The Full Stack Data Scientist Bootcamp®

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)
Instructor Guide to 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
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 ScienceThe 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: Data Science Overview
Lecture 1 Part 1: Data Science Overview
Lecture 2 What Is Data Science?
Lecture 3 DA vs DS vs AI vs ML
Lecture 4 Industries That Use and Hire Data Scientist
Lecture 5 Applications of Data Science
Lecture 6 Data Science Lifecycle and the Maturity Framework
Lecture 7 Who is a Data Scientist?
Lecture 8 Career Opportunities In Data Science
Lecture 9 Typical Backgrounds of Data Scientists
Lecture 10 The Ultimate Path To become a Data Scientist(Skills you need to develop)
Lecture 11 Typical Salary of a Data Scientist
Section 2: FULL SQL FOR DATA SCIENCE COURSE
Lecture 12 Overview
Section 3: SQL BEGINNER LEVEL
Lecture 13 Introduction To SQL for Data Science
Lecture 14 Types of Databases
Lecture 15 What is a Query?
Lecture 16 What is SQL?
Lecture 17 SQL or SEQUEL?
Lecture 18 SQL Installation
Lecture 19 SQL Installation Guide For MacOS
Lecture 20 SQL Installation Guide For Windows
Lecture 21 Extra Help in Installing SQL
Lecture 22 Overview of SQL workbench
Section 4: SQL Commands
Lecture 23 Introduction To SQL Commands
Lecture 24 SQL CRUD Commands
Section 5: Understanding and Creating SQL Databases
Lecture 25 SQL Schema
Lecture 26 Inserting Comments in SQL
Lecture 27 Creating Databases
Section 6: Understanding and Creating SQL Tables
Lecture 28 Overview of SQL Table
Section 7: Types Of SQL KEYS
Lecture 29 Primary Key
Lecture 30 Foreign Key
Lecture 31 Composite Key
Lecture 32 Super Key
Lecture 33 Alternate Key
Section 8: Data Types In SQL
Lecture 34 SQL Data Types
Section 9: CREATE Table and INSERT Data into Table
Lecture 35 CREATE Table
Lecture 36 INSERT Data
Section 10: SQL Exercise & Solution
Lecture 37 Exercise 1 and solution
Section 11: SQL Constraints
Lecture 38 Understanding SQL Constraints
Lecture 39 NOT NULL & UNIQUE Constraints
Lecture 40 DEFAULT Constraints
Lecture 41 PRIMAY KEY Constraint
Lecture 42 Alter SQL Constraint
Lecture 43 Adding and Dropping SQL Constraint
Lecture 44 Foreign Key Constraint
Section 12: SQL INTERMEDIATE LEVEL
Lecture 45 Creating Exiting Databases
Lecture 46 Overview Of Existing Databases
Lecture 47 The SELECT Statement in Details
Lecture 48 The ORDER BY Clause
Lecture 49 The WHERE Clause
Lecture 50 Operation with SELECT statement
Lecture 51 Aliasing in SQL
Lecture 52 The DISTINCT Keyword
Lecture 53 WHERE Clause with SQL Comparison operators
Lecture 54 Exercise 2 and Solution
Lecture 55 The AND, OR and NOT Operators
Lecture 56 Exercise 3 and Solution
Lecture 57 The IN Operator
Lecture 58 Exercise 4 and Solution
Lecture 59 The BETWEEN Operator
Lecture 60 Exercise 5 and Solution
Lecture 61 The LIKE Operator
Lecture 62 Exercise 6 and Solution
Lecture 63 The REGEXP Operator
Lecture 64 Exercise 7 and Solution
Lecture 65 IS NULL & IS NOT NULL Operator
Lecture 66 Exercise 8 and Solution
Lecture 67 The ORDER BY Clause in Details
Lecture 68 The LIMIT Clause
Lecture 69 Exercise 9 and Solution
Section 13: SQL JOINS
Lecture 70 Introduction To SQL JOINS
Lecture 71 Exercise 10 and Solution
Lecture 72 Joining Across Multiple Databases
Lecture 73 Exercise 11 and Solution
Lecture 74 Joining Table to Itself
Lecture 75 Joining Across Multiple SQL Tables
Lecture 76 LEFT and RIGHT JOIN
Lecture 77 Exercise 12 and Solution
Lecture 78 Exercise 13 and Solution
Section 14: Working With Existing SQL Table
Lecture 79 INSERTING Into Existing Table
Lecture 80 INSERTING Multiple Data Into Existing Table
Lecture 81 Creating A Copy of a Table
