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70.27 GB | 46min 27s | mp4 | 1280X720 | 16:9
Genre:eLearning |Language:English
Files Included :
01 Welcome To The Course.mp4 (33.38 MB)
03 Anaconda Installation.mp4 (20.42 MB)
04 Getting Started With VS Code.mp4 (134.21 MB)
01 Getting Started With VS Code.mp4 (36.83 MB)
02 Different Ways of Creating Virtual Environment.mp4 (83.05 MB)
04 Python Basics-Syntax And Semantics.mp4 (136.88 MB)
05 Variables In Python.mp4 (88.13 MB)
06 Basics Data Types In Python.mp4 (74.37 MB)
07 Operators In Python.mp4 (165.76 MB)
08 assignment-solution.zip (3.19 KB)
01 Conditional Statements (if, elif, else).mp4 (311.12 MB)
02 Loops In Python.mp4 (306.36 MB)
03 assignment-solutions.zip (2.59 KB)
01 List And List Comprehension In Python.mp4 (198.09 MB)
03 Tuple In Python.mp4 (78.6 MB)
05 Sets In Python.mp4 (197.64 MB)
07 Dictionaries In Python.mp4 (206.66 MB)
09 Real world Usecases Of List.mp4 (37.77 MB)
02 list-assignment-solution.zip (3.47 KB)
04 tuple-assignment-solution.zip (2.87 KB)
06 set-assignments-solution.zip (2.83 KB)
08 dictionaries-assignment.zip (3.16 KB)
01 Getting Started With Functions.mp4 (83.55 MB)
02 More Coding Example With Functions.mp4 (189.39 MB)
03 Lambda Function In Python.mp4 (57.97 MB)
04 Map Function In Python.mp4 (57.53 MB)
05 Filter Function In Python.mp4 (48.52 MB)
06 advancefunctions.zip (4.43 KB)
01 Import Modules And Packages In Python.mp4 (52.89 MB)
02 Standard Library Overview.mp4 (139.84 MB)
03 packagessolution.zip (3.5 KB)
01 File Operation In Python.mp4 (131.04 MB)
02 Working With File Paths.mp4 (50.2 MB)
03 filehandlingquestions.zip (3.19 KB)
01 Exception Handling With try except else and finally blocks.mp4 (139.88 MB)
02 exceptionhandlingsolution.zip (4.11 KB)
01 Classes And Objects In Python.mp4 (83.61 MB)
03 Inheritance In OOPS.mp4 (109.46 MB)
04 Polymorphism In OOPS.mp4 (107.85 MB)
05 Encapsulation In OOPS.mp4 (127.75 MB)
06 Abstraction In OOPS.mp4 (49.49 MB)
08 Magic Methods In Python.mp4 (46.58 MB)
09 Operator Overloading In Python.mp4 (51.75 MB)
10 Custom Exception Handling.mp4 (68.22 MB)
02 classesobjectssolution.zip (4.71 KB)
07 inheritancesolutions.zip (4.29 KB)
11 oopsquestion.zip (5.22 KB)
01 Iterators In Python.mp4 (19.22 MB)
02 Generators With Practical Implementation.mp4 (73.77 MB)
03 Function Copy,Cloures And Decorators.mp4 (171.06 MB)
04 itergendecorsolutions.zip (4.51 KB)
01 Numpy In Python.mp4 (541.51 MB)
02 Pandas- DataFrame And Series.mp4 (553.19 MB)
03 Data Manipulation With Pandas And Numpy.mp4 (226.65 MB)
04 Reading Data From Various Data Source Using Pandas.mp4 (210.68 MB)
05 Data Visualization With Matplotlib.mp4 (278.67 MB)
06 Data Visualization With Seaborn.mp4 (180.93 MB)
07 numpypandasassignments.zip (8.45 KB)
02 Crud Operation With SQLite3 And Python.mp4 (162.37 MB)
01 Module-SQLite3-Assignments-Solution.zip (4.18 KB)
01 Logging Practical Implementation In Python.mp4 (197.44 MB)
02 Logging With Multiple Loggers.mp4 (45.52 MB)
03 Logging With A Real World Example.mp4 (71.9 MB)
04 Module-Logging-Assignments-Questions.zip (4.52 KB)
01 What Is Process And Threads.mp4 (109.26 MB)
02 2-Multithreading Practical Implementation With Python.mp4 (38 MB)
03 Multiprocessing Practical Implementation With Python.mp4 (20.96 MB)
04 Thread Pool Executor and Process Pool.mp4 (23.56 MB)
05 Implement Web Scraping Usecase With Multithreading.mp4 (65.98 MB)
06 Real World Usecase Implementation With MultiProcessing.mp4 (56.3 MB)
01 Memory Allocation And DeallocationGarbage collection and Best Practises.mp4 (168.1 MB)
01 Introduction To Flask Framework.mp4 (100.42 MB)
02 Understanding Simple Flask App Skeleton.mp4 (44.19 MB)
03 Integrating HTML With Flask Web App.mp4 (23.38 MB)
04 Working With HTTP Verbs Get And Post.mp4 (112.4 MB)
05 Building Dynamic Url ,Variables Rule And Jinja 2 Template Engine.mp4 (380.38 MB)
06 Working With Rest API's And HTTP Verbs Put And Delete.mp4 (225.09 MB)
01 Building Web App Using Streamlit.mp4 (95.36 MB)
02 Example Of ML App With Streamlit Web App.mp4 (53.27 MB)
01 What is Statistics And its Application.mp4 (95.35 MB)
02 Types Of Statistics.mp4 (21.44 MB)
03 Population Vs Sample Data.mp4 (18.03 MB)
04 Measure Of Central Tendency.mp4 (135.66 MB)
05 Measure Of Dispersion.mp4 (101.7 MB)
06 Why Sample Variance Is Divided By n-1.mp4 (46.13 MB)
07 Standard Deviation.mp4 (58.91 MB)
08 What Are Variables.mp4 (77.66 MB)
09 What are Random Variables.mp4 (77.02 MB)
10 Histograms- Descriptive Statistics.mp4 (79.78 MB)
11 Percentile And Quartiles- Descriptive Statistics.mp4 (99.98 MB)
12 5 Number Summary-Descriptive Statistics.mp4 (89.54 MB)
13 Correlation And Covariance.mp4 (429.84 MB)
01 Addition Rule (For Mutual And Non Mutual Exclusive Events).mp4 (122.4 MB)
02 Probability-Multiplication Rule(Independent And Dependent Events).mp4 (117.57 MB)
01 The Relationship Between PDF,PMF And CDF.mp4 (370.03 MB)
02 Types Of Probability Distribution.mp4 (85.31 MB)
03 Bernoulli Distribution.mp4 (274.01 MB)
04 Binomial Distribution.mp4 (136.22 MB)
05 Poisson Distribution.mp4 (153.2 MB)
06 Normal Gaussian Distribution.mp4 (148.29 MB)
07 Standard Normal Distribution And Z Score.mp4 (236.43 MB)
08 Uniform Distribution.mp4 (138.26 MB)
09 Log Normal Distribution.mp4 (164.41 MB)
10 Power Law Distribution.mp4 (82.54 MB)
11 Pareto Distribution.mp4 (32.32 MB)
12 Central Limit Theorem.mp4 (108.08 MB)
13 Estimates.mp4 (39.14 MB)
01 Hypothesis Testing And Mechanism.mp4 (75.52 MB)
02 What is P value.mp4 (191.47 MB)
