The Full Stack Data Scientist BootCamp®

Total Data Science

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• WEEK 1 :: DATA SCIENCE OVERVIEW 2:09:19

  • 1: Sample Project Preview 43:37
  • 2: Course Curriculum Preview 24:18
  • 3: Lecture resources 01:00
  • 4: The Big Picture Preview 03:22
  • 5: Part 1: Data Science Overview 04:56
  • 6: What is Data Science? 07:43
  • 7: DA vs DS vs AI vs ML 04:49
  • 8: Industries That Use and Hire Data Scientist 03:32
  • 9: Applicatins of Data Science 08:41
  • 10: Data Science Lifecycle and the Maturity Framework 03:46
  • 11: Who is a Data Scientist? 06:25
  • 12: Career Opportunities In Data Science 03:28
  • 13: Typical Backgrounds of Data Scientists 01:54
  • 14: The Ultimate Path To become a Data Scientist(Skills you need to develop) 07:25
  • 15: Typical Salary of a Data Scientist 04:23

• COMPLETE SQL FOR DATA SCIENCE COURSE 01:00

  • 16: Overview 01:00

• SQL : BEGINNER LEVEL 44:48

  • 17: Introduction To SQL for Data Science 06:06
  • 18: Types of Databases 04:19
  • 19: What is a Query? 03:01
  • 20: What is SQL? 03:18
  • 21: SQL or SEQUEL? 02:59
  • 22: SQL Installation 01:36
  • 23: SQL Installation for Mac 04:53
  • 24: SQL Installation for Windows 04:01
  • 25: Extra Help in Installing SQL 00:46
  • 26: Overview of SQL workbench 13:49

• SQL COMMANDS 04:18

  • 27: Introduction To SQL Commands 03:02
  • 28: SQL CRUD Commands 01:16

• UNDERSTANDING AND CREATING SQL DATABASES 06:50

  • 29: SQL Schema 01:52
  • 30: Inserting Comments in SQL 01:22
  • 31: Creating Database 03:36

• UNDERSTANDING AND CREATING SQL TABLES 03:14

  • 32: Overview of SQL Table 03:14

• TYPES OF SQL KEYS 11:53

  • 33: KEYS in SQL 01:37
  • 34: Primary Key 03:14
  • 35: Foreign Key 02:43
  • 36: Composite Key 01:11
  • 37: Super Key 01:52
  • 38: Alternate Key 01:16

• DATA TYPES IN SQL 07:38

  • 39: SQL Data Types 07:38

• CREATE TABLE AND INSERT DATA INTO TABLE 19:36

  • 40: Create table in SQL 09:59
  • 41: INSERT Data into Tables 09:37

• SQL CONSTRAINTS 42:19

  • 42: Understanding SQL Constraints 06:00
  • 43: NOT NULL & UNIQUE Constraints 13:20
  • 44: DEFAULT Constraint 03:25
  • 45: PRIMAY KEY Constraint 04:15
  • 46: Alter SQL Constraint 03:22
  • 47: Adding and Dropping SQL Constraint 05:42
  • 48: Foreign Key Constraint 06:15

• WEEK 2 :: SQL INTERMEDIATE LEVEL 2:18:05

  • 49: Creating Existing Databases 06:15
  • 50: Overview Of Existing Databases 05:12
  • 51: The SELECT Clause in Details 09:52
  • 52: The ORDER BY Statement 02:01
  • 53: The WHERE Clause 04:46
  • 54: Operation with SELECT statement 07:10
  • 55: Aliasing in SQL 09:03
  • 56: Exercise 1 and Solution 05:28
  • 57: The DISTINCT Keyword 03:28
  • 58: WHERE Clause with SQL Comparison operators 07:15
  • 59: Exercise 2 and Solution 03:17
  • 60: The AND, OR and NOT Operators 11:48
  • 61: Exercise 3 and Solution 05:35
  • 62: The IN Operator 03:02
  • 63: Exercise 4 and Solution 02:18
  • 64: The BETWEEN Operator 02:41
  • 65: Exercise 5 and Solution 03:16
  • 66: The LIKE Operator 07:59
  • 67: Exercise 6 and Solution 03:51
  • 68: The REGEXP Operator 10:31
  • 69: Exercise 7 and Solution 07:21
  • 70: IS NULL & IS NOT NULL Operator 03:12
  • 71: Exercise 8 and Solution 02:43
  • 72: The ORDER BY Clause in Details 04:37
  • 73: The LIMIT Clause 02:31
  • 74: Exercise 9 and Solution 02:53

• SQL JOINS 1:11:14

  • 75: Introduction To SQL JOINS 08:59
  • 76: Exercise 10 and Solution 07:23
  • 77: Joining Across Multiple Databases 06:51
  • 78: Exercise 11 and Solution 08:27
  • 79: Joining Table to Itself 07:56
  • 80: Joining Across Multiple SQL Tables 11:16
  • 81: LEFT and RIGHT JOIN 06:32
  • 82: Exercise 12 and Solution 06:26
  • 83: Exercise 13 and Solution 07:24

• WORKING WITH EXISTING SQL TABLE 24:45

  • 84: INSERTING Multiple Data Into Existing Table 03:12
  • 85: Creating A Copy of a Table 05:51
  • 86: Updating Existing Table 10:21
  • 87: Updating Multiple Records In Existing Table 05:21

• SQL VIEW 17:29

  • 88: Create SQL VIEW 1 06:59
  • 89: Using SQL VIEW 1 04:40
  • 90: Alter SQL VIEW 04:13
  • 91: Drop SQL View 01:37

• SQL DATA SUMMARIZATION: AGGREGATION FUNCTIONS 08:25

  • 92: COUNT () Function 03:24
  • 93: SUM() Function 01:29
  • 94: AVG() Function 01:12
  • 95: SQL Combined Functions 02:20

• ADVANCE SQL FUNCTIONS 39:15

  • 96: Count Function in Details 05:15
  • 97: The HAVING() Function 03:50
  • 98: LENGTH() Function 06:35
  • 99: CONCAT() Function 06:04
  • 100: INSERT() Function 08:33
  • 101: LOCATE() Function 05:22
  • 102: UCASE() & LCASE() Function 03:36