Lecture 82 Updating Existing Table
Lecture 83 Updating Multiple Records In Existing Table
Section 15: SQL VIEW
Lecture 84 Create SQL VIEW
Lecture 85 Using SQL VIEW
Lecture 86 Alter SQL VIEW
Lecture 87 Drop SQL View
Section 16: SQL Data Summarisation: Aggregation Functions
Lecture 88 COUNT () Function
Lecture 89 SUM() Function
Lecture 90 AVG() Function
Lecture 91 SQL Combined Functions
Section 17: Advance SQL Functions
Lecture 92 Count Function in Details
Lecture 93 The HAVING() Function
Lecture 94 LENGTH() Function
Lecture 95 CONCAT() Function
Lecture 96 INSERT() Function
Lecture 97 LOCATE() Function
Lecture 98 UCASE() & LCASE() Function
Section 18: SQL ADVANCED LEVEL
Lecture 99 Overview
Section 19: SQL Stored Procedure
Lecture 100 Create a Stored Procedure
Lecture 101 Stored Procedure with Single Parameter
Lecture 102 Stored Procedure with Multiple Parameter
Lecture 103 Alter Stored Procedure
Lecture 104 Drop Stored Procedure
Section 20: Triggers
Lecture 105 Introduction to Triggers
Lecture 106 BEFORE Insert Triggers
Lecture 107 AFTER Insert Trigger
Lecture 108 DROP Triggers
Section 21: Transactions
Lecture 109 Creating Transactions
Lecture 110 Rollback Transactions
Lecture 111 Savepoint Transactions
Section 22: 1 MONTH: FULL PYTHON FOR DATA SCIENCE COURSE
Lecture 112 Overview
Section 23: Master Python For Data Science
Lecture 113 Install and Write Your First Python Code
Lecture 114 Python Course Datasets
Section 24: Introduction To Jupyter Notebook
Lecture 115 Introduction to Jupyter Notebook And Jupyter Lab
Lecture 116 Working with Code Vs Markdown
Section 25: Introduction To Google Colab
Lecture 117 Google Colab
Section 26: Getting Hands-On With Python
Lecture 118 Introduction
Lecture 119 Keywords And Identifiers
Lecture 120 Python Comments
Lecture 121 Python Docstring
Lecture 122 Python Variables
Lecture 123 Rules and Naming Conventions for Python Variables
Section 27: Python Output() | Input() | Import() Functions
Lecture 124 Python Output() Function
Lecture 125 Input() Function In Python
Lecture 126 Import() Function In Python
Section 28: Python Operators
Lecture 127 Arithmetic Operators
Lecture 128 Comparison Operators
Lecture 129 Logical Operators
Lecture 130 Bitwise Operators
Lecture 131 Assignment Operators
Lecture 132 Special Operators
Lecture 133 Membership Operators
Section 29: Python Flow Control
Lecture 134 If Statement
Lecture 135 If…Else Statement
Lecture 136 ELif Statement
Lecture 137 For loop
Lecture 138 While loop
Lecture 139 Break Statement
Lecture 140 Continue Statement
Section 30: Python Functions
Lecture 141 User Define Functions
Lecture 142 Arbitrary Arguments
Lecture 143 Function With Loops
Lecture 144 Lambda Function
Lecture 145 Built-In Function
Section 31: Python Global and Local Variables
Lecture 146 Global Variable
Lecture 147 Local Variable
Section 32: Working With Files In Python
Lecture 148 Python Files
Lecture 149 The Close Method
Lecture 150 The With Statement
Lecture 151 Writing To A File In Python
Section 33: Python Modules
Lecture 152 Python Modules
Lecture 153 Renaming Modules
Lecture 154 The from…import Statement
Section 34: Python Packages and Libraries
Lecture 155 Python Packages and Libraries
Lecture 156 PIP Install Python Libraries
Section 35: Data Types In Python
Lecture 157 Integer & Floating Point Numbers
Lecture 158 Complex Numbers & Strings
Lecture 159 LIST
Lecture 160 Tuple & List Mutability
Lecture 161 Tuple Immutability
Lecture 162 Set
Lecture 163 Dictionary
Section 36: Extra Content
Lecture 164 LIST
Lecture 165 Working On List
Lecture 166 Splitting Function
Lecture 167 Range In Python
Lecture 168 List Comprehension In Python
Section 37: Python NUMPY
Lecture 169 Lecture Resources