03 Z Test- Hypothesis Testing.mp4 (396.17 MB)
04 Student t Distribution.mp4 (55.49 MB)
05 T Stats With T test Hypothesis Testing.mp4 (153.3 MB)
06 Z test Vs T test.mp4 (16.47 MB)
07 Type 1 And Type 2 Error.mp4 (51.74 MB)
08 Bayes Theorem.mp4 (102.58 MB)
09 Confidence Interval And MArgin Of Error.mp4 (89.8 MB)
10 What is Chi Square Test.mp4 (84.85 MB)
11 ChiSquare Goodness OF Fit.mp4 (54.58 MB)
12 Annova Test.mp4 (22.66 MB)
13 Assumptions Of Annova.mp4 (28.2 MB)
14 Types Of Annova.mp4 (79.45 MB)
15 Partioning Of Variance In Annova.mp4 (468.98 MB)
01 Handling Missing Values.mp4 (297.09 MB)
02 Handling Imbalanced Dataset.mp4 (134.4 MB)
03 Handling Imbalanced Dataset Using Smote.mp4 (275.32 MB)
04 Handling Outliers Using Python.mp4 (84.64 MB)
05 Data Encoding- Nominal Or OHE.mp4 (203.48 MB)
06 Label And Ordinal Encoding.mp4 (98.72 MB)
07 Target Guided Ordinal Encoding.mp4 (54.14 MB)
01 Red Wine Dataset EDA.mp4 (443.58 MB)
02 EDA And Feature Engineering Flight Price Dataset.mp4 (302.31 MB)
03 Data Cleaning With Google Playstore Dataset.mp4 (489.76 MB)
04 Part 2- EDA Google Play Store Cleaned Dataset.mp4 (278.04 MB)
01 Introduction.mp4 (127.85 MB)
02 Types of ML Techniques.mp4 (161.24 MB)
03 Equation of Line, 3d, and Hyperplane.mp4 (211 MB)
04 Distance of a point from a plane.mp4 (107.54 MB)
05 Instance based Vs Model based learning.mp4 (244.95 MB)
01 Simple Linear Regression Introduction.mp4 (34.99 MB)
02 Understanding Simple Linear regression Equations.mp4 (125.27 MB)
03 Cost Function.mp4 (290.46 MB)
04 Convergence Algorithm.mp4 (190.33 MB)
05 Convergence Algorithm Part02.mp4 (176.22 MB)
06 Multiple Linear regression.mp4 (37.28 MB)
07 Performance Metrics.mp4 (250.68 MB)
08 MSE, MAE, RMSE.mp4 (409.79 MB)
09 Overfitting and Underfitting.mp4 (113.53 MB)
10 Linear Regression with OLS.mp4 (308.39 MB)
11 Simple Linear Regression Practical.mp4 (867.74 MB)
12 Multiple Linear regression.mp4 (586.95 MB)
13 Polynomial Regression Intuition.mp4 (173.57 MB)
14 Polynomial Regression Implementation.mp4 (202.28 MB)
15 Pipeline in Polynomial.mp4 (46.91 MB)
01 Ridge Regression.mp4 (308.3 MB)
02 Lasso & ElasticNet.mp4 (168.67 MB)
03 Types Of cross Validation.mp4 (284.76 MB)
04 Cleaning the Dataset.mp4 (297.18 MB)
05 EDA and Feature Engineering.mp4 (452.47 MB)
06 Feature Selection.mp4 (70.64 MB)
07 Model Training.mp4 (77.72 MB)
08 Hyperparameter tuning.mp4 (189.71 MB)
01 Basic Simple Linear Regression Project.mp4 (400.18 MB)
02 Multiple Linear Regression Projects With Assumptions.mp4 (519.95 MB)
03 Basic Regression Project From Scratch-EDA And Feature Engineering.mp4 (326.79 MB)
04 Model Training With Cross Validation Using Lasso Regression.mp4 (365.39 MB)
05 Model Training With Ridge and Elastic net With Cross Validation.mp4 (51.71 MB)
06 Model Pickling In ML Project.mp4 (15.53 MB)
07 End To End ML Project Implementation.mp4 (621.65 MB)
08 Project Deployment In AWS.mp4 (70.51 MB)
01 Can Linear Regression Solve Classifier Problem.mp4 (122.34 MB)
02 Logistic Regression Indepth Math Intuition.mp4 (356.36 MB)
03 Performance Metrics.mp4 (355.15 MB)
04 Logistic Regression OVR.mp4 (153.57 MB)
05 Logistic Regression Implementation.mp4 (245.56 MB)
06 Grid Search Hyper Parameter.mp4 (172.56 MB)
07 Randomised Search CV.mp4 (65.9 MB)
08 Logistic OVR.mp4 (58.65 MB)
09 Logistic Imbalanced Dataset.mp4 (175.26 MB)
10 Logistic Regression ROC.mp4 (178.57 MB)
01 Introduction to support vector Machine.mp4 (51.93 MB)
02 SoftMargin and Hard Margin.mp4 (26.77 MB)
03 SVM Maths Intuition.mp4 (125.17 MB)
04 SVC Cost function.mp4 (134.99 MB)
05 Support Vector Regression.mp4 (157.08 MB)
06 SVM Kernels.mp4 (119.58 MB)
07 Support Vector Classifiers.mp4 (254.34 MB)
08 SVM Kernels implementation.mp4 (250.13 MB)
09 Support Vector Regression Implementation.mp4 (198.49 MB)
01 Understanding Baye's Theorem.mp4 (521.84 MB)
02 Variants Of Naive Baye's.mp4 (121.48 MB)
03 Naive Baye's Practical Implementation.mp4 (98.01 MB)
01 KNN Classification And Regression Indepth Intuition.mp4 (239.7 MB)
02 Optimization Of KNN- KDtree And Ball Tree Indepth Intuition.mp4 (229.93 MB)
03 KNN Classifier And Regressor Classification.mp4 (62.7 MB)
01 Introduction TO Decision Tree.mp4 (125.8 MB)
02 Entropy and Gini Impurity.mp4 (166.25 MB)
03 Information Gain.mp4 (176.21 MB)
04 Entropy vs Gini impurity.mp4 (41.26 MB)
05 Decision Tree Split for Numerical Features.mp4 (50.49 MB)
06 Post Pruning & Pre Pruning.mp4 (85.19 MB)
07 Decision Tree Regression.mp4 (408.7 MB)
08 Decision Tree Implementation.mp4 (310.92 MB)
09 Decision tree Prepruning.mp4 (79.9 MB)
10 Diabetes Prediction Using Decision Tree Regressor.mp4 (184.23 MB)
01 Bagging & Boosting Ensemble Techniques.mp4 (151.65 MB)
02 Random Forest Regression.mp4 (126.7 MB)
03 Problem Classification.mp4 (29.89 MB)
04 Feature Engineering Part 01.mp4 (122.11 MB)
05 Feature Engineering Part 02.mp4 (80.63 MB)
06 Model Training Step.mp4 (110.1 MB)
07 Random Forest Regression Project-Problem Statement.mp4 (18.82 MB)
08 Feature Engineering.mp4 (161.1 MB)
09 Model Training.mp4 (66.56 MB)
01 Introduction to Adaboost ML algorithm.mp4 (124.49 MB)
02 Creating Decision Tree Stump.mp4 (77.88 MB)
03 Performance of Decision Tree Stump.mp4 (153.11 MB)
04 Updating Weights.mp4 (52.77 MB)
05 Normalising Weights and Assigning Bins.mp4 (88.27 MB)
06 Selecting New Datapoints for Next tree.mp4 (117.14 MB)
07 Final Prediction for Adaboost.mp4 (89.21 MB)
08 Adaboost Model Training.mp4 (205.4 MB)
09 Adaboost Regressor Model Training.mp4 (109.34 MB)
01 Gradient Boosting Regression.mp4 (224.39 MB)
