The Full Stack Data Scientist BootCamp®

By far the most comprehensive, up-to-date, and most credible Data Science course on Data Science. The Full-Stack Data Scientist BootCamp® is the all-in-one course 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 over 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.

• 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.
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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
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• 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.

Certificate of completion

Digital certificate - Included

Reed Courses Certificate of Completion

Digital certificate - Included