Lecture 170 Introduction To Numpy
Lecture 171 Creating Multi-Dimensional Numpy Arrays
Lecture 172 Numpy: Arange Function
Lecture 173 Numpy: Zeros, Ones and Eye functions
Lecture 174 Numpy: Reshape Function
Lecture 175 Numpy: Linspace
Lecture 176 Numpy: Resize Function
Lecture 177 Numpy:Generating Random Values With random.rand
Lecture 178 Numpy:Generating Random Values With random.randn
Lecture 179 Numpy:Generating Random Values With random.randint
Lecture 180 Numpy: Indexing & Slicing
Lecture 181 Numpy: Broadcasting
Lecture 182 Numpy: How To Create A Copy Dataset
Lecture 183 Numpy: DataFrame Introduction
Lecture 184 Numpy: Creating Matrix
Section 38: Numpy Assignment
Section 39: Python PANDAS
Lecture 185 Pandas Lecture resources
Lecture 186 Pandas- Series 1
Lecture 187 Pandas- Series 2
Lecture 188 Pandas- Loc & iLoc
Lecture 189 Pandas- DataFrame Introduction
Lecture 190 Pandas- Operations On Pandas DataFrame
Lecture 191 Pandas- Selection And Indexing On Pandas DataFrame
Lecture 192 Pandas- Reading A Dataset Into Pandas DataFrame
Lecture 193 Pandas- Adding A Column To Pandas DataFrame
Lecture 194 Pandas- How To Drop Columns And Rows In Pandas DataFrame
Lecture 195 Pandas- How To Reset Index In Pandas Dataframe
Lecture 196 Pandas- How To Rename A Column In Pandas Dataframe
Lecture 197 Pandas- Tail(), Column and Index
Lecture 198 Pandas- How To Check For Missing Values or Null Values(isnull() Vs Isna())
Lecture 199 Pandas- Pandas Describe Function
Lecture 200 Pandas- Conditional Selection With Pandas
Lecture 201 Pandas- How To Deal With Null Values
Lecture 202 Pandas- How To Sort Values In Pandas
Lecture 203 Pandas- Pandas Groupby
Lecture 204 Pandas- Count() & Value_Count()
Lecture 205 Pandas- Concatenate Function
Lecture 206 Pandas- Join & Merge(Creating Dataset)
Lecture 207 Pandas-Join
Lecture 208 Pandas- Merge
Section 40: Data Visualisation: MatplotIib And Seaborn
Lecture 209 Lecture resources
Lecture 210 Matplotlib | Subplots
Lecture 211 Seborn | Scatterplot | Correlation | Boxplot | Heatmap
Lecture 212 Univariate | Bivariate | Multivariate Data Visualisation
Section 41: Python Project Assignment
Section 42: FULL STATISTICS FOR DATA SCIENCE
Lecture 213 Overview
Section 43: Master Statistics For Data Science
Lecture 214 Lecture resources
Lecture 215 Statistics For Data Science Curriculum
Lecture 216 Why Statistics Is Important For Data Science
Lecture 217 How Much Maths Do I Need To Know?
Section 44: Statistical Methods Deep Dive
Lecture 218 Statistical Methods Deep Dive
Lecture 219 Types Of Statistics
Lecture 220 Common Statistical Terms
Section 45: Data
Lecture 221 What Is Data?
Lecture 222 Data Types
Lecture 223 Data Attributes and Data Sources
Lecture 224 Structured Vs Unstructured Data
Section 46: Frequency Distribution
Lecture 225 Frequency Distribution
Section 47: Central Tendency
Lecture 226 Central Tendency
Lecture 227 Mean,Median, Mode
Section 48: Measures of Dispersion
Lecture 228 Measures of Dispersion
Lecture 229 Variance and Standard Deviation
Lecture 230 Example of Variance and Standard Deviation
Lecture 231 Variance and Standard Deviation In Python
Section 49: Coefficient of Variations
Lecture 232 Coefficient of Variations
Section 50: The Five Number Summary & The Quartiles
Lecture 233 The Five Number Summary
Lecture 234 The Quartiles: Q1 | Q2 | Q3 | IQR
Section 51: The Normal Distribution
Lecture 235 Introduction To Normal Distribution
Lecture 236 Skewed Distributions
Lecture 237 Central Limit Theorem
Section 52: Correlation
Lecture 238 Introduction to Correlation
Lecture 239 Scatterplot For Correlation
Lecture 240 Correlation is NOT Causation
Section 53: Probability
Lecture 241 Why Probability In Data Science?