02 Gradient Boost Classifier Training.mp4 (120.32 MB)
03 Gradient Boost Regression Model Training.mp4 (141.39 MB)
01 Xgboost Classification Indepth Intuit.mp4 (553.6 MB)
02 Xgboost Regressor.mp4 (324.25 MB)
03 Model Training Xgboost.mp4 (152.68 MB)
04 Xgboost Regressor Training.mp4 (78.57 MB)
01 Introduction To Unsupervised Machine Learning.mp4 (80.67 MB)
01 Curse Of Dimensionality.mp4 (146.04 MB)
02 Feature Selection and Extraction.mp4 (270.45 MB)
03 PCA Geometric Intuition.mp4 (164.33 MB)
04 PCA Maths Intuition 01.mp4 (171.78 MB)
05 Eigen Decomposition on Covariance Matrix.mp4 (327.38 MB)
06 PCA Implementation.mp4 (64.39 MB)
01 Kmeans Clustering Geometric Intuition.mp4 (123.89 MB)
02 How to Find K Values.mp4 (107.12 MB)
03 Random Initialisation Trap(Kmeans++).mp4 (94.03 MB)
04 K means Clustering Implementation.mp4 (384.54 MB)
01 Hierarichal Clustering.mp4 (286.87 MB)
02 Agglomerative Clustering Implementation.mp4 (197.86 MB)
03 Kmeans vs Hierarical Mean Clustering.mp4 (53.69 MB)
01 How DBSCAN Works.mp4 (129.7 MB)
02 Examples After Applying DBSCAN.mp4 (49.39 MB)
03 Pros and Cons Of DBSCAN.mp4 (121.03 MB)
04 DBSCAN Clustering Implementation.mp4 (63.46 MB)
01 Silhoutte Clustering Intuition.mp4 (87.21 MB)
01 Anomaly Detection USsing Isolation Forest Indepth Intuition.mp4 (447.31 MB)
02 DBSCAN Clustering Anomaly Detection.mp4 (124.22 MB)
03 Local Outlier Factor Anomaly Detection.mp4 (75.7 MB)
01 Dockers Series.mp4 (4.49 MB)
02 Dockers and What is Containers.mp4 (198.77 MB)
03 Docker image vs Containers.mp4 (62.44 MB)
04 Docker vs Virtual Machines.mp4 (126.47 MB)
05 Docker Installation.mp4 (116.78 MB)
06 Docker Basic Commands.mp4 (186.83 MB)
07 Creating a Docker Image.mp4 (71.69 MB)
08 Push Docker Image to Docker Hub.mp4 (78.51 MB)
09 Docker Compose.mp4 (263.08 MB)
09 9-Dockers-Compose.mp4 (790.44 MB)
01 Introduction, Installation and Basic Commands.mp4 (187.09 MB)
02 Git Merge, Push, Checkout and Log with commands.mp4 (214.45 MB)
03 Resolving Git Branch Merge Conflict.mp4 (254.92 MB)
01 End To End ML Project With Deployment-Github And Code Set Up.mp4 (690.31 MB)
02 Implementing Project Structure, Logging And Exception Handling.mp4 (628.83 MB)
03 Discussing Project Problem Statement,EDA And Model Training.mp4 (645.09 MB)
04 Data Ingestion Implementation.mp4 (255.63 MB)
05 Data Transformation Using Pipelines Implementation.mp4 (351.94 MB)
06 Model Trainer Implementation.mp4 (239.89 MB)
07 Model Hyperparameter Tuning Implementation.mp4 (66.44 MB)
08 Building Prediction Pipeline.mp4 (252.03 MB)
09 ML Project Deployment Using AWS Beanstalk.mp4 (163.58 MB)
10 Deployment EC2 Instance With ECR.mp4 (425.35 MB)
11 Deployment Azure With Container And Images.mp4 (441.32 MB)
01 Project Structure Set up With Environment.mp4 (62.27 MB)
02 Github Repository Set Up With VS Code.mp4 (109.37 MB)
03 Packaging the Project With Setup py.mp4 (252.37 MB)
04 Logging And Exception Handling Implementation.mp4 (225.79 MB)
05 Introduction To ETL Pipelines.mp4 (62.52 MB)
06 Setting Up MongoDb Atlas.mp4 (179.01 MB)
07 ETL Pipeline Setup With Python.mp4 (322.21 MB)
08 Data Ingestion Architecture.mp4 (50.38 MB)
09 Implementing Data Ingestion Configuration.mp4 (222.57 MB)
10 Implementing Data Ingestions Component.mp4 (559.97 MB)
11 Implementing Data Validation-Part 1.mp4 (412.37 MB)
12 Implementing Data Validation-Part 2.mp4 (470.8 MB)
13 Data Transformation Architecture.mp4 (233.62 MB)
14 Data Transformation Implementation.mp4 (531.25 MB)
15 Model Trainer Implementation- Part 1.mp4 (383.85 MB)
16 Model Trainer And Evaluation With Hyperparameter Tuning.mp4 (352.56 MB)
17 Model Experiment Tracking With MLFLOW.mp4 (148.02 MB)
18 MLFLOW Experiment Tracking With Remote Respository Dagshub.mp4 (85.46 MB)
19 Model Pusher Implementation.mp4 (39.96 MB)
20 Model Training Pipeline Implementation.mp4 (446.15 MB)
21 Batch Prediction Pipeline Implementation.mp4 (191.96 MB)
22 Final Model And Artifacts Pusher To AWS S3 buckets.mp4 (330.92 MB)
23 Building Docker Image And Github Actions.mp4 (42.18 MB)
24 Github Action-Docker Image Push to AWS ECR Repo Implementation.mp4 (281.2 MB)
25 Final Deployment To EC2 instance.mp4 (348.25 MB)
01 networksecurity.zip (3.47 MB)
01 Getting Started With MLOPS With MLFlow And Dagshub With Project.mp4 (622.49 MB)
02 Data Versioning Control Implementation.mp4 (253.47 MB)
03 Building Data Science Projects With BentoML.mp4 (289.88 MB)
01 Roadmap to Learn NLP for Machine Learning.mp4 (248.89 MB)
02 Practical Use cases of NLP.mp4 (96.9 MB)
03 Tokenisation and Basic Terminologies.mp4 (103.07 MB)
04 Tokenisation Practicals.mp4 (239.44 MB)
05 Text Preprocessing Stemming using NLTK.mp4 (244.6 MB)
06 Text Preprocessing Lemmatization NLTK.mp4 (82.37 MB)
07 Text Preprocessing Stopwords.mp4 (327.72 MB)
08 Parts of Speech Tagging Using NLTK.mp4 (194.28 MB)
09 Named Entity Recognition.mp4 (54.99 MB)
10 What's Next.mp4 (110.75 MB)
11 One Hot Encoding Intuition.mp4 (81.92 MB)
12 Advantages and Disadvantages of OHE.mp4 (168 MB)
13 Bag of Words Intuition.mp4 (190.03 MB)
14 Advantages and Disadvantages BOW.mp4 (138.18 MB)
15 BOW implementation using NLTK.mp4 (281.55 MB)
16 N Grams.mp4 (38.96 MB)
17 N Gram BOW Implementation Using NLTK.mp4 (117.87 MB)
18 TF-IDF Instituion.mp4 (148.64 MB)
19 Advantages and Disadvantages of TF-IDF.mp4 (50.84 MB)
20 TFIDF Practical implementation Python.mp4 (60.39 MB)
21 Word Embeddings.mp4 (67.85 MB)
22 Word2Vec Intuition.mp4 (263.52 MB)
23 Word2Vec Cbow Intuition.mp4 (89.3 MB)
24 SkipGram Indepth Intuition.mp4 (185.12 MB)
