Certified AI Engineering Masterclass From Zero to AI Hero

Creation Time	Jan. 3, 2026, 6:13 a.m.
Last Access Time	Oct. 4, 2026, 9:23 a.m.
File Size	7.0 GB
Keywords	From AI Masterclass Zero to Engineering Hero Certified
Total Requests	748
Total Files	413
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12. Week 11 Recurrent Neural Networks (RNNs) and Sequence Modeling/7. Day 6 Sequence-to-Sequence Models and Applications.mp4 162.7 MB
2. Week 1 Python Programming Basics/8. Day 7 Pythonic Code and Project Work.mp4 148.6 MB
6. Week 5 Introduction to Machine Learning/4. Day 3 Advanced Regression Models – Polynomial Regression and Regularization.mp4 133.4 MB
12. Week 11 Recurrent Neural Networks (RNNs) and Sequence Modeling/2. Day 1 Introduction to Sequence Modeling and RNNs.mp4 126.5 MB
2. Week 1 Python Programming Basics/3. Day 2 Control Flow in Python.mp4 123.2 MB
2. Week 1 Python Programming Basics/5. Day 4 Data Structures (Lists, Tuples, Dictionaries, Sets).mp4 115.1 MB
11. Week 10 Convolutional Neural Networks (CNNs)/8. Day 7 CNN Project – Image Classification on Fashion MNIST or CIFAR-10.mp4 104.2 MB
9. Week 8 Model Tuning and Optimization/4. Day 3 Advanced Hyperparameter Tuning with Bayesian Optimization.mp4 102.3 MB
3. Week 2 Data Science Essentials/7. Day 6 Data Visualization with Matplotlib and Seaborn.mp4 101.7 MB
10. Week 9 Neural Networks and Deep Learning Fundamentals/7. Day 6 Building Neural Networks with PyTorch.mp4 99.6 MB
14. Week 13 Transfer Learning and Fine-Tuning/3. Day 2 Transfer Learning in Computer Vision.mp4 99.3 MB
11. Week 10 Convolutional Neural Networks (CNNs)/2. Day 1 Introduction to Convolutional Neural Networks.mp4 99.0 MB
14. Week 13 Transfer Learning and Fine-Tuning/6. Day 5 Fine-Tuning Techniques in NLP.mp4 97.9 MB
4. Week 3 Mathematics for Machine Learning/6. Day 5 Probability Theory and Distributions.mp4 94.7 MB
6. Week 5 Introduction to Machine Learning/8. Day 7 Supervised Learning Mini Project.mp4 93.6 MB
5. Week 4 Probability and Statistics for Machine Learning/8. Day 7 Statistical Analysis Project – Analyzing Real-World Data.mp4 93.3 MB
12. Week 11 Recurrent Neural Networks (RNNs) and Sequence Modeling/3. Day 2 Understanding RNN Architecture and Backpropagation Through Time (BPTT).mp4 92.4 MB
3. Week 2 Data Science Essentials/5. Day 4 Data Cleaning and Preparation with Pandas.mp4 92.0 MB
6. Week 5 Introduction to Machine Learning/5. Day 4 Introduction to Classification and Logistic Regression.mp4 91.2 MB
12. Week 11 Recurrent Neural Networks (RNNs) and Sequence Modeling/6. Day 5 Text Preprocessing and Word Embeddings for RNNs.mp4 90.4 MB
11. Week 10 Convolutional Neural Networks (CNNs)/4. Day 3 Pooling Layers and Dimensionality Reduction.mp4 90.1 MB
11. Week 10 Convolutional Neural Networks (CNNs)/3. Day 2 Convolutional Layers and Filters.mp4 89.4 MB
2. Week 1 Python Programming Basics/6. Day 5 Working with Strings_edit.mp4 89.2 MB
2. Week 1 Python Programming Basics/4. Day 3 Functions and Modules.mp4 87.7 MB
3. Week 2 Data Science Essentials/8. Day 7 Exploratory Data Analysis (EDA) Project.mp4 86.5 MB
3. Week 2 Data Science Essentials/2. Day 1 Introduction to NumPy for Numerical Computing.mp4 85.8 MB
2. Week 1 Python Programming Basics/7. Day 6 File Handling.mp4 85.6 MB
