The Full Stack Data Scientist Bootcamp®
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 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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