25 Advantages of Word2Vec.mp4 (20.25 MB)
26 AvgWord2vec Indepth Intuition.mp4 (67.32 MB)
27 Word2vec Practical Implementation Gensim.mp4 (94.91 MB)
28 Spam ham Project using BOW.mp4 (110.65 MB)
29 Spam And Ham Project Using TFidf.mp4 (49.48 MB)
30 Best Practises For Solving ML Problems.mp4 (154.91 MB)
31 Part 1-Text Classification With Word2vec And AvgWord2vec.mp4 (278.12 MB)
32 Part 2- Text Classification With Word2vec And AvgWord2vec.mp4 (227.54 MB)
33 Part 1-Kindle Review Sentiment Analysis.mp4 (442.68 MB)
34 Part 2- Kindle Review Sentiment Analysis.mp4 (79.26 MB)
01 Introduction.mp4 (79.39 MB)
02 Why Deep Learning is getting Popular.mp4 (183.23 MB)
03 3 - Perception Intuition.mp4 (179.6 MB)
04 Advantages and Disadvantages of Perceptron.mp4 (66.02 MB)
05 ANN Intuition and Learning.mp4 (310.97 MB)
06 Back Propogation and Weight Updation.mp4 (366.89 MB)
07 Chain Rule of Derivatives.mp4 (160.07 MB)
08 Vanishing Gradient Problem and Sigmoid.mp4 (314.97 MB)
09 Sigmoid Activation Function.mp4 (92.53 MB)
10 Sigmoid Activation Function 2 0.mp4 (187.1 MB)
11 Tanh Activation Function.mp4 (53.19 MB)
12 Relu activation Function.mp4 (146.84 MB)
13 Leaky Relu and Parametric Relu.mp4 (63.04 MB)
14 ELU Activation Function.mp4 (37.66 MB)
15 Softmax For Multiclass Classification.mp4 (108.02 MB)
16 Which Activation Function To Apply When.mp4 (23.73 MB)
17 Loss Function Vs Cost Function.mp4 (64.67 MB)
18 Regression Cost Function.mp4 (228.83 MB)
19 Loss Function Classification Problem.mp4 (287.39 MB)
20 Which Loss Function To Use When.mp4 (16.17 MB)
21 Gradient Descent Optimisers.mp4 (166.5 MB)
22 SGD.mp4 (131.12 MB)
23 Mini Batch With SGD.mp4 (143.57 MB)
24 SGD With Momentum.mp4 (178.82 MB)
25 Adagard.mp4 (80.42 MB)
26 RMSPROP.mp4 (33.94 MB)
27 Adam Optimiser.mp4 (90.29 MB)
28 Exploding Gradient Problem.mp4 (135.56 MB)
29 Weight Initialisation Techniques.mp4 (112.42 MB)
30 Dropout Layers.mp4 (169.87 MB)
31 CNN Introduction.mp4 (116.18 MB)
32 Human Brain Vs CNN.mp4 (104.21 MB)
33 All you need to Know about Images.mp4 (95.74 MB)
34 Convolution Operation In CNN.mp4 (147.03 MB)
35 Padding In CNN.mp4 (31.29 MB)
36 Operation Of CNN Vs ANN.mp4 (71.21 MB)
37 Max, Min and Average Pooling.mp4 (96.75 MB)
38 Flattening and Fully Connected Layers.mp4 (138.05 MB)
39 CNN example with RGB.mp4 (40.79 MB)
01 Discussing Classification Problem Statement And Setting Up Vs Code.mp4 (141.59 MB)
02 Feature Transformation Using Sklearn With ANN.mp4 (208.89 MB)
03 Step By Step Training With ANN With Optimizer and Loss Functions.mp4 (363.44 MB)
04 Prediction With Trained ANN Model.mp4 (133.75 MB)
05 Integrating ANN Model With Streamlit Web APP.mp4 (77.21 MB)
06 Deploying Streamlit web app with ANN Model.mp4 (26.95 MB)
07 ANN Regresiion Practical Implementation.mp4 (164.73 MB)
08 Finding Optimal Hidden Layers And Hidden Neurons In ANN.mp4 (155.77 MB)
01 annclassification.zip (319.49 KB)
01 Introduction To NLP In Deep Learning.mp4 (346.39 MB)
01 Understanding RNN Architecture- RNN Vs ANN.mp4 (200.28 MB)
02 Forward Propogation With Time In RNN Training.mp4 (189.37 MB)
03 Backward Propogation With Time In RNN Training.mp4 (377.75 MB)
04 Problems With RNN.mp4 (334.48 MB)
01 5-Problems-With-RNN.zip (6.18 MB)
01 Problem Statement.mp4 (68.93 MB)
02 Getting Started With Word Embedding Layers.mp4 (115.99 MB)
03 Implementing Word Embedding With Keras Tensorflow.mp4 (114.21 MB)
04 Loading And Understanding IMDB Dataset And Feature Engineering.mp4 (82.81 MB)
05 Training Simple RNN With Embedding Layer.mp4 (45.49 MB)
06 Prediction From Trained Simple RNN.mp4 (47.88 MB)
07 End To End Streamlit Web App Integrated With RNN And deployment.mp4 (83.34 MB)
01 simple-rnn-imdb.zip (14.16 MB)
01 Why LSTM RNN.mp4 (190.28 MB)
02 LSTM RNN Architecture.mp4 (47.31 MB)
03 Forget Gate In LSTM RNN.mp4 (170.95 MB)
04 Input Gate And Candidate Memory In LSTM RNN.mp4 (114.41 MB)
05 Output Gate In LSTM RNN.mp4 (105.59 MB)
06 Training Process In LSTM RNN.mp4 (168.98 MB)
07 Variants Of LSTM RNN.mp4 (102.99 MB)
08 GRU RNN Complete Indepth Intuition.mp4 (352.86 MB)
01 Discussing Problem Statement.mp4 (34.83 MB)
02 Data Collection And Data Processing.mp4 (140.43 MB)
03 LSTM Neural Network Model Training.mp4 (56.78 MB)
04 Prediction From LSTM Model.mp4 (47.14 MB)
05 Streamlit Webapp Integration With LSTM Trained Model.mp4 (53.45 MB)
06 GRU RNN Variant Practical Implementation.mp4 (15.15 MB)
01 LSTM-RNN.zip (25.45 MB)
01 Bidirectional RNN Architecture and Intuition.mp4 (221.64 MB)
01 Indepth Intuition Of Encoder And Decoder-Sequence to Sequence Architecture.mp4 (397.03 MB)
02 Problems With Encoder And Decoder.mp4 (120.15 MB)
01 Attention Mechanism Indepth Architecture Explanation.mp4 (357.1 MB)
01 Plan Of Action.mp4 (36.58 MB)
02 What And Why To Use Transformers.mp4 (193.77 MB)
03 Understanding the basic architecture of transformers.mp4 (195.01 MB)
04 Self Attention Layer Working.mp4 (738.12 MB)
05 Multi Head Attention.mp4 (125.75 MB)
06 Feed Forward Neural Network With Multi Head Attention.mp4 (108.6 MB)
07 Positional Encoding Indepth Intuition.mp4 (351.37 MB)
08 Layer Normalization.mp4 (409.82 MB)
09 Layer Normalization Examples.mp4 (31.07 MB)
10 Complete Encoder transformer architecture.mp4 (255.2 MB)
11 Decoder Transformer- Plan Of Action.mp4 (83.24 MB)
12 Decoder Transformer- Masked Multi Head Attention Working.mp4 (580 MB)
13 Encoder Decoder Multi Head Attention.mp4 (171.95 MB)
14 Final Decoder Linear And Softmax Layer.mp4 (155.27 MB)]
Screenshot