9. Week 8 Model Tuning and Optimization/8. Day 7 Optimization Project – Building and Tuning a Final Model.mp4 85.5 MB
8. Week 7 Advanced Machine Learning Algorithms/8. Day 7 Ensemble Learning Project – Comparing Models on a Real Dataset.mp4 85.1 MB
11. Week 10 Convolutional Neural Networks (CNNs)/6. Day 5 Building CNN Architectures with PyTorch.mp4 84.3 MB
10. Week 9 Neural Networks and Deep Learning Fundamentals/8. Day 7 Neural Network Project – Image Classification on CIFAR-10.mp4 83.3 MB
10. Week 9 Neural Networks and Deep Learning Fundamentals/5. Day 4 Gradient Descent and Optimization Techniques.mp4 82.0 MB
14. Week 13 Transfer Learning and Fine-Tuning/4. Day 3 Fine-Tuning Techniques in Computer Vision.mp4 81.5 MB
3. Week 2 Data Science Essentials/3. Day 2 Advanced NumPy Operations.mp4 80.7 MB
4. Week 3 Mathematics for Machine Learning/2. Day 1 Linear Algebra Fundamentals.mp4 80.5 MB
13. Week 12 Transformers and Attention Mechanisms/4. Day 3 Self-Attention and Multi-Head Attention in Transformers.mp4 79.0 MB
13. Week 12 Transformers and Attention Mechanisms/7. Day 6 Advanced Transformers – BERT Variants and GPT-3.mp4 77.6 MB
2. Week 1 Python Programming Basics/2. Day 1 Introduction to Python and Development Setup.mp4 77.1 MB
13. Week 12 Transformers and Attention Mechanisms/5. Day 4 Positional Encoding and Feed-Forward Networks.mp4 76.8 MB
8. Week 7 Advanced Machine Learning Algorithms/6. Day 5 LightGBM and CatBoost.mp4 76.1 MB
7. Week 6 Feature Engineering and Model Evaluation/8. Day 7 Cross-Validation and Hyperparameter Tuning.mp4 75.4 MB
4. Week 3 Mathematics for Machine Learning/3. Day 2 Advanced Linear Algebra Concepts.mp4 74.1 MB
3. Week 2 Data Science Essentials/4. Day 3 Introduction to Pandas for Data Manipulation.mp4 73.9 MB
9. Week 8 Model Tuning and Optimization/7. Day 6 Automated Hyperparameter Tuning with GridSearchCV and RandomizedSearchCV.mp4 73.8 MB
13. Week 12 Transformers and Attention Mechanisms/6. Day 5 Hands-On with Pre-Trained Transformers – BERT and GPT.mp4 73.7 MB
8. Week 7 Advanced Machine Learning Algorithms/5. Day 4 Introduction to XGBoost.mp4 73.2 MB
10. Week 9 Neural Networks and Deep Learning Fundamentals/6. Day 5 Building Neural Networks with TensorFlow and Keras.mp4 72.4 MB
4. Week 3 Mathematics for Machine Learning/7. Day 6 Statistics Fundamentals.mp4 71.9 MB
5. Week 4 Probability and Statistics for Machine Learning/6. Day 5 Types of Hypothesis Tests.mp4 70.1 MB
11. Week 10 Convolutional Neural Networks (CNNs)/7. Day 6 Regularization and Data Augmentation for CNNs.mp4 70.0 MB
5. Week 4 Probability and Statistics for Machine Learning/2. Day 1 Probability Theory and Random Variables.mp4 69.7 MB
14. Week 13 Transfer Learning and Fine-Tuning/8. Day 7 Transfer Learning Project – Fine-Tuning for a Custom Task.mp4 69.4 MB
13. Week 12 Transformers and Attention Mechanisms/8. Day 7 Transformer Project – Text Summarization or Translation.mp4 69.2 MB
13. Week 12 Transformers and Attention Mechanisms/3. Day 2 Introduction to Transformers Architecture.mp4 68.9 MB
4. Week 3 Mathematics for Machine Learning/4. Day 3 Calculus for Machine Learning (Derivatives).mp4 68.2 MB
7. Week 6 Feature Engineering and Model Evaluation/6. Day 5 Creating and Transforming Features.mp4 67.8 MB