• WEEK 3 :: SQL ADVANCED LEVEL 01:00

  • 103: Overview 01:00

• SQL STORED PROCEDURE 21:43

  • 104: Create a Stored Procedure 05:46
  • 105: Stored Procedure with Single Parameter 05:48
  • 106: Stored Procedure with Multiple Parameter 05:20
  • 107: Alter Stored Procedure 03:18
  • 108: Drop Stored Procedure 01:31

• TRIGGERS 25:24

  • 109: Introduction to Triggers 05:38
  • 110: BEFORE Insert Triggers 07:05
  • 111: AFTER Insert Triggers 10:59
  • 112: DROP Triggers 01:42

• TRANSACTION 17:07

  • 113: Creating Transactions 07:12
  • 114: Rollback Transactions 04:05
  • 115: Savepoint Transactions 05:50

• RECOMMENDATION 01:00

  • 116: Recommendation 01:00

• FULL PYTHON FOR DATA SCIENCE COURSE 53:51

  • 117: WEEK 4 :: BEGINNER : PYTHON FOR DATA SCIENCE 01:00
  • 118: Python Course Curriculum 06:20
  • 119: Install and Write Your First Python Code 14:19
  • 120: Introduction To Jupyter Notebook & Jupyter Lab 15:40
  • 121: Working with Code Vs Markdown 15:32
  • 122: Introduction To Google Colab 01:00

• Datasets 01:00

  • 123: Download datasets 01:00

• Hands-On With Python 42:09

  • 124: Lecture resources 01:00
  • 125: Python Hands-On: Introduction 01:00
  • 126: Hands-On With Python: Keywords And Identifiers 12:53
  • 127: Hands-On Coding: Python Comments 07:09
  • 128: Hands-On Coding: Python Docstring 03:25
  • 129: Hands-On Coding: Python Variables 09:03
  • 130: Hands-On Coding: Rules and Naming Conventions for Python Variables 07:39

• PYTHON OUTPUT(), INPUT() AND IMPORT() FUNCTIONS 15:17

  • 131: Hands-On Coding: Output() Function In Python 02:29
  • 132: Hands-On Coding: Input() Function In Python 07:56
  • 133: Hands-On Coding: Import() Function In Python 04:52

• PYTHON OPERATORS 27:59

  • 134: Hands-On Coding: Arithmetic Operators 02:12
  • 135: Hands-On Coding: Comparison Operators 01:53
  • 136: Hands-On Coding: Logical Operators 07:38
  • 137: Hands-On Coding: Bitwise Operators 07:51
  • 138: Hands-On Coding: Assignment Operators 03:34
  • 139: Python Hands-On: Special Operators 02:00
  • 140: Hands-On Coding: Membership Operators 02:51

• PYTHON FLOW CONTROL 28:20

  • 141: If Statement 04:20
  • 142: If...Else Statement 02:11
  • 143: ELif Statement 06:02
  • 144: For loop 03:13
  • 145: While loop 05:16
  • 146: Break Statement 03:22
  • 147: Continue Statement 03:56

• WEEK 2: PYTHON FUNCTIONS 35:34

  • 148: User Define Functions 12:46
  • 149: Arbitrary Arguments 05:13
  • 150: Function With Loops 02:12
  • 151: Lambda Function 08:43
  • 152: Built-In Function 06:40

• PYTHON GLOBAL AND LOCAL VARIABLES 06:26

  • 153: Global Variable 02:00
  • 154: Local Variable 04:26

• WORKING WITH FILES IN PYTHON 19:09

  • 155: Python Files 07:58
  • 156: The Close Method 01:19
  • 157: The With Statement 02:30
  • 158: Writing To A File In Python 07:22

• PYTHON MODULES 10:00

  • 159: module 1 06:23
  • 160: rename 01:28
  • 161: module last 02:09

• PYTHON PACKAGES AND LIBRARIES 11:29

  • 162: Python Packages and Libraries 04:54
  • 163: PIP Install Python Libraries 06:35

• DATA TYPES IN PYTHON 27:29

  • 164: Lecture resources 01:00
  • 165: Lesson 1: Integer & Floating Point Numbers 03:46
  • 166: Lesson 2: Complex Numbers & Strings 03:50
  • 167: Lesson 3: LIST 02:38
  • 168: Lesson 4: Tuple & List Mutability 04:59
  • 169: Lesson 5: Tuple Immutability 03:25
  • 170: Lesson 6: Set 02:53
  • 171: Lesson 7: Dictionary 04:58

• EXTRA CONTENT 43:00

  • 172: LIST 09:45
  • 173: Working On List 07:09
  • 174: Splitting Function 10:44
  • 175: Range In Python 09:06
  • 176: List Comprehension In Python 06:16

• WEEK 3: NUMPY 1:21:31

  • 177: Lecture resources 01:00
  • 178: Introduction To Numpy 11:30
  • 179: Numpy: Creating Multi-Dimensional Arrays 01:53
  • 180: Numpy: Arange Function 05:53
  • 181: Numpy: Zeros, Ones and Eye functions 04:46
  • 182: Numpy: Reshape Function 01:23
  • 183: Numpy: Linspace 02:23
  • 184: Numpy: Resize Function 05:23
  • 185: Numpy: Generating Random Values With random.rand 03:04
  • 186: Numpy: Generating Random Values With random.randn 02:26
  • 187: Numpy: Generating Random Values With random.randint 03:40
  • 188: Numpy: Indexing & Slicing 17:00
  • 189: Numpy: Broadcasting 01:17
  • 190: Numpy: How To Create A Copy Dataset 04:28
  • 191: Numpy- DataFrame Introduction 15:25

