Srbija Posted August 1, 2022 Share #1 Posted August 1, 2022 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 Homepage Hidden Content Give reaction to this post to see the hidden content. 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