12. Week 11 Recurrent Neural Networks (RNNs) and Sequence Modeling/8. Day 7 RNN Project – Text Generation or Sentiment Analysis.mp4 67.0 MB
11. Week 10 Convolutional Neural Networks (CNNs)/5. Day 4 Building CNN Architectures with Keras and TensorFlow.mp4 66.8 MB
5. Week 4 Probability and Statistics for Machine Learning/7. Day 6 Correlation and Regression Analysis.mp4 65.7 MB
5. Week 4 Probability and Statistics for Machine Learning/3. Day 2 Probability Distributions in Machine Learning.mp4 64.5 MB
6. Week 5 Introduction to Machine Learning/7. Day 6 k-Nearest Neighbors (k-NN) Algorithm.mp4 64.4 MB
14. Week 13 Transfer Learning and Fine-Tuning/5. Day 4 Transfer Learning in NLP.mp4 63.4 MB
7. Week 6 Feature Engineering and Model Evaluation/4. Day 3 Encoding Categorical Variables.mp4 62.6 MB
8. Week 7 Advanced Machine Learning Algorithms/7. Day 6 Handling Imbalanced Data.mp4 62.3 MB
4. Week 3 Mathematics for Machine Learning/5. Day 4 Calculus for Machine Learning (Integrals and Optimization).mp4 62.1 MB
7. Week 6 Feature Engineering and Model Evaluation/5. Day 4 Feature Selection Techniques.mp4 61.2 MB
7. Week 6 Feature Engineering and Model Evaluation/3. Day 2 Data Scaling and Normalization.mp4 61.2 MB
10. Week 9 Neural Networks and Deep Learning Fundamentals/2. Day 1 Introduction to Deep Learning and Neural Networks.mp4 60.6 MB
8. Week 7 Advanced Machine Learning Algorithms/4. Day 3 Boosting and Gradient Boosting.mp4 60.2 MB
9. Week 8 Model Tuning and Optimization/3. Day 2 Grid Search and Random Search.mp4 60.2 MB
6. Week 5 Introduction to Machine Learning/6. Day 5 Model Evaluation and Cross-Validation.mp4 59.8 MB
6. Week 5 Introduction to Machine Learning/3. Day 2 Introduction to Supervised Learning and Regression Models.mp4 59.7 MB
7. Week 6 Feature Engineering and Model Evaluation/7. Day 6 Model Evaluation Techniques.mp4 58.8 MB
5. Week 4 Probability and Statistics for Machine Learning/4. Day 3 Statistical Inference - Estimation and Confidence Intervals.mp4 58.8 MB
10. Week 9 Neural Networks and Deep Learning Fundamentals/4. Day 3 Loss Functions and Backpropagation.mp4 57.8 MB
6. Week 5 Introduction to Machine Learning/2. Day 1 Machine Learning Basics and Terminology.mp4 57.8 MB
13. Week 12 Transformers and Attention Mechanisms/2. Day 1 Introduction to Attention Mechanisms.mp4 57.2 MB
4. Week 3 Mathematics for Machine Learning/8. Day 7 Math-Driven Mini Project – Linear Regression from Scratch.mp4 57.2 MB
3. Week 2 Data Science Essentials/6. Day 5 Data Aggregation and Grouping in Pandas.mp4 56.9 MB
12. Week 11 Recurrent Neural Networks (RNNs) and Sequence Modeling/4. Day 3 Long Short-Term Memory (LSTM) Networks.mp4 56.3 MB
8. Week 7 Advanced Machine Learning Algorithms/2. Day 1 Introduction to Ensemble Learning.mp4 56.2 MB
14. Week 13 Transfer Learning and Fine-Tuning/7. Day 6 Domain Adaptation and Transfer Learning Challenges.mp4 55.6 MB
14. Week 13 Transfer Learning and Fine-Tuning/2. Day 1 Introduction to Transfer Learning.mp4 55.3 MB
10. Week 9 Neural Networks and Deep Learning Fundamentals/3. Day 2 Forward Propagation and Activation Functions.mp4 55.0 MB
7. Week 6 Feature Engineering and Model Evaluation/2. Day 1 Introduction to Feature Engineering.mp4 54.3 MB