• Numpy assignment 01:00

  • 192: Numpy Assignment 01:00

• WEEK 4: PANDAS 2:35:39

  • 193: Lecture resources 01:00
  • 194: Pandas- Series 1 19:21
  • 195: Pandas- Series 2 11:05
  • 196: Pandas- Loc & iLoc 07:48
  • 197: Pandas- DataFrame Introduction 04:17
  • 198: Pandas- Operations On Pandas DataFrame 09:10
  • 199: Pandas- Selection And Indexing On Pandas DataFrame 03:12
  • 200: Pandas- Reading A Dataset Into Pandas DataFrame 08:32
  • 201: Pandas- Adding A Column To Pandas DataFrame 04:33
  • 202: Pandas- How To Drop Columns And Rows In Pandas DataFrame 11:03
  • 203: Pandas- How To Reset Index In Pandas Dataframe 03:32
  • 204: Pandas- How To Rename A Column In Pandas Dataframe 06:29
  • 205: Pandas- Tail(), Column and Index 02:56
  • 206: Pandas- How To Check For Missing Values or Null Values(isnull() Vs Isna()) 06:16
  • 207: Pandas- Pandas Describe Function 05:40
  • 208: Pandas- Conditional Selection With Pandas 09:14
  • 209: Pandas- How To Deal With Null Values 07:14
  • 210: Pandas- How To Sort Values In Pandas 03:10
  • 211: Pandas- Pandas Groupby 00:37
  • 212: Pandas- Count() & Value_Count() 02:14
  • 213: Pandas- Concatenate Function 06:47
  • 214: Pandas- Join & Merge(Creating Dataset) 03:45
  • 215: Pandas-Join 09:49
  • 216: Pandas- Merge 07:55

• DATA VISUALISATION: MATPLOTIIB AND SEABORN 1:42:52

  • 217: Lecture resources 01:00
  • 218: Matplotlib | Subplots 29:40
  • 219: Seborn | Scatterplot | Correlation | Boxplot | Heatmap 43:56
  • 220: Univariate | Bivariate | Multivariate Data Visualisation 28:16

• PROJECT: PYTHON ASSIGNMENT 01:00

  • 221: Assignment 01:00

• PYTHON PROJECTS 2:42:15

  • 222: PROJECT 1: Analyse The Top Movie Streaming |NETFLIX | Amazon Prime |Hulu |Disney 1:57:59
  • 223: PROJECT 2: Analysis of UBER Data 44:16

• 2ND MONTH 01:00

  • 224: Overview 01:00

• FULL STATISTICS FOR DATA SCIENCE 13:39

  • 225: Lecture resources 01:00
  • 226: Why Statistics Is Important For Data Science 09:59
  • 227: How Much Maths Do I Need To Know? 02:40

• STATISTICAL METHODS DEEP DIVE 15:28

  • 228: Statistical Methods Deep Dive 06:30
  • 229: Types of Statistics 03:42
  • 230: Common Statistical Concepts 05:16

• Data 18:38

  • 231: What Is Data? 01:57
  • 232: Data Types 12:01
  • 233: Data Attributes and Data Sources 02:40
  • 234: Structured vs. Unstructured Data 01:00
  • 235: Recommended stats lecture 01:00

• FREQUENCY DISTRIBUTION 15:02

  • 236: Frequency Distribution 15:02

• CENTRAL TENDENCY 18:58

  • 237: Mean, Median, Mode 16:07
  • 238: Central Tendency 02:51

• MEASURES OF DISPERSION 12:00

  • 239: Measure Of Dispersion 02:04
  • 240: Variance and Standard Deviation In Python 02:00
  • 241: Example of Variance and Standard Deviation 05:56
  • 242: Variance and Standard Deviation In Python 02:00

• COEFFICIENT OF VARIATIONS 05:46

  • 243: Coefficient of Variations 05:46

• THE FIVE NUMBER SUMMARY & THE QUARTILES 14:27

  • 244: The Quartiles 09:42
  • 245: The FIVE(5) Number Summary 04:45

• THE NORMAL DISTRIBUTION 28:44

  • 246: Introduction To Normal Distribution 09:51
  • 247: Skewed Distributions 07:58
  • 248: Central Limit Theorem 10:55

• CORRELATION 14:51

  • 249: Introduction to Correlation 11:41
  • 250: Scatterplot For Correlation 01:53
  • 251: Correlation is NOT Causation 01:17

• 3RD MONTH 01:00

  • 252: Overview 01:00

• WEEK 1 :: PROBABILITY 39:59

  • 253: Why Probability In Data Science_ 06:19
  • 254: Probability Key Concepts (1) 10:28
  • 255: Mutually Exclusive (1) 03:45
  • 256: Independent Events 06:13
  • 257: Rules For Computing Probability 13:14

• BAYE'S THEOREM 01:06

  • 258: Baye-s Theorem Overview 01:06

• HYPOTHESIS TESTING 26:18

  • 259: Introduction To Hypothesis 03:50
  • 260: Null Vs Alternative Hypothesis 01:34
  • 261: Setting Up Null and Alternative Hypothesis 00:37
  • 262: One-tailed Vs Two-tailed test 01:40
  • 263: Key Points On Hypothesis Testing 03:08
  • 264: Type 1 Vs Type 2 errors 06:00
  • 265: Process Of Hypothesis testing 01:46
  • 266: P-Value 02:28
  • 267: Alpha-Value 03:15
  • 268: Confidence Level 02:00

• Stats Project 02:00

  • 269: Project Assignment 01:00
  • 270: Project Solution 01:00

• GITHUB FOR DATA SCIENCE 49:15

  • 271: Introduction to Github for Data Science (1) 01:38
  • 272: Setting up Github account for Data Science (1) 03:25
  • 273: Create Github Profile for Data Science 22:44
  • 274: Create Github Project Description for Data Science 21:28

• ARTIFICIAL INTELLIGENCE(AI) AND MACHINE LEARNING(ML) 01:00

  • 275: Overview 01:00

• WEEK 2 :: FULL MACHINE LEARNING COURSE 57:00

  • 276: Introduction To Machine Learning 01:33
  • 277: ML Curriculum export 11:19
  • 278: Practical Understanding Of Machine Learning 1 10:29
  • 279: Practical Understanding Of Machine Learning 2 03:43
  • 280: Applications of Machine Learning 09:07
  • 281: Machine Learning Life Cycle 20:49

• USE CASE 24:47

  • 282: The Microsoft Data Science USE CASE 19:43
  • 283: Setting Up Your Environment for Machine Learning 05:04