Lecture 242 Probability Key Concepts
Lecture 243 Mutually Exclusive Events
Lecture 244 Independent Events
Lecture 245 Rules For Computing Probability
Section 54: Baye’s Theorem
Lecture 246 Baye’s Theorem Overview
Section 55: Hypothesis Testing
Lecture 247 Introduction To Hypothesis.mp4
Lecture 248 Null Vs Alternative Hypothesis
Lecture 249 Setting Up Null and Alternative Hypothesis
Lecture 250 One-tailed Vs Two-tailed test
Lecture 251 Key Points On Hypothesis Testing
Lecture 252 Type 1 vs Type 2 Errors
Lecture 253 Process Of Hypothesis testing
Lecture 254 P-Value
Lecture 255 Alpha-Value or Alpha Level
Lecture 256 Confidence Level
Section 56: PROJECT: Statistics For Data Science
Lecture 257 Project resources
Lecture 258 Project Solution Code
Section 57: GITHUB For Data Science
Lecture 259 Lecture resources
Lecture 260 Introduction to Github for Data Science
Lecture 261 Setting up Github account for Data Science projects
Lecture 262 Create Github Profile for Data Science
Lecture 263 Create Github Project Description for Data Science
Section 58: ARTIFICIAL INTELLIGENCE(AI) and MACHINE LEARNING(ML)
Lecture 264 Overview
Section 59: FULL MACHINE LEARNING COURSE
Lecture 265 Introduction To Machine Learning
Lecture 266 Overview of Machine Learning Curriculum
Lecture 267 Practical Understanding Of Machine Learning (PART 1)
Lecture 268 Practical Understanding Of Machine Learning (PART 2)
Lecture 269 Applications of Machine Learning
Lecture 270 Machine Learning Life Cycle
Section 60: USE CASE
Lecture 271 The Microsoft Data Science Project
Lecture 272 Setting Up Your Environment for Machine Learning
Section 61: Machine Learning Algorithms
Lecture 273 How Machine Learning Algorithms Learn
Lecture 274 Difference Between Algorithm and Model
Lecture 275 Supervised vs Unsupervised ML
Lecture 276 Dependent vs Independent Variables
Section 62: Working with Machine Learning Data
Lecture 277 Lecture Resources
Lecture 278 Considerations When Loading Data
Lecture 279 Loading Data from a CSV File
Lecture 280 Loading Data from a URL
Lecture 281 Loading Data from a Text File
Lecture 282 Loading Data from an Excel File
Lecture 283 Skipping Rows while Loading Data
Lecture 284 Peek at your Data
Lecture 285 Dimension of your Data
Lecture 286 Checking Data Types of your Dataset
Lecture 287 Descriptive Statistics of your Dataset
Lecture 288 Class Distribution of your Dataset
Lecture 289 Correlation of your Dataset
Lecture 290 Skewness of your Dataset
Lecture 291 Missing Values in your Dataset
Lecture 292 Histogram of Dataset
Lecture 293 Density Plot of Dataset
Lecture 294 Box and Whisker Plot
Lecture 295 Correlation Matrix
Lecture 296 Scatter Matrix(Pairplot)
Section 63: Regression
Lecture 297 What is Regression?
Section 64: Linear Regression
Lecture 298 Introduction to Linear Regression
Lecture 299 Conceptual Understanding of Linear Regression
Lecture 300 Hyperplane
Lecture 301 MSE vs RMSE
Section 65: LAB SESSION: Linear Regression
Lecture 302 Training Data vs Validation Data vs Testing Data
Lecture 303 Splitting Dataset into Training and Testing
Lecture 304 Linear Regression LAB 1
Lecture 305 Linear Regression LAB 2(PART 1)
Lecture 306 Linear Regression LAB 2(PART 2)
Section 66: Logistic Regression Algorithm
Lecture 307 Regressor Algorithm Vs Classifier Algorithm
Lecture 308 Introduction To Logistic Regression Algorithm
Lecture 309 Limitations of Linear Regression
Lecture 310 PART 2: Intuitive Understanding Of Logistic Regression
Lecture 311 The Mathematics Behind Logistic Regression Algorithm
Lecture 312 LAB SESSION 1: Practical Implementation of Logistic Regression Algorithm
Lecture 313 LAB SESSION 2: Practical Implementation of Logistic Regression Algorithm
Lecture 314 LAB SESSION 3: Building Logistic Regression Model
Section 67: Naive Bayes Algorithm (NB)
Lecture 315 Introduction to Naive Bayes Algorithm
Lecture 316 The Mathematics Behind Naive Bayes Algorithm
Lecture 317 LAB SESSION: Building Naive Bayes Model
Section 68: K-Nearest Neighbor Algorithm (KNN)
Lecture 318 Introduction to K-Nearest Neighbor Algorithm
Lecture 319 Distance Measures In K-Nearest Neighbor
Lecture 320 Exploratory Data Analysis In K-NN
Lecture 321 LAB SESSION: Building A K-Nearest Neighbor
Lecture 322 Choosing K In K-NN
Section 69: Support Vector Machine Algorithm (SVM)
Lecture 323 Introduction to Support Vector Machine (SVM) algorithm
Lecture 324 Mathematics of SVM and Intuitive Understanding of SVM Algorithm
Lecture 325 Non-Linearly Separable Vectors
Lecture 326 SVM Data Pre-processing
Lecture 327 Building an SVM Model
Section 70: Machine Learning Algorithm Performance Metrics
Lecture 328 Lecture Resources
Lecture 329 Overview
Lecture 330 Confusion Matrix: True Positive | False Positive | True Negative | False Neg.