8. Week 7 Advanced Machine Learning Algorithms/3. Day 2 Bagging and Random Forests.mp4 53.5 MB
9. Week 8 Model Tuning and Optimization/2. Day 1 Introduction to Hyperparameter Tuning.mp4 51.0 MB
9. Week 8 Model Tuning and Optimization/5. Day 4 Regularization Techniques for Model Optimization.mp4 49.4 MB
9. Week 8 Model Tuning and Optimization/6. Day 5 Cross-Validation and Model Evaluation Techniques.mp4 48.6 MB
5. Week 4 Probability and Statistics for Machine Learning/5. Day 4 Hypothesis Testing and P-Values.mp4 43.8 MB
1. Introduction to Course/2.2 Code_files.zip 30.4 MB
12. Week 11 Recurrent Neural Networks (RNNs) and Sequence Modeling/5. Day 4 Gated Recurrent Units (GRUs).mp4 26.0 MB
1. Introduction to Course/1. Introduction to AI Engineering Masterclass From Zero to AI Hero.mp4 16.1 MB
9. Week 8 Model Tuning and Optimization/1. Introduction to Week 8 Model Tuning and Optimization.mp4 3.4 MB
12. Week 11 Recurrent Neural Networks (RNNs) and Sequence Modeling/1. Introduction to Week 11 Recurrent Neural Networks (RNNs) and Sequence Modeling.mp4 3.2 MB
13. Week 12 Transformers and Attention Mechanisms/1. Introduction to Week 12 Transformers and Attention Mechanisms.mp4 3.1 MB
11. Week 10 Convolutional Neural Networks (CNNs)/1. Introduction to Week 10 Convolutional Neural Networks (CNNs).mp4 3.1 MB
10. Week 9 Neural Networks and Deep Learning Fundamentals/1. Introduction to Week 9 Neural Networks and Deep Learning Fundamentals.mp4 3.0 MB
6. Week 5 Introduction to Machine Learning/1. Introduction to Week 5 Introduction to Machine Learning.mp4 3.0 MB
5. Week 4 Probability and Statistics for Machine Learning/1. Introduction to Week 4 Probability and Statistics for Machine Learning.mp4 2.9 MB
14. Week 13 Transfer Learning and Fine-Tuning/1. Introduction to Week 13 Transfer Learning and Fine-Tuning.mp4 2.9 MB
3. Week 2 Data Science Essentials/1. Introduction to Week 2 Data Science Essentials.mp4 2.8 MB
4. Week 3 Mathematics for Machine Learning/1. Introduction to Week 3 Mathematics for Machine Learning.mp4 2.7 MB
7. Week 6 Feature Engineering and Model Evaluation/1. Introduction to Week 6 Feature Engineering and Model Evaluation.mp4 2.7 MB
8. Week 7 Advanced Machine Learning Algorithms/1. Introduction to Week 7 Advanced Machine Learning Algorithms.mp4 2.6 MB
2. Week 1 Python Programming Basics/1. Introduction to Week 1 Python Programming Basics.mp4 2.5 MB
1. Introduction to Course/2.1 AIBootcamp.pdf 2.4 MB
12. Week 11 Recurrent Neural Networks (RNNs) and Sequence Modeling/7. Day 6 Sequence-to-Sequence Models and Applications.mp4.idx2 1.7 MB
6. Week 5 Introduction to Machine Learning/4. Day 3 Advanced Regression Models – Polynomial Regression and Regularization.mp4.idx2 1.4 MB
12. Week 11 Recurrent Neural Networks (RNNs) and Sequence Modeling/2. Day 1 Introduction to Sequence Modeling and RNNs.mp4.idx2 1.3 MB
11. Week 10 Convolutional Neural Networks (CNNs)/8. Day 7 CNN Project – Image Classification on Fashion MNIST or CIFAR-10.mp4.idx2 1.1 MB
3. Week 2 Data Science Essentials/7. Day 6 Data Visualization with Matplotlib and Seaborn.mp4.idx2 1.1 MB
9. Week 8 Model Tuning and Optimization/4. Day 3 Advanced Hyperparameter Tuning with Bayesian Optimization.mp4.idx2 1.1 MB