![[Image: HRcslTS8_o.jpg]](https://images2.imgbox.com/96/a8/HRcslTS8_o.jpg)
01 Welcome To The Course.mp4 (33.38 MB)
03 Anaconda Installation.mp4 (20.42 MB)
04 Getting Started With VS Code.mp4 (134.21 MB)
01 Getting Started With VS Code.mp4 (36.83 MB)
02 Different Ways of Creating Virtual Environment.mp4 (83.05 MB)
04 Python Basics-Syntax And Semantics.mp4 (136.88 MB)
05 Variables In Python.mp4 (88.13 MB)
06 Basics Data Types In Python.mp4 (74.37 MB)
07 Operators In Python.mp4 (165.76 MB)
08 assignment-solution.zip (3.19 KB)
01 Conditional Statements (if, elif, else).mp4 (311.12 MB)
02 Loops In Python.mp4 (306.36 MB)
03 assignment-solutions.zip (2.59 KB)
01 List And List Comprehension In Python.mp4 (198.09 MB)
03 Tuple In Python.mp4 (78.6 MB)
05 Sets In Python.mp4 (197.64 MB)
07 Dictionaries In Python.mp4 (206.66 MB)
09 Real world Usecases Of List.mp4 (37.77 MB)
02 list-assignment-solution.zip (3.47 KB)
04 tuple-assignment-solution.zip (2.87 KB)
06 set-assignments-solution.zip (2.83 KB)
08 dictionaries-assignment.zip (3.16 KB)
01 Getting Started With Functions.mp4 (83.55 MB)
02 More Coding Example With Functions.mp4 (189.39 MB)
03 Lambda Function In Python.mp4 (57.97 MB)
04 Map Function In Python.mp4 (57.53 MB)
05 Filter Function In Python.mp4 (48.52 MB)
06 advancefunctions.zip (4.43 KB)
01 Import Modules And Packages In Python.mp4 (52.89 MB)
02 Standard Library Overview.mp4 (139.84 MB)
03 packagessolution.zip (3.5 KB)
01 File Operation In Python.mp4 (131.04 MB)
02 Working With File Paths.mp4 (50.2 MB)
03 filehandlingquestions.zip (3.19 KB)
01 Exception Handling With try except else and finally blocks.mp4 (139.88 MB)
02 exceptionhandlingsolution.zip (4.11 KB)
01 Classes And Objects In Python.mp4 (83.61 MB)
03 Inheritance In OOPS.mp4 (109.46 MB)
04 Polymorphism In OOPS.mp4 (107.85 MB)
05 Encapsulation In OOPS.mp4 (127.75 MB)
06 Abstraction In OOPS.mp4 (49.49 MB)
08 Magic Methods In Python.mp4 (46.58 MB)
09 Operator Overloading In Python.mp4 (51.75 MB)
10 Custom Exception Handling.mp4 (68.22 MB)
02 classesobjectssolution.zip (4.71 KB)
07 inheritancesolutions.zip (4.29 KB)
11 oopsquestion.zip (5.22 KB)
01 Iterators In Python.mp4 (19.22 MB)
02 Generators With Practical Implementation.mp4 (73.77 MB)
03 Function Copy,Cloures And Decorators.mp4 (171.06 MB)
04 itergendecorsolutions.zip (4.51 KB)
01 Numpy In Python.mp4 (541.51 MB)
02 Pandas- DataFrame And Series.mp4 (553.19 MB)
03 Data Manipulation With Pandas And Numpy.mp4 (226.65 MB)
04 Reading Data From Various Data Source Using Pandas.mp4 (210.68 MB)
05 Data Visualization With Matplotlib.mp4 (278.67 MB)
06 Data Visualization With Seaborn.mp4 (180.93 MB)
07 numpypandasassignments.zip (8.45 KB)
02 Crud Operation With SQLite3 And Python.mp4 (162.37 MB)
01 Module-SQLite3-Assignments-Solution.zip (4.18 KB)
01 Logging Practical Implementation In Python.mp4 (197.44 MB)
02 Logging With Multiple Loggers.mp4 (45.52 MB)
03 Logging With A Real World Example.mp4 (71.9 MB)
04 Module-Logging-Assignments-Questions.zip (4.52 KB)
01 What Is Process And Threads.mp4 (109.26 MB)
02 2-Multithreading Practical Implementation With Python.mp4 (38 MB)
03 Multiprocessing Practical Implementation With Python.mp4 (20.96 MB)
04 Thread Pool Executor and Process Pool.mp4 (23.56 MB)
05 Implement Web Scraping Usecase With Multithreading.mp4 (65.98 MB)
06 Real World Usecase Implementation With MultiProcessing.mp4 (56.3 MB)
01 Memory Allocation And DeallocationGarbage collection and Best Practises.mp4 (168.1 MB)
01 Introduction To Flask Framework.mp4 (100.42 MB)
02 Understanding Simple Flask App Skeleton.mp4 (44.19 MB)
03 Integrating HTML With Flask Web App.mp4 (23.38 MB)
04 Working With HTTP Verbs Get And Post.mp4 (112.4 MB)
05 Building Dynamic Url ,Variables Rule And Jinja 2 Template Engine.mp4 (380.38 MB)
06 Working With Rest API's And HTTP Verbs Put And Delete.mp4 (225.09 MB)
01 Building Web App Using Streamlit.mp4 (95.36 MB)
02 Example Of ML App With Streamlit Web App.mp4 (53.27 MB)
01 What is Statistics And its Application.mp4 (95.35 MB)
02 Types Of Statistics.mp4 (21.44 MB)
03 Population Vs Sample Data.mp4 (18.03 MB)
04 Measure Of Central Tendency.mp4 (135.66 MB)
05 Measure Of Dispersion.mp4 (101.7 MB)
06 Why Sample Variance Is Divided By n-1.mp4 (46.13 MB)
07 Standard Deviation.mp4 (58.91 MB)
08 What Are Variables.mp4 (77.66 MB)
09 What are Random Variables.mp4 (77.02 MB)
10 Histograms- Descriptive Statistics.mp4 (79.78 MB)
11 Percentile And Quartiles- Descriptive Statistics.mp4 (99.98 MB)
12 5 Number Summary-Descriptive Statistics.mp4 (89.54 MB)
13 Correlation And Covariance.mp4 (429.84 MB)
01 Addition Rule (For Mutual And Non Mutual Exclusive Events).mp4 (122.4 MB)
02 Probability-Multiplication Rule(Independent And Dependent Events).mp4 (117.57 MB)
01 The Relationship Between PDF,PMF And CDF.mp4 (370.03 MB)
02 Types Of Probability Distribution.mp4 (85.31 MB)
03 Bernoulli Distribution.mp4 (274.01 MB)
04 Binomial Distribution.mp4 (136.22 MB)
05 Poisson Distribution.mp4 (153.2 MB)
06 Normal Gaussian Distribution.mp4 (148.29 MB)
07 Standard Normal Distribution And Z Score.mp4 (236.43 MB)
08 Uniform Distribution.mp4 (138.26 MB)
09 Log Normal Distribution.mp4 (164.41 MB)
10 Power Law Distribution.mp4 (82.54 MB)
11 Pareto Distribution.mp4 (32.32 MB)
12 Central Limit Theorem.mp4 (108.08 MB)
13 Estimates.mp4 (39.14 MB)
01 Hypothesis Testing And Mechanism.mp4 (75.52 MB)
02 What is P value.mp4 (191.47 MB)
03 Z Test- Hypothesis Testing.mp4 (396.17 MB)
04 Student t Distribution.mp4 (55.49 MB)
05 T Stats With T test Hypothesis Testing.mp4 (153.3 MB)