Lecture 331 Accuracy
Lecture 332 Precision
Lecture 333 Recall
Lecture 334 The Tug of War between Precision and Recall
Lecture 335 F 1 Score
Lecture 336 Classification Report
Lecture 337 ROC and AUC
Lecture 338 LAB SESSION: AUC and ROC
Section 71: Overfitting and Underfitting
Lecture 339 Overfitting and Underfitting
Lecture 340 LAB SESSION: Preventing Overfitting (PART 1)
Lecture 341 LAB SESSION: Preventing Overfitting (PART 2)
Lecture 342 Preventing Underfitting
Section 72: Bias vs Variance
Lecture 343 Bias vs Variance
Lecture 344 The Bias Variance Tradeoff
Section 73: Decision Tree Algorithm
Lecture 345 Decision Tree Overview
Lecture 346 CART: Introduction To Decision Tree
Lecture 347 Purity Metrics: Gini Impurity | Gini Index
Lecture 348 Calculating Gini Impurity (PART 1)
Lecture 349 Calculating Gini Impurity (PART 2)
Lecture 350 Information Gain
Lecture 351 Overfitting in Decision Trees
Lecture 352 Prunning
Lecture 353 LAB SESSION: Prunning
Section 74: Ensemble Techniques
Lecture 354 Lecture Resources
Lecture 355 Introduction To Ensemble Techniques
Lecture 356 Understanding Ensemble Techniques
Lecture 357 Difference b/n Random Forest & Decision Tree
Lecture 358 Why Random Forest Algorithm
Lecture 359 More on Random Forest Algorithm
Lecture 360 Introduction to Bootstrap Sampling | Bagging
Lecture 361 Understanding Bootstrap Sampling
Lecture 362 Diving Deeper into Bootstrap Sampling
Lecture 363 Bootstrap Sampling summary
Lecture 364 Bagging
Lecture 365 Boosting
Lecture 366 Adaboost : Introduction
Lecture 367 The Maths behind Adaboost algorithm
Lecture 368 Gradient Boost: Introduction
Lecture 369 Gradient Boosting : An Intuitive Understanding
Lecture 370 The Mathematics behind Gradient Boosting Algorithm
Lecture 371 XGBoost: Introduction
Lecture 372 Maths of XGBoost (PART 1)
Lecture 373 Maths of XGBoost (PART 2)
Lecture 374 LAB SESSION 1: Ensemble Techniques
Lecture 375 LAB SESSION 2: Ensemble Techniques
Lecture 376 Stacking: An Introduction
Lecture 377 LAB SESSION: Stacking
Section 75: UNSUPPERVISED MACHINE LEARNING ALGORITHMS
Lecture 378 Overview
Section 76: K-Means Clustering Algorithm
Lecture 379 Difference between K-NN and K-Means
Lecture 380 Introduction to K-Means Clustering algorithm
Lecture 381 The Llyod’s Method-Shifting the Centroids
Lecture 382 LAB SESSION: K-Means Algorithm
Lecture 383 Choosing K in Kmeans-The Elbow Method
Section 77: Hierarchical Clustering Algorithm
Lecture 384 Introduction to Hierarchical Clustering
Lecture 385 Dendrograms(Cophenetic correlation)
Lecture 386 LAB SESSION: Building Hierarchical Clustering Model
Section 78: Principal Component Analysis (PCA)
Lecture 387 Overview of Principal Component Analysis (PCA)
Section 79: Feature Engineering : Model Selection & Optimisation
Lecture 388 Lecture Resources
Lecture 389 KFold Cross Validation
Lecture 390 LAB SESSION: KFold Cross Validation
Lecture 391 Bootstrap Sampling
Lecture 392 Leave One Out Cross Validation(LOOCV).mp4
Lecture 393 Hyper-parameter Tuning: An Introduction
Lecture 394 GridSearchCV: An Introduction
Lecture 395 RandomSearchCV: An Introduction
Lecture 396 LAB SESSION 1: GridSearchCV
Lecture 397 LAB SESSION 2: GridSearchCV.mp4
Lecture 398 LAB SESSION: RandomSearchCV
Lecture 399 Reguralization
Lecture 400 Lasso(L1) and Ridge (L2) Regression
Section 80: Saving and Loading ML Model
Lecture 401 Saving and Loading ML Model
Section 81: RECOMMENDATION SYSTEMS
Lecture 402 Lecture Resources
Lecture 403 Recommendation System: An Overview
Lecture 404 Where Recommender Systems came from
Lecture 405 Applications of Recommendation Systems
Lecture 406 Why Recommender Systems?