• MACHINE LEARNING ALGORITHMS 29:05

  • 284: How Machine Learning Algorithms Learn 09:17
  • 285: Supervised vs Unsupervised ML 16:30
  • 286: Dependent vs Independent Variables 03:18

• WORKING WITH MACHINE LEARNING DATA 1:13:57

  • 287: Considerations When Loading Data 06:22
  • 288: Loading Data from a CSV File 07:01
  • 289: Loading Data from a URL 01:22
  • 290: Loading Data from a Text File 03:50
  • 291: Loading Data from an Excel File 03:36
  • 292: Skipping Rows while Loading Data 02:51
  • 293: Peek at your Data 03:12
  • 294: Dimension of your Dataset 02:01
  • 295: Checking Data Types of your Dataset 03:53
  • 296: Discriptive Statistics of your Dataset 04:48
  • 297: Class Distribution of your Dataset 04:49
  • 298: Skewness of your Dataset 01:51
  • 299: Missing Values in your Dataset 02:10
  • 300: Histogram of Dataset 04:48
  • 301: Density Plot of Dataset 03:32
  • 302: Box and Whisker Plot 03:02
  • 303: Correlation Matrix 04:55
  • 304: Scatter Matrix(Pairplot) 03:30
  • 305: Correlation of your Dataset 06:24

• WEEK 3 :: SUPERVISED MACHINE LEARNING ALGORITHMS 45:32

  • 306: What is Regression? 07:32
  • 307: Introduction to Linear Regression 05:34
  • 308: Conceptual Understanding of Linear Regression 19:59
  • 309: Hyperplane 02:14
  • 310: MSE vs RMSE 10:13

• LAB SESSION: LINEAR REGRESSION 2:12:01

  • 311: Training Data vs Validation Data vs Testing Data 09:16
  • 312: Splitting Dataset into Training and Testing 20:46
  • 313: Linear Regression LAB 1 40:59
  • 314: Linear Regression LAB 2(PART 1) 34:31
  • 315: Linear Regression LAB 2 (PART 2) 26:29

• LOGISTIC REGRESSION ALGORITHM 2:32:39

  • 316: Regressor Algorithm Vs Classifier Algorithm 09:47
  • 317: Introduction To Logistic Regression Algorithm 04:01
  • 318: PART 2: Intuitive Understanding Of Logistic Regression 22:48
  • 319: Limitation of Linear Regression 19:45
  • 320: The Mathematics Behind Logistic Regression Algorithm 22:52
  • 321: LAB SESSION 1: Practical Implementation of Logistic Regression Algorithm 23:34
  • 322: LAB SESSION 2: Practical Implementation of Logistic Regression Algorithm 28:39
  • 323: LAB SESSION 3: Practical Implementation of Logistic Regression Algorithm 21:13

• NAIVE BAYES ALGORITHM 1:10:42

  • 324: Introduction to Naive Bayes Algorithm 04:43
  • 325: The Mathematics Behind Naive Bayes Algorithm 28:53
  • 326: LAB SESSION: Building Naive Bayes Model 37:06

• K-NEAREST NEIGBHOR ALGORITHM (KNN) 1:36:10

  • 327: K-Nearest Neighbor Classification-INTRODUCTION 13:31
  • 328: K-Nearest Neighbor Classification-Distance Measures 21:06
  • 329: K-Nearest Neighbor Classification-EDA 26:39
  • 330: K-Nearest Neighbor-LAB SESSION (Model) 17:09
  • 331: K-Nearest Neighbor Classification-Choosing K 17:45

• SUPPORT VECTOR MACHINE (SVM) ALGORITHM 1:06:28

  • 332: svm intro (1) 01:16
  • 333: Mathematics of SVM and Intuitive Understanding of SVM Algorithm (1) 24:25
  • 334: PART 2-Support Vector Machine (SVM)-Machine Learning (1) 11:53
  • 335: PART 3-Support Vector Machine (SVM)-Machine Learning (1) 09:44
  • 336: PART 4-Support Vector Machine (SVM)-Machine Learning (1) 19:10

• MACHINE LEARNING ALGORITHM PERFORMANCE METRICS 1:12:51

  • 337: lecture resources 01:00
  • 338: Overview 01:44
  • 339: Confusion Matrix: True Positive | False Positive | True Negative | False Neg. 23:50
  • 340: Accuracy 13:04
  • 341: Precision 05:28
  • 342: Recall 04:05
  • 343: The Tug of War between Precision and Recall 08:16
  • 344: F 1 Score 01:32
  • 345: Classification Report 03:24
  • 346: ROC and AUC 01:00
  • 347: LAB SESSION: AUC and ROC 09:28

• DECISION TREE ALGORITHM 1:08:31

  • 348: Lecture resources 01:00
  • 349: Decision Tree Overview 01:27
  • 350: CART: Introduction To Decision Tree 15:24
  • 351: Purity Metrics: Gini Impurity | Gini Index 15:57
  • 352: Calculating Gini Impurity (PART 1) 10:43
  • 353: Calculating Gini Impurity (PART 2) 07:20
  • 354: Information Gain 08:52
  • 355: Overfitting in Decision Trees 04:20
  • 356: Prunning 02:28
  • 357: Lab : Prunning 01:00

• WEEK 4 :: ENSEMBLE TECHNIQUES 4:31:04

  • 358: Lecture resources 01:00
  • 359: Lecture resources 01:00
  • 360: Introduction To Ensemble Techniques 08:24
  • 361: Understanding Ensemble Techniques 02:54
  • 362: Difference b/n Random Forest & Decision Tree 04:55
  • 363: Why Random Forest Algorithm 05:25
  • 364: More on Random Forest Algorithm 03:04
  • 365: Introduction to Bootstrap Sampling | Bagging 03:35
  • 366: Understanding Bootstrap Sampling 02:54
  • 367: Diving Deeper into Bootstrap Sampling 09:57
  • 368: Bootstrap Sampling summary 08:10
  • 369: Bagging 05:43
  • 370: Boosting 08:07
  • 371: Adaboost : Introduction 04:35
  • 372: The Maths behind Adaboost algorithm 21:16
  • 373: Gradient Boost: Introduction 04:33
  • 374: Gradient Boosting : An Intuitive Understanding 15:08
  • 375: The Mathematics behind Gradient Boosting Algorithm 33:15
  • 376: XGBoost: Introduction 01:56
  • 377: Maths of XGBoost (PART 1) 18:10
  • 378: Maths of XGBoost (PART 2) 18:48
  • 379: LAB SESSION 1: Ensemble Techniques 23:27
  • 380: LAB SESSION 2: Ensemble Techniques 39:49
  • 381: Stacking: An Introduction 09:24
  • 382: LAB SESSION: Stacking 15:35