06 Z test Vs T test.mp4 (16.47 MB)
07 Type 1 And Type 2 Error.mp4 (51.74 MB)
08 Bayes Theorem.mp4 (102.58 MB)
09 Confidence Interval And MArgin Of Error.mp4 (89.8 MB)
10 What is Chi Square Test.mp4 (84.85 MB)
11 ChiSquare Goodness OF Fit.mp4 (54.58 MB)
12 Annova Test.mp4 (22.66 MB)
13 Assumptions Of Annova.mp4 (28.2 MB)
14 Types Of Annova.mp4 (79.45 MB)
15 Partioning Of Variance In Annova.mp4 (468.98 MB)
01 Handling Missing Values.mp4 (297.09 MB)
02 Handling Imbalanced Dataset.mp4 (134.4 MB)
03 Handling Imbalanced Dataset Using Smote.mp4 (275.32 MB)
04 Handling Outliers Using Python.mp4 (84.64 MB)
05 Data Encoding- Nominal Or OHE.mp4 (203.48 MB)
06 Label And Ordinal Encoding.mp4 (98.72 MB)
07 Target Guided Ordinal Encoding.mp4 (54.14 MB)
01 Red Wine Dataset EDA.mp4 (443.58 MB)
02 EDA And Feature Engineering Flight Price Dataset.mp4 (302.31 MB)
03 Data Cleaning With Google Playstore Dataset.mp4 (489.76 MB)
04 Part 2- EDA Google Play Store Cleaned Dataset.mp4 (278.04 MB)
01 Introduction.mp4 (127.85 MB)
02 Types of ML Techniques.mp4 (161.24 MB)
03 Equation of Line, 3d, and Hyperplane.mp4 (211 MB)
04 Distance of a point from a plane.mp4 (107.54 MB)
05 Instance based Vs Model based learning.mp4 (244.95 MB)
01 Simple Linear Regression Introduction.mp4 (34.99 MB)
02 Understanding Simple Linear regression Equations.mp4 (125.27 MB)
03 Cost Function.mp4 (290.46 MB)
04 Convergence Algorithm.mp4 (190.33 MB)
05 Convergence Algorithm Part02.mp4 (176.22 MB)
06 Multiple Linear regression.mp4 (37.28 MB)
07 Performance Metrics.mp4 (250.68 MB)
08 MSE, MAE, RMSE.mp4 (409.79 MB)
09 Overfitting and Underfitting.mp4 (113.53 MB)
10 Linear Regression with OLS.mp4 (308.39 MB)
11 Simple Linear Regression Practical.mp4 (867.74 MB)
12 Multiple Linear regression.mp4 (586.95 MB)
13 Polynomial Regression Intuition.mp4 (173.57 MB)
14 Polynomial Regression Implementation.mp4 (202.28 MB)
15 Pipeline in Polynomial.mp4 (46.91 MB)
01 Ridge Regression.mp4 (308.3 MB)
02 Lasso & ElasticNet.mp4 (168.67 MB)
03 Types Of cross Validation.mp4 (284.76 MB)
04 Cleaning the Dataset.mp4 (297.18 MB)
05 EDA and Feature Engineering.mp4 (452.47 MB)
06 Feature Selection.mp4 (70.64 MB)
07 Model Training.mp4 (77.72 MB)
08 Hyperparameter tuning.mp4 (189.71 MB)
01 Basic Simple Linear Regression Project.mp4 (400.18 MB)
02 Multiple Linear Regression Projects With Assumptions.mp4 (519.95 MB)
03 Basic Regression Project From Scratch-EDA And Feature Engineering.mp4 (326.79 MB)
04 Model Training With Cross Validation Using Lasso Regression.mp4 (365.39 MB)
05 Model Training With Ridge and Elastic net With Cross Validation.mp4 (51.71 MB)
06 Model Pickling In ML Project.mp4 (15.53 MB)
07 End To End ML Project Implementation.mp4 (621.65 MB)
08 Project Deployment In AWS.mp4 (70.51 MB)
01 Can Linear Regression Solve Classifier Problem.mp4 (122.34 MB)
02 Logistic Regression Indepth Math Intuition.mp4 (356.36 MB)
03 Performance Metrics.mp4 (355.15 MB)
04 Logistic Regression OVR.mp4 (153.57 MB)
05 Logistic Regression Implementation.mp4 (245.56 MB)
06 Grid Search Hyper Parameter.mp4 (172.56 MB)
07 Randomised Search CV.mp4 (65.9 MB)
08 Logistic OVR.mp4 (58.65 MB)
09 Logistic Imbalanced Dataset.mp4 (175.26 MB)
10 Logistic Regression ROC.mp4 (178.57 MB)
01 Introduction to support vector Machine.mp4 (51.93 MB)
02 SoftMargin and Hard Margin.mp4 (26.77 MB)
03 SVM Maths Intuition.mp4 (125.17 MB)
04 SVC Cost function.mp4 (134.99 MB)
05 Support Vector Regression.mp4 (157.08 MB)
06 SVM Kernels.mp4 (119.58 MB)
07 Support Vector Classifiers.mp4 (254.34 MB)
08 SVM Kernels implementation.mp4 (250.13 MB)
09 Support Vector Regression Implementation.mp4 (198.49 MB)
01 Understanding Baye's Theorem.mp4 (521.84 MB)
02 Variants Of Naive Baye's.mp4 (121.48 MB)
03 Naive Baye's Practical Implementation.mp4 (98.01 MB)
01 KNN Classification And Regression Indepth Intuition.mp4 (239.7 MB)
02 Optimization Of KNN- KDtree And Ball Tree Indepth Intuition.mp4 (229.93 MB)
03 KNN Classifier And Regressor Classification.mp4 (62.7 MB)
01 Introduction TO Decision Tree.mp4 (125.8 MB)
02 Entropy and Gini Impurity.mp4 (166.25 MB)
03 Information Gain.mp4 (176.21 MB)
04 Entropy vs Gini impurity.mp4 (41.26 MB)
05 Decision Tree Split for Numerical Features.mp4 (50.49 MB)
06 Post Pruning & Pre Pruning.mp4 (85.19 MB)
07 Decision Tree Regression.mp4 (408.7 MB)
08 Decision Tree Implementation.mp4 (310.92 MB)
09 Decision tree Prepruning.mp4 (79.9 MB)
10 Diabetes Prediction Using Decision Tree Regressor.mp4 (184.23 MB)
01 Bagging & Boosting Ensemble Techniques.mp4 (151.65 MB)
02 Random Forest Regression.mp4 (126.7 MB)
03 Problem Classification.mp4 (29.89 MB)
04 Feature Engineering Part 01.mp4 (122.11 MB)
05 Feature Engineering Part 02.mp4 (80.63 MB)
06 Model Training Step.mp4 (110.1 MB)
07 Random Forest Regression Project-Problem Statement.mp4 (18.82 MB)
08 Feature Engineering.mp4 (161.1 MB)
09 Model Training.mp4 (66.56 MB)
01 Introduction to Adaboost ML algorithm.mp4 (124.49 MB)
02 Creating Decision Tree Stump.mp4 (77.88 MB)
03 Performance of Decision Tree Stump.mp4 (153.11 MB)
04 Updating Weights.mp4 (52.77 MB)
05 Normalising Weights and Assigning Bins.mp4 (88.27 MB)
06 Selecting New Datapoints for Next tree.mp4 (117.14 MB)
07 Final Prediction for Adaboost.mp4 (89.21 MB)
08 Adaboost Model Training.mp4 (205.4 MB)
09 Adaboost Regressor Model Training.mp4 (109.34 MB)
01 Gradient Boosting Regression.mp4 (224.39 MB)
02 Gradient Boost Classifier Training.mp4 (120.32 MB)
03 Gradient Boost Regression Model Training.mp4 (141.39 MB)