Lecture 407 Types of Recommender Systems
Lecture 408 Popularity based Recommender Systems
Lecture 409 LAB SESSION: Popularity based Recommender
Lecture 410 Content-based Filtering: An Overview
Lecture 411 Cosine Similarity
Lecture 412 Cosine Similarity with Python
Lecture 413 Document Term Frequency Matrix
Lecture 414 LAB SESSION: Building Content-based Recommender Engine
Lecture 415 Collaborative Filtering: An Introduction
Lecture 416 LAB SESSION: Collaborative Filtering
Lecture 417 Evaluation Metrics for Recommender Systems
Section 82: PROJECTS SESSION: MACHINE LEARNING
Lecture 418 Overview
Section 83: ML PROJECTS: Building CRUD App
Lecture 419 CRUD Project Overview
Lecture 420 Building CRUD App
Section 84: ML PROJECT: Building Covid-19 Report Dashboard for Berlin City
Lecture 421 Project files
Lecture 422 Project Overview: Building Covid-19 Report Dashboard App for Berlin City
Lecture 423 Building a Covid Dashboard App for Berlin City
Section 85: ML PROJECTS: Building IPL Score Predictor App
Lecture 424 ML Project: Building IPL Score Predictor App
Section 86: ML PROJECTS: Building a Sales Forcast App
Lecture 425 ML Project: Building a Sales Forcast App
Section 87: SCIENTIFIC RESEARCH PAPER
Lecture 426 Lecture resources
Lecture 427 Reading Scientific Paper: An Overview
Lecture 428 What you will learn
Lecture 429 What is a Scientific Research Paper?
Lecture 430 Importance of Reading Research Papers
Lecture 431 Components of a Research Paper
Lecture 432 How to Read Scientific Research Papers
Lecture 433 Where to find Data Science research papers
Lecture 434 Assignment
Section 88: ARTIFICIAL INTELLIGENCE: Computer Vision
Lecture 435 Lecture resources
Lecture 436 Artificial Intelligence: An Introduction
Lecture 437 The Big Picture of AI
Section 89: DEEP LEARNING
Lecture 438 Introduction To Deep Learning
Lecture 439 What you will learn
Lecture 440 What is Artificial Neural Network?
Lecture 441 Neurons and Perceptrons
Lecture 442 Machine Learning vs Deep Learning
Lecture 443 Why Deep Learning
Lecture 444 Applications of Deep Learning
Section 90: Artificial Neural Network
Lecture 445 Neural Network: An Overview
Lecture 446 Architecture: Components of the Perceptron
Lecture 447 Fully Connected Neural Network
Lecture 448 Types of Neural Networks
Lecture 449 How Neural Networks work
Lecture 450 Propagation: Forward and Back Propagation
Lecture 451 Understanding Neural Network
Lecture 452 Hands-on of Forward and Back Propagation (PART 1)
Lecture 453 Hands-on of Forward and Back Propagation (PART 2)
Lecture 454 Chain Rule in Backpropagation
Lecture 455 Optimizers In NN
Section 91: Activation Functions
Lecture 456 Activation Functions: An Introduction
Lecture 457 Sigmoid Activation Function
Lecture 458 Vanishing Gradient
Lecture 459 TanH Activation Function
Lecture 460 ReLU Activation Function
Lecture 461 Leaky ReLU Activation Function
Lecture 462 ELU Activation Function
Lecture 463 SoftMax Activation Function
Lecture 464 Activation functions summary
Lecture 465 project files
Section 92: Tensorflow and Keras
Lecture 466 Overview
Lecture 467 Introduction to Tensorflow
Lecture 468 Tensors and Dataflows in Tensorflow
Lecture 469 Tensorflow Versions
Lecture 470 Keras
Section 93: LAB SESSION: Deep Learning(ANN)
Lecture 471 Lecture resources
Lecture 472 LAB SESSION : Building your first Neural Network
Lecture 473 LAB SESSION : Building your Second Neural Network
Lecture 474 Handling Overfitting in Neural Network
Lecture 475 L2 Regularisation
Lecture 476 Dropout for Overfitting in Neural Network
Lecture 477 Early Stopping for overfitting in NN
Lecture 478 ModelCheck pointing
Lecture 479 Load best weight
Lecture 480 Tensorflow Playground
Lecture 481 Building Your Third Neural Network with MNIST
Section 94: FULL COMPUTER VISION COURSE
Lecture 482 Lecture resources
Section 95: COMPUTER VISION: Beginner Level
Lecture 483 lecture resources
Lecture 484 Working with Images
Lecture 485 The concept of Pixels
Lecture 486 Gray-Scale Image
Lecture 487 Color Image
Lecture 488 Different Image formats
Lecture 489 Image Transformation: Filtering
Lecture 490 Affine and Projective Transformation
Lecture 491 Image Feature Extraction
Lecture 492 LAB SESSION: working with images
Lecture 493 LAB SESSION 2: Working with Images
Section 96: CPU vs GPU vs TPU
Lecture 494 Introduction to CPUs, GPUs and TPUs
Lecture 495 Accessing GPUs for Deep Learning
Lecture 496 CPU vs GPU speed
Section 97: COMPUTER VISION: Intermediate Level
Lecture 497 Lecture resources
Lecture 498 Introduction to Convolutional Neural Networks(CNN)
Lecture 499 Understanding Convolution (PART 1)
Lecture 500 Understanding Convolution (PART 2)
Lecture 501 Convolution Operation
Lecture 502 Understanding : Filter/Kernel | Feature Map | Input Volume | Receptive Field
Lecture 503 Filter vs Kernel
Lecture 504 Stride and Step Size
Lecture 505 Padding
Lecture 506 Pooling
Lecture 507 Understanding CNN Architecture
Lecture 508 LAB SESSION: CNN Lab 1
Lecture 509 LAB SESSION: CNN Lab 2
Section 98: COMPUTER VISION: Advanced Level
Lecture 510 Overview
Lecture 511 Lecture resources
Section 99: CNN Architectures
Lecture 512 State-of-the-Art CNN architecture
Lecture 513 LeNet Architecture
Lecture 514 LAB SESSION: LeNet LAB
Lecture 515 AlexNet Architecture
Lecture 516 LAB SESSION: AlexNet LAB
Lecture 517 VGG Architecture and LAB
Lecture 518 GoogleNet or Inception Net
Section 100: Transfer Learning
Lecture 519 Understanding Transfer Learning
Lecture 520 Steps to perform transfer learning
Lecture 521 When to use Transfer learning and when NOT to use.