• OVERFITTING AND UNDERFITTING 1:39:34

  • 383: overfit 1 27:26
  • 384: overfit 2 37:51
  • 385: overfit 3 09:08
  • 386: overfit 4 25:09

• BIAS VS VARIANCE 52:27

  • 387: Introduction 04:02
  • 388: Bias vs Variance 26:56
  • 389: The Bias Variance Tradeoff 12:21
  • 390: Summary 09:08

• PROJECT: SUPERVISED MACHINE LEARNING 02:00

  • 391: Project Assignment 01:00
  • 392: Project solution 01:00

• 4TH MONTH 01:00

  • 393: Overview 01:00

• UNSUPERVISED MACHINE LEARNING ALGORITHMS 01:00

  • 394: All Unsupervised ML Resources 01:00

• WEEK 3: K-MEANS CLUSTERING ALGORITHM 1:21:04

  • 395: What is K-Means Clustering? 14:32
  • 396: The Llyod's Method-Shifting the Centroids 09:09
  • 397: K-Means Algorithm-LAB SESSION 44:35
  • 398: Choosing K in Kmeans-The Elbow Method 12:48

• HIERARCHICAL CLUSTERING ALGORITHM 1:20:50

  • 399: Machine Learning-Hierarchical clustering 26:35
  • 400: Hierarchical Clustering-Dendrograms(Cophenetic correlation) 11:46
  • 401: Hierarchical Clustering-LAB 42:29

• PRINCIPAL COMPONENT ANALYSIS (PCA) 37:45

  • 402: PART 1: Understanding PCA 20:59
  • 403: PART 2: Understanding PCA 05:01
  • 404: PCA in Python 10:45
  • 405: PCA Further Read 01:00

• PROJECT: UNSUPERVISED MACHINE LEARNING 03:00

  • 406: Project assignment 02:00
  • 407: Project solution 01:00

• FEATURE ENGINEERING : MODEL SELECTION & OPTIMIZATION 2:38:17

  • 408: lecture resources 01:00
  • 409: KFold Cross Validation 25:15
  • 410: LAB SESSION: KFold Cross Validation 11:52
  • 411: Bootstrap Sampling 12:13
  • 412: Leave One Out Cross Validation(LOOCV) 04:49
  • 413: LAB SECTION: LOOCV 06:56
  • 414: Hyper-parameter Tuning: An Introduction 16:53
  • 415: Hyper-parameter Tuning: Continue 06:39
  • 416: GridSearchCV: An Introduction 39:48
  • 417: RandomSearchCV: An Introduction 02:31
  • 418: LAB SESSION 1: GridSearchCV 13:43
  • 419: LAB SESSION 2: GridSearchCV 14:38
  • 420: Regularization 01:00
  • 421: Lasso(L1) and Ridge (L2) Regression 01:00

• WEEK 2 :: ADVANCED FEATURE ENGINEERING 01:00

  • 422: Advanced Feature Engineering 01:00

• SAVING AND LOADING ML MODEL 01:00

  • 423: Introduction 01:00

• Web Scraping On Wikipedia 08:12

  • 424: lecture resources 01:00
  • 425: Introduction To Web Scraping Libraries 01:31
  • 426: Library- Requests 01:55
  • 427: Library- BeautifulSoup 01:00
  • 428: Library- Selenium 01:11
  • 429: Library- Scrapy 01:35

• PROJECT: WIKIPEDIA WEB SCRAPING 31:58

  • 430: lecture resources 01:00
  • 431: Web Scraping On Wikipedia 30:58

• PROJECT: ONLINE BOOK STORE WEB SCRAPPING 55:36

  • 432: Critical Analysis Of Web Pages 06:04
  • 433: PART 1- Examining And Scraping Individual Entities From Source Page 22:51
  • 434: PART 2- Examining And Scraping Individual Entities From Source Page 10:11
  • 435: Data Preprocessing On Scraped Data 16:30

• WEB SCRAPING: BUILDING AMAZON AUTO SCRAPER 44:30

  • 436: lecture resources 01:00
  • 437: Building Amazon Web Scraper 00:54
  • 438: Installation of Libraries & Analysis of Amazon.com 14:44
  • 439: Building Amazon Generic Auto Scraper 27:52

• WEEK 4 :: RECOMMENDATION SYSTEMS 2:08:56

  • 440: Recommendation System: An Overview 02:54
  • 441: Where Recommender Systems came from 02:49
  • 442: Applications of Recommendation Systems 06:25
  • 443: Why Recommender Systems? 09:27
  • 444: Types of Recommender Systems 00:49
  • 445: Popularity based Recommender Systems 04:42
  • 446: LAB SESSION: Popularity based Recommender 15:13
  • 447: Content-based Filtering: An Overview 07:48
  • 448: Cosine Similarity 12:04
  • 449: Cosine Similarity with Python 05:49
  • 450: Document Term Frequency Matrix 19:22
  • 451: LAB SESSION: Building Content-based Recommender Engine 34:05
  • 452: Collaborative Filtering: An Introduction 02:49
  • 453: Evaluation Metrics for Recommender Systems 04:40

• 4TH MONTH 01:00

  • 454: Overview 01:00

• HANDS-ON PROJECTS 01:00

  • 455: Overview 01:00

• BANK NOTE ANALYSIS 2:07:24

  • 456: Project resources 01:00
  • 457: Introduction 02:32
  • 458: Exploratory Data Analysis 20:40
  • 459: Data Preparation 46:32
  • 460: PART 1: Model Building 19:53
  • 461: PART 2: Model Building 36:47