01 Xgboost Classification Indepth Intuit.mp4 (553.6 MB)
02 Xgboost Regressor.mp4 (324.25 MB)
03 Model Training Xgboost.mp4 (152.68 MB)
04 Xgboost Regressor Training.mp4 (78.57 MB)
01 Introduction To Unsupervised Machine Learning.mp4 (80.67 MB)
01 Curse Of Dimensionality.mp4 (146.04 MB)
02 Feature Selection and Extraction.mp4 (270.45 MB)
03 PCA Geometric Intuition.mp4 (164.33 MB)
04 PCA Maths Intuition 01.mp4 (171.78 MB)
05 Eigen Decomposition on Covariance Matrix.mp4 (327.38 MB)
06 PCA Implementation.mp4 (64.39 MB)
01 Kmeans Clustering Geometric Intuition.mp4 (123.89 MB)
02 How to Find K Values.mp4 (107.12 MB)
03 Random Initialisation Trap(Kmeans++).mp4 (94.03 MB)
04 K means Clustering Implementation.mp4 (384.54 MB)
01 Hierarichal Clustering.mp4 (286.87 MB)
02 Agglomerative Clustering Implementation.mp4 (197.86 MB)
03 Kmeans vs Hierarical Mean Clustering.mp4 (53.69 MB)
01 How DBSCAN Works.mp4 (129.7 MB)
02 Examples After Applying DBSCAN.mp4 (49.39 MB)
03 Pros and Cons Of DBSCAN.mp4 (121.03 MB)
04 DBSCAN Clustering Implementation.mp4 (63.46 MB)
01 Silhoutte Clustering Intuition.mp4 (87.21 MB)
01 Anomaly Detection USsing Isolation Forest Indepth Intuition.mp4 (447.31 MB)
02 DBSCAN Clustering Anomaly Detection.mp4 (124.22 MB)
03 Local Outlier Factor Anomaly Detection.mp4 (75.7 MB)
01 Dockers Series.mp4 (4.49 MB)
02 Dockers and What is Containers.mp4 (198.77 MB)
03 Docker image vs Containers.mp4 (62.44 MB)
04 Docker vs Virtual Machines.mp4 (126.47 MB)
05 Docker Installation.mp4 (116.78 MB)
06 Docker Basic Commands.mp4 (186.83 MB)
07 Creating a Docker Image.mp4 (71.69 MB)
08 Push Docker Image to Docker Hub.mp4 (78.51 MB)
09 Docker Compose.mp4 (263.08 MB)
09 9-Dockers-Compose.mp4 (790.44 MB)
01 Introduction, Installation and Basic Commands.mp4 (187.09 MB)
02 Git Merge, Push, Checkout and Log with commands.mp4 (214.45 MB)
03 Resolving Git Branch Merge Conflict.mp4 (254.92 MB)
01 End To End ML Project With Deployment-Github And Code Set Up.mp4 (690.31 MB)
02 Implementing Project Structure, Logging And Exception Handling.mp4 (628.83 MB)
03 Discussing Project Problem Statement,EDA And Model Training.mp4 (645.09 MB)
04 Data Ingestion Implementation.mp4 (255.63 MB)
05 Data Transformation Using Pipelines Implementation.mp4 (351.94 MB)
06 Model Trainer Implementation.mp4 (239.89 MB)
07 Model Hyperparameter Tuning Implementation.mp4 (66.44 MB)
08 Building Prediction Pipeline.mp4 (252.03 MB)
09 ML Project Deployment Using AWS Beanstalk.mp4 (163.58 MB)
10 Deployment EC2 Instance With ECR.mp4 (425.35 MB)
11 Deployment Azure With Container And Images.mp4 (441.32 MB)
01 Project Structure Set up With Environment.mp4 (62.27 MB)
02 Github Repository Set Up With VS Code.mp4 (109.37 MB)
03 Packaging the Project With Setup py.mp4 (252.37 MB)
04 Logging And Exception Handling Implementation.mp4 (225.79 MB)
05 Introduction To ETL Pipelines.mp4 (62.52 MB)
06 Setting Up MongoDb Atlas.mp4 (179.01 MB)
07 ETL Pipeline Setup With Python.mp4 (322.21 MB)
08 Data Ingestion Architecture.mp4 (50.38 MB)
09 Implementing Data Ingestion Configuration.mp4 (222.57 MB)
10 Implementing Data Ingestions Component.mp4 (559.97 MB)
11 Implementing Data Validation-Part 1.mp4 (412.37 MB)
12 Implementing Data Validation-Part 2.mp4 (470.8 MB)
13 Data Transformation Architecture.mp4 (233.62 MB)
14 Data Transformation Implementation.mp4 (531.25 MB)
15 Model Trainer Implementation- Part 1.mp4 (383.85 MB)
16 Model Trainer And Evaluation With Hyperparameter Tuning.mp4 (352.56 MB)
17 Model Experiment Tracking With MLFLOW.mp4 (148.02 MB)
18 MLFLOW Experiment Tracking With Remote Respository Dagshub.mp4 (85.46 MB)
19 Model Pusher Implementation.mp4 (39.96 MB)
20 Model Training Pipeline Implementation.mp4 (446.15 MB)
21 Batch Prediction Pipeline Implementation.mp4 (191.96 MB)
22 Final Model And Artifacts Pusher To AWS S3 buckets.mp4 (330.92 MB)
23 Building Docker Image And Github Actions.mp4 (42.18 MB)
24 Github Action-Docker Image Push to AWS ECR Repo Implementation.mp4 (281.2 MB)
25 Final Deployment To EC2 instance.mp4 (348.25 MB)
01 networksecurity.zip (3.47 MB)
01 Getting Started With MLOPS With MLFlow And Dagshub With Project.mp4 (622.49 MB)
02 Data Versioning Control Implementation.mp4 (253.47 MB)
03 Building Data Science Projects With BentoML.mp4 (289.88 MB)
01 Roadmap to Learn NLP for Machine Learning.mp4 (248.89 MB)
02 Practical Use cases of NLP.mp4 (96.9 MB)
03 Tokenisation and Basic Terminologies.mp4 (103.07 MB)
04 Tokenisation Practicals.mp4 (239.44 MB)
05 Text Preprocessing Stemming using NLTK.mp4 (244.6 MB)
06 Text Preprocessing Lemmatization NLTK.mp4 (82.37 MB)
07 Text Preprocessing Stopwords.mp4 (327.72 MB)
08 Parts of Speech Tagging Using NLTK.mp4 (194.28 MB)
09 Named Entity Recognition.mp4 (54.99 MB)
10 What's Next.mp4 (110.75 MB)
11 One Hot Encoding Intuition.mp4 (81.92 MB)
12 Advantages and Disadvantages of OHE.mp4 (168 MB)
13 Bag of Words Intuition.mp4 (190.03 MB)
14 Advantages and Disadvantages BOW.mp4 (138.18 MB)
15 BOW implementation using NLTK.mp4 (281.55 MB)
16 N Grams.mp4 (38.96 MB)
17 N Gram BOW Implementation Using NLTK.mp4 (117.87 MB)
18 TF-IDF Instituion.mp4 (148.64 MB)
19 Advantages and Disadvantages of TF-IDF.mp4 (50.84 MB)
20 TFIDF Practical implementation Python.mp4 (60.39 MB)
21 Word Embeddings.mp4 (67.85 MB)
22 Word2Vec Intuition.mp4 (263.52 MB)
23 Word2Vec Cbow Intuition.mp4 (89.3 MB)
24 SkipGram Indepth Intuition.mp4 (185.12 MB)
25 Advantages of Word2Vec.mp4 (20.25 MB)
26 AvgWord2vec Indepth Intuition.mp4 (67.32 MB)