Lecture 522 LAB SESSION: Transfer Learning with VGG-16
Section 101: Object Detection
Lecture 523 Overview and Agenda
Lecture 524 Computer Vision Task
Lecture 525 Datasets Powering Object Detection
Lecture 526 Image Classification vs Image Localisation
Lecture 527 Challenges of Object Detection
Section 102: Performance Metrics for Object Detection
Lecture 528 Intersection Over Union(IoU)
Lecture 529 Precision and Recall
Lecture 530 Mean Average Precision(mAP)
Section 103: Objection Detection Techniques
Lecture 531 Lecture resources
Lecture 532 Overview
Lecture 533 Brute Force Approach
Lecture 534 Sliding Window
Lecture 535 Region Proposal
Lecture 536 R-CNN
Lecture 537 Fast R-CNN
Lecture 538 ROI Pooling
Lecture 539 Faster R-CNN
Lecture 540 State-of-the-Art Algorithms
Lecture 541 YOLO
Lecture 542 LAB SESSION 1: YOLO LAB Overview
Lecture 543 LAB SESSION 2: YOLO
Lecture 544 LAB SESSION 3: YOLO
Lecture 545 SSD
Section 104: OPENCV FULL TUTORIAL
Lecture 546 Introduction To OpenCV
Lecture 547 Opencv
Lecture 548 Opencv
Lecture 549 Opencv
Lecture 550 Opencv
Lecture 551 Opencv
Lecture 552 Opencv
Lecture 553 Opencv
Lecture 554 Opencv
Lecture 555 Opencv
Lecture 556 Opencv
Lecture 557 Opencv
Lecture 558 Opencv
Lecture 559 Opencv
Lecture 560 opencv
Lecture 561 opencv
Lecture 562 opencv
Lecture 563 opencv
Lecture 564 opencv
Lecture 565 opencv
Lecture 566 opencv
Section 105: PROJECTS: COMPUTER VISION PROJECTS
Lecture 567 Overview
Section 106: CV PROJECT: Car Parking Space Counter Using OpenCV
Lecture 568 Car Park Counter with OpenCV: Project Overview
Lecture 569 PART 1: Building Car Park Counter With OpenCV
Lecture 570 PART 2: Building Car Park Counter With OpenCV
Section 107: CV PROJECT(Kaggle): Fruit and Vegetable Classification
Lecture 571 Lecture resources
Lecture 572 PROJECT: Fruit and Vegetable Classification Overview
Lecture 573 Setup your First Kaggle Code Notebook
Lecture 574 Building Fruit and Vegetable Classifier with Kaggle Notebooks
Lecture 575 Deploy a Computer Vision Classifier App
Section 108: CV PROJECT: Predicting Lung Disease with Computer Vision
Lecture 576 Predicting Lung Disease
Section 109: CV PROJECT: Nose Mask Detection with Computer Vision
Lecture 577 Project files
Lecture 578 Data Preprocessing
Lecture 579 Training the CNN
Lecture 580 Detecting Face Mask
Section 110: CV PROJECT: Pose Detection
Lecture 581 Building a Pose Detector
Section 111: CV PROJECT: Building a Face Detector with Computer vision
Lecture 582 Building a Face Detector with AI
Section 112: CV PROJECT: Building a virtual AI Keyboard
Lecture 583 CV Project : Building AI Virtual Keyboard
Section 113: CV PROJECT: Yolov4 Object Detection Using Webcam
Lecture 584 Yolov4 Object Detection Using Webcam
Section 114: NATURAL LANGUAGE PROCESSING(NLP)
Lecture 585 Lecture resources
Lecture 586 Overview
Lecture 587 Recapitulation
Lecture 588 What is NLP?