• BIG MART SALES PREDICTION 1:51:41

  • 462: Introduction 02:56
  • 463: Dataset Overview 49:48
  • 464: Feature Engineering & Feature Transformation 44:36
  • 465: Model Building 14:21

• AMAZON.COM EMPLOYEE ACCESS CHALLENGE 1:17:28

  • 466: Introduction 02:12
  • 467: Exploratory Data Analysis 34:41
  • 468: Model Building 39:35
  • 469: project resources 01:00

• BREAST CANCER DETECTION USING SVM AND KNN : PART 1 1:36:18

  • 470: Introduction 02:59
  • 471: Dataset Summary 06:37
  • 472: Exploratory Data Analysis 45:21
  • 473: Building the Model 40:21
  • 474: lecture resources 01:00

• PREDICTING COMPRESSIVE STRENGTH OF CONCRETE 1:59:54

  • 475: Introduction 02:48
  • 476: Importing Dataset & Exploratory Data Analysis(EDA) 44:15
  • 477: Feature Engineering And Model Building 1 46:05
  • 478: Feature Engineering And Model Building 2 26:46

• STOCK MARKET CLUSTERING 54:49

  • 479: Introduction 02:30
  • 480: Live Data Extraction From Yahoo Finance 20:28
  • 481: Performing Clustering 31:51

• 5TH MONTH 01:00

  • 482: Overview 01:00

• CLOUD DEPLOYMENT 01:00

  • 483: Overview 01:00

• STREAMLIT FOR BUILDING MACHINE LEARNING APPS 4:38:04

  • 484: Demo 17:04
  • 485: PART 1: Introduction to STREAMLIT 21:27
  • 486: PART 2: Introduction to STREAMLIT 48:37
  • 487: PART 3: Introduction to STREAMLIT 24:42
  • 488: PART 1-Streamlit Build Your First Machine Learning Web App 54:45
  • 489: PART 2-Streamlit Build Your First Machine Learning Web App 38:42
  • 490: PART 3-Streamlit Build Your First Machine Learning Web App 20:06
  • 491: PART 4-Streamlit Build Your First Machine Learning Web App 52:41

• FLASK TUTORIAL 52:51

  • 492: Introduction 02:54
  • 493: Installation and Initializing Flask 16:24
  • 494: Linking html files 17:30
  • 495: Linking CSS files 16:03

• WEEK 1 ::END-TO-END MACHINE LEARNING WITH DEPLOYMENT : PREDICT RESTAURANT RATING 2:30:32

  • 496: Predict Restaurant Rating 03:09
  • 497: Dataset overview 07:51
  • 498: Exploratory Data Analysis (EDA) 47:03
  • 499: Model Building 23:00
  • 500: Key Flask Concepts & Application Development Interface (API) 11:23
  • 501: Creating Folders 23:49
  • 502: Creating Folder Contents 16:19
  • 503: Final Deployment 17:58

• CLOUD :: HEROKU MACHINE LEARNING CLOUD DEPLOYMENT 1:59:40

  • 504: Demo 05:42
  • 505: Introduction 02:34
  • 506: Dataset Preparation 16:16
  • 507: Feature Engineering 14:56
  • 508: 1 Model Building & Hyper-parameter tuning 20:10
  • 509: 2 Model Building & Hyper-parameter tuning 20:55
  • 510: 3 Model Building & Hyper-parameter tuning 28:30
  • 511: 4 Model Building & Hyper-parameter tuning 10:37

• CLOUD :: AMAZON WEB SERVICE (AWS) DEPLOYMENT 35:21

  • 512: AWS Introduction 02:47
  • 513: AWS Dataset Intro 02:13
  • 514: AWS Creating App.py File For Deployment 16:07
  • 515: PART 2 AWS Machine Learning Deployment 14:14

• CLOUD :: MICROSOFT AZURE DEPLOYMENT 23:09

  • 516: azure export 23:09

• WEEK 2 :: PROJECTS SESSION: MACHINE LEARNING 01:00

  • 517: Overview 01:00

• ML PROJECTS: BUILDING A NETFLIX RECOMMENDATION SYSTEM 2:48:40

  • 518: Building a Netflix Recommendation System 06:15
  • 519: Data Preparation (PART 1) 18:09
  • 520: Data Preparation (PART 2) 26:32
  • 521: 1 Data Preparation (PART 3&4) 14:53
  • 522: 1 Data Preparation (PART 3&4) Con't 12:17
  • 523: Data Preparation (PART 5) 16:08
  • 524: Main.py (PART 1.1) 05:43
  • 525: Main.py (PART 1.2) 13:59
  • 526: Main.py (PART 2) 12:31
  • 527: Preparing HTML Files 1.1 04:09
  • 528: Preparing HTML Files 1.2 07:02
  • 529: Preparing HTML Files 2.1 05:44
  • 530: Preparing HTML Files 2.2 12:53
  • 531: Final Heroku Cloud Deployment 09:00
  • 532: Optional: How to Fix Errors when deploying 03:25

• 5TH MONTH 01:00

  • 533: Overview 01:00

• ML PROJECTS: BUILDING CRUD APP 33:30

  • 534: CRUD Project Overview 05:04
  • 535: Building CRUD App 28:26

• ML PROJECT: BUILDING COVID-19 REPORT DASHBOARD FOR BERLIN CITY 46:26

  • 536: Covid dashboard overview 03:25
  • 537: Building Covid dashboard 43:01

• ML PROJECTS: BUILDING IPL SCORE PREDICTOR APP 1:01:05

  • 538: ML Project: Building IPL Score Predictor App 01:48
  • 539: Dataset Overview 05:38
  • 540: Exploratory Data Analysis 09:45
  • 541: Dealing With Categorical Values 03:04
  • 542: Model Building 05:44
  • 543: App.py 09:23
  • 544: Index.html and style.css 25:43