27 Word2vec Practical Implementation Gensim.mp4 (94.91 MB)
28 Spam ham Project using BOW.mp4 (110.65 MB)
29 Spam And Ham Project Using TFidf.mp4 (49.48 MB)
30 Best Practises For Solving ML Problems.mp4 (154.91 MB)
31 Part 1-Text Classification With Word2vec And AvgWord2vec.mp4 (278.12 MB)
32 Part 2- Text Classification With Word2vec And AvgWord2vec.mp4 (227.54 MB)
33 Part 1-Kindle Review Sentiment Analysis.mp4 (442.68 MB)
34 Part 2- Kindle Review Sentiment Analysis.mp4 (79.26 MB)
01 Introduction.mp4 (79.39 MB)
02 Why Deep Learning is getting Popular.mp4 (183.23 MB)
03 3 - Perception Intuition.mp4 (179.6 MB)
04 Advantages and Disadvantages of Perceptron.mp4 (66.02 MB)
05 ANN Intuition and Learning.mp4 (310.97 MB)
06 Back Propogation and Weight Updation.mp4 (366.89 MB)
07 Chain Rule of Derivatives.mp4 (160.07 MB)
08 Vanishing Gradient Problem and Sigmoid.mp4 (314.97 MB)
09 Sigmoid Activation Function.mp4 (92.53 MB)
10 Sigmoid Activation Function 2 0.mp4 (187.1 MB)
11 Tanh Activation Function.mp4 (53.19 MB)
12 Relu activation Function.mp4 (146.84 MB)
13 Leaky Relu and Parametric Relu.mp4 (63.04 MB)
14 ELU Activation Function.mp4 (37.66 MB)
15 Softmax For Multiclass Classification.mp4 (108.02 MB)
16 Which Activation Function To Apply When.mp4 (23.73 MB)
17 Loss Function Vs Cost Function.mp4 (64.67 MB)
18 Regression Cost Function.mp4 (228.83 MB)
19 Loss Function Classification Problem.mp4 (287.39 MB)
20 Which Loss Function To Use When.mp4 (16.17 MB)
21 Gradient Descent Optimisers.mp4 (166.5 MB)
22 SGD.mp4 (131.12 MB)
23 Mini Batch With SGD.mp4 (143.57 MB)
24 SGD With Momentum.mp4 (178.82 MB)
25 Adagard.mp4 (80.42 MB)
26 RMSPROP.mp4 (33.94 MB)
27 Adam Optimiser.mp4 (90.29 MB)
28 Exploding Gradient Problem.mp4 (135.56 MB)
29 Weight Initialisation Techniques.mp4 (112.42 MB)
30 Dropout Layers.mp4 (169.87 MB)
31 CNN Introduction.mp4 (116.18 MB)
32 Human Brain Vs CNN.mp4 (104.21 MB)
33 All you need to Know about Images.mp4 (95.74 MB)
34 Convolution Operation In CNN.mp4 (147.03 MB)
35 Padding In CNN.mp4 (31.29 MB)
36 Operation Of CNN Vs ANN.mp4 (71.21 MB)
37 Max, Min and Average Pooling.mp4 (96.75 MB)
38 Flattening and Fully Connected Layers.mp4 (138.05 MB)
39 CNN example with RGB.mp4 (40.79 MB)
01 Discussing Classification Problem Statement And Setting Up Vs Code.mp4 (141.59 MB)
02 Feature Transformation Using Sklearn With ANN.mp4 (208.89 MB)
03 Step By Step Training With ANN With Optimizer and Loss Functions.mp4 (363.44 MB)
04 Prediction With Trained ANN Model.mp4 (133.75 MB)
05 Integrating ANN Model With Streamlit Web APP.mp4 (77.21 MB)
06 Deploying Streamlit web app with ANN Model.mp4 (26.95 MB)
07 ANN Regresiion Practical Implementation.mp4 (164.73 MB)
08 Finding Optimal Hidden Layers And Hidden Neurons In ANN.mp4 (155.77 MB)
01 annclassification.zip (319.49 KB)
01 Introduction To NLP In Deep Learning.mp4 (346.39 MB)
01 Understanding RNN Architecture- RNN Vs ANN.mp4 (200.28 MB)
02 Forward Propogation With Time In RNN Training.mp4 (189.37 MB)
03 Backward Propogation With Time In RNN Training.mp4 (377.75 MB)
04 Problems With RNN.mp4 (334.48 MB)
01 5-Problems-With-RNN.zip (6.18 MB)
01 Problem Statement.mp4 (68.93 MB)
02 Getting Started With Word Embedding Layers.mp4 (115.99 MB)
03 Implementing Word Embedding With Keras Tensorflow.mp4 (114.21 MB)
04 Loading And Understanding IMDB Dataset And Feature Engineering.mp4 (82.81 MB)
05 Training Simple RNN With Embedding Layer.mp4 (45.49 MB)
06 Prediction From Trained Simple RNN.mp4 (47.88 MB)
07 End To End Streamlit Web App Integrated With RNN And deployment.mp4 (83.34 MB)
01 simple-rnn-imdb.zip (14.16 MB)
01 Why LSTM RNN.mp4 (190.28 MB)
02 LSTM RNN Architecture.mp4 (47.31 MB)
03 Forget Gate In LSTM RNN.mp4 (170.95 MB)
04 Input Gate And Candidate Memory In LSTM RNN.mp4 (114.41 MB)
05 Output Gate In LSTM RNN.mp4 (105.59 MB)
06 Training Process In LSTM RNN.mp4 (168.98 MB)
07 Variants Of LSTM RNN.mp4 (102.99 MB)
08 GRU RNN Complete Indepth Intuition.mp4 (352.86 MB)
01 Discussing Problem Statement.mp4 (34.83 MB)
02 Data Collection And Data Processing.mp4 (140.43 MB)
03 LSTM Neural Network Model Training.mp4 (56.78 MB)
04 Prediction From LSTM Model.mp4 (47.14 MB)
05 Streamlit Webapp Integration With LSTM Trained Model.mp4 (53.45 MB)
06 GRU RNN Variant Practical Implementation.mp4 (15.15 MB)
01 LSTM-RNN.zip (25.45 MB)
01 Bidirectional RNN Architecture and Intuition.mp4 (221.64 MB)
01 Indepth Intuition Of Encoder And Decoder-Sequence to Sequence Architecture.mp4 (397.03 MB)
02 Problems With Encoder And Decoder.mp4 (120.15 MB)
01 Attention Mechanism Indepth Architecture Explanation.mp4 (357.1 MB)
01 Plan Of Action.mp4 (36.58 MB)
02 What And Why To Use Transformers.mp4 (193.77 MB)
03 Understanding the basic architecture of transformers.mp4 (195.01 MB)
04 Self Attention Layer Working.mp4 (738.12 MB)
05 Multi Head Attention.mp4 (125.75 MB)
06 Feed Forward Neural Network With Multi Head Attention.mp4 (108.6 MB)
07 Positional Encoding Indepth Intuition.mp4 (351.37 MB)
08 Layer Normalization.mp4 (409.82 MB)
09 Layer Normalization Examples.mp4 (31.07 MB)
10 Complete Encoder transformer architecture.mp4 (255.2 MB)
11 Decoder Transformer- Plan Of Action.mp4 (83.24 MB)
12 Decoder Transformer- Masked Multi Head Attention Working.mp4 (580 MB)
13 Encoder Decoder Multi Head Attention.mp4 (171.95 MB)
14 Final Decoder Linear And Softmax Layer.mp4 (155.27 MB)]
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![[Image: signature.png]](https://softwarez.info/images/avsg/signature.png)