Lecture 589 Applications of NLP
Lecture 590 The Must-Know NLP Terminologies
Lecture 591 Word
Lecture 592 Tokens and Tokenizations
Lecture 593 Corpus
Lecture 594 Sentence and Document
Lecture 595 Vocabulary
Lecture 596 Stopwords
Section 115: Hands-On NLP: Text Pre-processing
Lecture 597 Tokenization with NLTK , SpaCy and Gensim
Lecture 598 Removing Stopwords with NLP Libraries
Section 116: Text Pre-processing: Normalization
Lecture 599 Text Normalization
Lecture 600 Stemming and Lemmatization
Lecture 601 LAB SESSION: Stemming and Lemmatization
Section 117: Part Of Speech (POS) Tagging
Lecture 602 Lecture resources
Lecture 603 Understanding POS Tagging
Lecture 604 LAB SESSION: Part of Speech Tagging
Lecture 605 Chunking
Section 118: ADVANCED LEVEL: Hands-On Text Pre-processing
Lecture 606 Advanced Text Preprocessing
Lecture 607 Frequency of Words | Bi-Gram | N-Grams
Lecture 608 More on Stemming and Lemmatization
Section 119: Introduction To Statistical NLP Techniques
Lecture 609 Bag of Words (BoW)
Lecture 610 TF-IDF
Section 120: Language Modelling
Lecture 611 Understanding language modelling
Section 121: Word Embeddings
Lecture 612 Understanding Word Embeddings
Lecture 613 Feature Representations
Section 122: Word2Vec
Lecture 614 The Challenge with BoW and TF-IDF
Lecture 615 Understanding Word2Vec
Lecture 616 LAB SESSION: Word2Vec
Lecture 617 CBOW and Skip-Gram
Section 123: GloVe
Lecture 618 Understanding GloVe
Section 124: Sentence Parsing
Lecture 619 Sentence Parsing
Lecture 620 Chunking & Chinking & Syntax Tree
Section 125: Sequential Models
Lecture 621 Sequential Model: An Introduction
Lecture 622 Traditional ML vs Sequential Modelling
Section 126: Recurrent Neural Network (RNN)
Lecture 623 What is a Recurrent Neural Network (RNN) ?
Lecture 624 Types of RNNs
Lecture 625 Use Cases of RNNs
Lecture 626 Vanilla Neural Network (NN) vs Recurrent Neural Network (RNN)
Lecture 627 Backpropagation Through Time (BTT)
Lecture 628 Mathematics Behind BTT
Lecture 629 Vanishing and Exploding Gradient
Lecture 630 The problem of Long Term Dependencies
Lecture 631 Bidirectional RNN (BRNN)
Lecture 632 Gated Recurrent Unit(GRU)
Section 127: LSTM
Lecture 633 Lecture resources
Lecture 634 LSTM: An Introduction
Lecture 635 The LSTM Architecture
Lecture 636 LAB SESSION 1: LSTM
Lecture 637 LAB SESSION 2: Tween Sentiment Analysis using RNN
Lecture 638 LAB SESSION 3: Tween Sentiment Analysis using LSTM
Section 128: Sequence To Sequence Models (Seq2Seq)
Lecture 639 Sequence To Sequence models: An introduction
Lecture 640 Encoder & Decoder
Lecture 641 LAB SESSION: Language Translation
Lecture 642 LAB SESSION 2: Language Translation
Section 129: NLP PROJECT: Sentiment Analyzer
Lecture 643 Project files
Lecture 644 Building Sentiment Analyzer App
Lecture 645 LAB: Building Sentiment Analyzer App
Section 130: Name Entity Recognition (NER)
Lecture 646 Lecture Resources
Lecture 647 NER : An Introduction
Lecture 648 Example of Name Entity Recognition
Lecture 649 How Name Entity Recognition works
Lecture 650 Applications of NER
Lecture 651 LAB SESSION: Hands-On Name Entity Recognition
Lecture 652 LAB SESSION 2: Name Entity Recognition
Lecture 653 LAB SESSION: Visualizing Name Entity Recognition
Lecture 654 Assignment
Section 131: NLP PROJECT: Building a Name Entity Recognition App
Lecture 655 Project: Building a Name Entity Recognition Web App
Lecture 656 Project: Building your NER web App
Section 132: NLP PROJECT: AI Resume Analyzer App
Lecture 657 NLP Project: Building AI Resume Analyzer
Lecture 658 Project: AI Resume Analyzer
This course is for beginners who want to start a career in Data Science,Anyone who is interested to become a Full Stack Data Scientist,Any student who want to enter the field of Data Science after college,Any graduate who finds it difficult to find job in other IT field and will like to upskill in Data Science to secure a job,Any employee or worker looking for a career change,Anyone interested in the field of Artificial Intelligence,Anyone interested in the field of Computer Vision,Anyone interested in the field of Natural Language Processing,Anyone enrolled in other course and finding it difficult to understand the concepts,Anyone who wants to really dive deep into understanding the concepts and master it,Anyone who wants to secure a job in the field of Data Science, AI and Machine Learning,Anyone interested in building AI and Data Science products
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