• ML PROJECTS: BUILDING A SALES FORCAST APP 1:26:44

  • 545: Building A Sales Forecast App 03:01
  • 546: Exploratory Data Analysis 05:50
  • 547: Feature Creation 11:46
  • 548: sales forcast Feature Correlation and Multicolinearity 16:36
  • 549: Price Forcast Dealing with Outliers 24:03
  • 550: forcast Model EXPORT 13:38
  • 551: ml_ sales forecast deploy 11:50

• WEEK 1 :: DEEP LEARNING 50:11

  • 552: 3 08:25
  • 553: 4 (1) 01:53
  • 554: 5 10:35
  • 555: 6 08:54
  • 556: 7 (1) 03:22
  • 557: 8 09:39
  • 558: 9 07:23

• ARTIFICIAL NEURAL NETWORK 1:22:07

  • 559: nn 1 00:37
  • 560: nn 2 06:42
  • 561: nn 3 02:34
  • 562: nn 4 (1) 01:21
  • 563: nn 5 05:15
  • 564: nn 6 04:31
  • 565: nn 7 17:18
  • 566: nn 8 20:03
  • 567: nn 9 23:46

• WEEK 2 :: ACTIVATION FUNCTIONS 1:04:44

  • 568: activation 1 09:51
  • 569: activation 2 06:47
  • 570: activation 3 14:14
  • 571: activation 4 05:47
  • 572: activation 5 07:23
  • 573: activation 7 03:07
  • 574: activation 8 05:52
  • 575: activation 9 09:04
  • 576: activation 10 02:39

• TENSORFLOW AND KERAS 18:45

  • 577: activation 11 02:03
  • 578: activation 12 06:46
  • 579: activation 13 04:03
  • 580: activation 14 02:24
  • 581: activation 15 03:29

• LAB SESSION: DEEP LEARNING(ANN) 2:35:06

  • 582: lecture resources 01:00
  • 583: LAB SESSION 1: Building your first Neural Network 15:07
  • 584: LAB SESSION 2: Building your first Neural Network 31:55
  • 585: Handling Overfitting in Neural Network 24:59
  • 586: L2 Regularisation 04:53
  • 587: Dropout for Overfitting in Neural Network 05:55
  • 588: Early Stopping for overfitting in NN 08:14
  • 589: ModelCheck pointing 07:13
  • 590: Load best weight 02:24
  • 591: Tensorflow Playground 07:44
  • 592: 1 Building Your Third Neural Network with MNIST 29:40
  • 593: 2 Building Your Third Neural Network with MNIST 16:02

• WEEK 3 :: FULL COMPUTER VISION COURSE 01:00

  • 594: Computer Vision lecture resources 01:00

• COMPUTER VISION (CV): BEGINNER LEVEL 52:12

  • 595: Working with Images 02:01
  • 596: The concept of Pixels 04:10
  • 597: Gray-Scale Image 05:12
  • 598: Color Image 04:29
  • 599: Different Image formats 03:47
  • 600: Image Transformation: Filtering 01:06
  • 601: Affine and Projective Transformation 03:21
  • 602: Image Feature Extraction 05:00
  • 603: LAB SESSION : working with images 23:06

• CPU VS GPU VS TPU 21:56

  • 604: Introduction to CPUs, GPUs and TPUs 13:29
  • 605: Accessing GPUs for Deep Learning 04:05
  • 606: CPU vs GPU speed 04:22

• COMPUTER VISION: INTERMEDIATE LEVEL 2:58:16

  • 607: lecture resources 01:00
  • 608: Understanding Convolution (PART 1) 13:12
  • 609: Understanding Convolution (PART 2) 07:03
  • 610: Convolution Operation 21:20
  • 611: Understanding : Filter/Kernel | Feature Map | Input Volume | Receptive Field 10:28
  • 612: Stride and Step Size 02:00
  • 613: Padding 08:11
  • 614: Pooling 16:36
  • 615: Understanding CNN Architecture 15:25
  • 616: LAB SESSION_ CNN Lab 1 45:49
  • 617: LAB SESSION_ CNN Lab 2 37:12

• WEEK 4 :: COMPUTER VISION: ADVANCED LEVEL 02:00

  • 618: Overview 01:00
  • 619: lecture resources 01:00

• CNN ARCHITECTURES 1:30:55

  • 620: cv; State of the art CNN architecture 06:19
  • 621: LeNet Architecture 28:27
  • 622: cv_ LeNet LAB 07:29
  • 623: cv_ AlexNet architecture 02:36
  • 624: cv_ AlexNet LAB 11:38
  • 625: cv_ VGG Architecture and LAB 24:32
  • 626: cv_ GoogleNet or Inception Net 09:54

• TRANSFER LEARNING 41:53

  • 627: Understanding Transfer Learning 22:18
  • 628: Steps to perform transfer learning 04:30
  • 629: When to use Transfer learning and when NOT to use. 03:32
  • 630: LAB SESSION: Transfer Learning with VGG-16 11:33

• OBJECT DETECTION 24:22

  • 631: Overview and Agenda 01:39
  • 632: Computer Vision Task 02:12
  • 633: Datasets Powering Object Detection 05:00
  • 634: Image Classification vs Image Localisation 11:09
  • 635: Challenges of Object Detection 04:22

• PERFORMANCE METRICS FOR OBJECT DETECTION 14:27

  • 636: Intersection over union 07:20
  • 637: Precision and Recall 06:13
  • 638: Mean Average Precision(mAP) 00:54

• OBJECTION DETECTION TECHNIQUES 2:15:43

  • 639: Resources 01:00
  • 640: Overview 00:47
  • 641: Brute Force Approach 01:28
  • 642: Sliding Window 01:49
  • 643: Region Proposal 04:33
  • 644: R-CNN 05:31
  • 645: Fast R-CNN 05:58
  • 646: ROI Pooling 09:01
  • 647: Faster R-CNN 12:04
  • 648: State-of-the-Art Algorithms 02:40
  • 649: YOLO 16:34
  • 650: LAB SESSION 1: YOLO LAB Overview 01:37
  • 651: LAB SESSION 2: YOLO 23:21
  • 652: LAB SESSION 3.1: YOLO 08:03
  • 653: LAB SESSION 3.2: YOLO 35:21
  • 654: SSD 05:56

