is NLP?

Lecture 704 Applications of NLP

Lecture 705 The Must-Know NLP Terminologies

Lecture 706 Word

Lecture 707 Tokens and Tokenizations

Lecture 708 Corpus

Lecture 709 Sentence and Document

Lecture 710 Vocabulary

Lecture 711 Stopwords

Section 132: Hands-On NLP: Text Pre-processing

Lecture 712 Tokenization with NLTK , SpaCy and Gensim

Lecture 713 Removing Stopwords with NLP Libraries

Section 133: Text Pre-processing: Normalization

Lecture 714 Text Normalization

Lecture 715 Stemming and Lemmatization

Lecture 716 LAB SESSION: Stemming and Lemmatization

Section 134: Part Of Speech (POS) Tagging

Lecture 717 Lecture resources

Lecture 718 Understanding POS Tagging

Lecture 719 LAB SESSION: Part of Speech Tagging

Lecture 720 Chunking

Section 135: Hands-On Text Pre-processing

Lecture 721 Advanced Text Preprocessing

Lecture 722 Frequency of Words | Bi-Gram | N-Grams

Lecture 723 More on Stemming and Lemmatization

Section 136: Introduction To Statistical NLP Techniques

Lecture 724 Bag of Words (BoW)

Lecture 725 TF-IDF

Section 137: Language Modelling

Lecture 726 Understanding language modelling

Section 138: INTERMEDIATE LEVEL: Word Embeddings

Lecture 727 Understanding Word Embeddings

Lecture 728 Feature Representations

Section 139: Word2Vec

Lecture 729 The Challenge with BoW and TF-IDF

Lecture 730 Understanding Word2Vec

Lecture 731 LAB SESSION: Word2Vec

Lecture 732 CBOW and Skip-Gram

Section 140: GloVe

Lecture 733 Understanding GloVe

Section 141: Sentence Parsing

Lecture 734 Sentence Parsing

Lecture 735 Chunking & Chinking & Syntax Tree

Section 142: Sequential Models

Lecture 736 Sequential Model: An Introduction

Lecture 737 Traditional ML vs Sequential Modelling

Section 143: ADVANCED LEVEL: Recurrent Neural Network (RNN)

Lecture 738 What is a Recurrent Neural Network (RNN) ?

Lecture 739 Types of RNNs

Lecture 740 Use Cases of RNNs

Lecture 741 Vanilla Neural Network (NN) vs Recurrent Neural Network (RNN)

Lecture 742 Backpropagation Through Time (BTT)

Lecture 743 Mathematics Behind BTT

Lecture 744 Vanishing and Exploding Gradient

Lecture 745 The problem of Long Term Dependencies

Lecture 746 Bidirectional RNN (BRNN)

Lecture 747 Gated Recurrent Unit(GRU)

Section 144: LSTM

Lecture 748 Lecture resources

Lecture 749 LSTM: An Introduction

Lecture 750 The LSTM Architecture

Lecture 751 LAB SESSION 1: LSTM

Lecture 752 LAB SESSION 2: Tween Sentiment Analysis using RNN

Lecture 753 LAB SESSION 3: Tween Sentiment Analysis using LSTM

Section 145: Sequence To Sequence Models (Seq2Seq)

Lecture 754 Sequence To Sequence models: An introduction

Lecture 755 Encoder & Decoder

Lecture 756 LAB SESSION: Language Translation

Lecture 757 LAB SESSION 2: Language Translation

Section 146: NLP PROJECT: Sentiment Analyzer

Lecture 758 Project files

Lecture 759 Building Sentiment Analyzer App

Lecture 760 LAB: Building Sentiment Analyzer App

Section 147: Name Entity Recognition (NER)

Lecture 761 Lecture Resources

Lecture 762 NER : An Introduction

Lecture 763 Example of Name Entity Recognition

Lecture 764 How Name Entity Recognition works

Lecture 765 Applications of NER

Lecture 766 LAB SESSION: Hands-On Name Entity Recognition

Lecture 767 LAB SESSION 2: Name Entity Recognition

Lecture 768 LAB SESSION: Visualizing Name Entity Recognition

Lecture 769 Assignment

Section 148: NLP PROJECT: Building a Name Entity Recognition App

Lecture 770 Project: Building a Name Entity Recognition Web App

Lecture 771 Project: Building your NER web App

Section 149: NLP PROJECT: AI Resume Analyzer App

Lecture 772 Project files

Lecture 773 NLP Project: Building AI Resume Analyzer

Lecture 774 Project: AI Resume Analyzer

Section 150: Microsoft Power BI

Lecture 775 Lecture resources

Lecture 776 Power BI: An Introduction

Lecture 777 Installation

Lecture 778 Query Editor Overview

Lecture 779 Connectors and Get Data Into Power BI

Lecture 780 Clean up Messy Data (PART 1)

Lecture 781 Clean up Messy Data (PART 2)

Lecture 782 Clean up Messy Data (PART 3)

Lecture 783 Creating Relationships

Lecture 784 Explore Data Using Visuals

Lecture 785 Analyzing Multiple Data Tables Together

Lecture 786 Writing DAX Measure (Implicit vs. Explicit Measures)

Lecture 787 Calculated Column

Lecture 788 Measure vs. Calculated Column

Lecture 789 Hybrid Measures

Lecture 790 The 80/20 Rule

Lecture 791 Text, Image, Cards, Shape

Lecture 792 Conditional Formatting

Lecture 793 Line Chart, Bar Chart

Lecture 794 Top 10 Products/Customers

Section 151: GUIDE TO HACKATHONS AND VIRTUAL INTERNSHIP

Lecture 795 Hackathons

Lecture 796 Guide to Virtual Internship

This course is for beginners who want to start a career in Data Science,Anyone who is interested to become a Full Stack Data Scientist,Any student who want to enter the field of Data Science after college,Any graduate who finds it difficult to find job in other IT field and will like to upskill in Data Science to secure a job,Any employee or worker looking for a career change,Anyone interested in the field of Artificial Intelligence,Anyone interested in the field of Computer Vision,Anyone interested in the field of Natural Language Processing,Anyone enrolled in other course and finding it difficult to understand the concepts,Anyone who wants to really dive deep into understanding the concepts and master it,Anyone who wants to secure a job in the field of Data Science, AI and Machine Learning,Anyone interested in building AI and Data Science products

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