• 7TH MONTH 01:00

  • 655: Overview 01:00

• WEEK 1 :: OPENCV FULL TUTORIAL 2:12:18

  • 656: OpenCV intro 03:43
  • 657: OpenCV_ Installation 07:45
  • 658: OpenCV_ Setup 02:16
  • 659: OpenCV_ Reading Images 05:32
  • 660: OpenCV_ Reading Video 09:04
  • 661: Stacking Images together 10:46
  • 662: OpenCV Join 09:03
  • 663: IMAGE: Face Detection with OpenCV 11:05
  • 664: VIDEO: Face Detection with OpenCV 06:09
  • 665: Live Streaming with OpenCV 04:00
  • 666: OpenCV Functions 05:38
  • 667: Image Detection Techniques 02:53
  • 668: Edge Detection 02:24
  • 669: Dilation and Erode 10:24
  • 670: OpenCV Conventions 04:19
  • 671: Adding Shapes 09:44
  • 672: Creating Lines 04:16
  • 673: Creating Shapes(Rectangle) 02:34
  • 674: Warp Perspective 16:15
  • 675: Adding Text 04:28

• CV PROJECT: CAR PARKING SPACE COUNTER USING OPENCV 1:35:18

  • 676: Carparking Introduction 02:50
  • 677: Carparking 1 38:06
  • 678: carpark 1 33:21
  • 679: carpark 2 21:01

• CV PROJECT(KAGGLE): FRUIT AND VEGETABLE CLASSIFICATION 1:22:37

  • 680: cv_ fruit and veg intro 07:04
  • 681: Setup your First Kaggle Code Notebook 07:07
  • 682: Building Fruit and Vegetable Classifier with Kaggle Notebooks 46:42
  • 683: Deploy a Computer Vision Classifier App 21:44

• CV PROJECT: PREDICTING LUNG DISEASE WITH COMPUTER VISION 26:26

  • 684: cv_ Lung disease 26:26

• WEEK 2 :: CV PROJECT: NOSE MASK DETECTION WITH COMPUTER VISION 30:30

  • 685: cv_ Data Preprocessing 12:01
  • 686: cv_ Training the CNN 04:51
  • 687: cv_ Detecting Face Mask 13:38

• CV PROJECT: POSE DETECTION 28:13

  • 688: cv_ Building a Detector 03:28
  • 689: cv_ Pose Detection 1 08:06
  • 690: cv_ Pose Detection 2 16:39

• CV PROJECT: BUILDING A VIRTUAL AI KEYBOARD 1:09:25

  • 691: cv_ CV Project _ Building AI Virtual Keyboard 01:01
  • 692: cv_ Building AI Virtual Keyboard 1.1 11:26
  • 693: cv_ Building AI Virtual Keyboard 1.2 07:22
  • 694: cv_ Building AI Virtual Keyboard (PART 2) 11:24
  • 695: cv_Building AI Virtual Keyboard (PART 3.1) 12:45
  • 696: cv_Building AI Virtual Keyboard (PART 3.2) 03:44
  • 697: cv_Building AI Virtual Keyboard (PART 4) 10:20
  • 698: cv_Building AI Virtual Keyboard (PART 5.1) 05:21
  • 699: cv_Building AI Virtual Keyboard (PART 5.2) 06:02

• CV PROJECT: YOLOV4 OBJECT DETECTION USING WEBCAM 28:19

  • 700: yolo project 28:19

• WEEK 3 :: NATURAL LANGUAGE PROCESSING(NLP) 30:40

  • 701: nlp_ Overview 00:50
  • 702: nlp_ Recapitulation 03:22
  • 703: nlp_ What is NLP_ 03:39
  • 704: nlp_ Applications of NLP 09:21
  • 705: nlp_ The Must-Know NLP Terminologies 02:40
  • 706: nlp_ Word 00:39
  • 707: nlp_ Tokens and Tokenizations 06:20
  • 708: nlp_ Corpus 01:32
  • 709: nlp_ Sentence and Document 02:17

• What Next? 01:00

  • 710: What next on your journey? 01:00

Beginner To Advanced Level

Complete In 9 Months or Less !!

This course is meant for students and working professionals who wish to become Data scientists, Machine Learning Engineers, and AI professionals.

The ALL IN ONE Course !

All of the following are included in the Course:

• Full SQL Course from A-Z
• Full Python Course from A-Z
• Full Statistics for Data Science course from A-Z
• Full Machine Learning course from A-Z
• Full ML Model Cloud Deployment course A-Z
• Full Deep Learning course from A-Z
• Full Artificial Intelligence course from A-Z
• Full Computer Vision course from A-Z
• Full Natural Language Processing course from A-Z
• Full Microsoft Power BI course from A-Z
• Reading Scientific Research Paper
• Github for Data Science
• Recommendation System
• A guide to do Virtual Internship

This course is for:

• Students who wish to work as Data Scientist
• Working professionals who want to transition to the field of Artificial Intelligence(A.I.) , Machine Learning, Deep learning, Computer Vision(CV), Natural Language Processing(NLP)
• Anyone interested in building A.I applications such as ChatGPT or BARD
• Anyone interesting in diving deeper into the field of Computer Vision(CV), Natural Language Processing(NLP)
• Anyone aspiring to be a Computer Vision(CV) or Natural Language Processing(NLP) engineer.
• Anyone finding it difficult to understand the field and concepts in Data Science and wants a breakdown step-by-step guide in understanding these concepts.
• Anyone who wants to be career secured and not easily affected by layoffs in organizations
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This is a beginner to advanced course and the instructor with many years of experience in the industry and classroom breaks the concepts down for anyone at any level to understand.

A laptop, internet connections and willingness to learn is enough to succeed in this comprehensive course.

Students in this course can choose a career path to become:

• Data Scientist
• Computer Vision(CV) engineer,
• Natural Language Processing(NLP) engineer
• Data Analyst
• Data consultant
• Pursue high degree : masters or PhD in the field or Data Science, Analytics, Artificial intelligence

These careers are thoroughly discussed in the course so students can make informed decisions.

Currently there are no Q&As for this course. Be the first to ask a question.