10. Week 9 Neural Networks and Deep Learning Fundamentals/7. Day 6 Building Neural Networks with PyTorch.mp4.idx2 1.1 MB
14. Week 13 Transfer Learning and Fine-Tuning/3. Day 2 Transfer Learning in Computer Vision.mp4.idx2 1.1 MB
11. Week 10 Convolutional Neural Networks (CNNs)/2. Day 1 Introduction to Convolutional Neural Networks.mp4.idx2 1.1 MB
14. Week 13 Transfer Learning and Fine-Tuning/6. Day 5 Fine-Tuning Techniques in NLP.mp4.idx2 1.0 MB
6. Week 5 Introduction to Machine Learning/8. Day 7 Supervised Learning Mini Project.mp4.idx2 1.0 MB
4. Week 3 Mathematics for Machine Learning/6. Day 5 Probability Theory and Distributions.mp4.idx2 1.0 MB
5. Week 4 Probability and Statistics for Machine Learning/8. Day 7 Statistical Analysis Project – Analyzing Real-World Data.mp4.idx2 1.0 MB
12. Week 11 Recurrent Neural Networks (RNNs) and Sequence Modeling/3. Day 2 Understanding RNN Architecture and Backpropagation Through Time (BPTT).mp4.idx2 1010.9 KB
3. Week 2 Data Science Essentials/5. Day 4 Data Cleaning and Preparation with Pandas.mp4.idx2 1008.1 KB
6. Week 5 Introduction to Machine Learning/5. Day 4 Introduction to Classification and Logistic Regression.mp4.idx2 1002.0 KB
12. Week 11 Recurrent Neural Networks (RNNs) and Sequence Modeling/6. Day 5 Text Preprocessing and Word Embeddings for RNNs.mp4.idx2 990.6 KB
11. Week 10 Convolutional Neural Networks (CNNs)/4. Day 3 Pooling Layers and Dimensionality Reduction.mp4.idx2 987.3 KB
11. Week 10 Convolutional Neural Networks (CNNs)/3. Day 2 Convolutional Layers and Filters.mp4.idx2 980.6 KB
3. Week 2 Data Science Essentials/8. Day 7 Exploratory Data Analysis (EDA) Project.mp4.idx2 953.2 KB
3. Week 2 Data Science Essentials/2. Day 1 Introduction to NumPy for Numerical Computing.mp4.idx2 940.1 KB
9. Week 8 Model Tuning and Optimization/8. Day 7 Optimization Project – Building and Tuning a Final Model.mp4.idx2 937.5 KB
8. Week 7 Advanced Machine Learning Algorithms/8. Day 7 Ensemble Learning Project – Comparing Models on a Real Dataset.mp4.idx2 931.5 KB
11. Week 10 Convolutional Neural Networks (CNNs)/6. Day 5 Building CNN Architectures with PyTorch.mp4.idx2 924.7 KB
10. Week 9 Neural Networks and Deep Learning Fundamentals/8. Day 7 Neural Network Project – Image Classification on CIFAR-10.mp4.idx2 913.0 KB
10. Week 9 Neural Networks and Deep Learning Fundamentals/5. Day 4 Gradient Descent and Optimization Techniques.mp4.idx2 899.8 KB
14. Week 13 Transfer Learning and Fine-Tuning/4. Day 3 Fine-Tuning Techniques in Computer Vision.mp4.idx2 897.0 KB
3. Week 2 Data Science Essentials/3. Day 2 Advanced NumPy Operations.mp4.idx2 887.9 KB
4. Week 3 Mathematics for Machine Learning/2. Day 1 Linear Algebra Fundamentals.mp4.idx2 881.5 KB
13. Week 12 Transformers and Attention Mechanisms/4. Day 3 Self-Attention and Multi-Head Attention in Transformers.mp4.idx2 865.9 KB
13. Week 12 Transformers and Attention Mechanisms/7. Day 6 Advanced Transformers – BERT Variants and GPT-3.mp4.idx2 850.7 KB
13. Week 12 Transformers and Attention Mechanisms/5. Day 4 Positional Encoding and Feed-Forward Networks.mp4.idx2 838.9 KB
8. Week 7 Advanced Machine Learning Algorithms/6. Day 5 LightGBM and CatBoost.mp4.idx2 838.2 KB
7. Week 6 Feature Engineering and Model Evaluation/8. Day 7 Cross-Validation and Hyperparameter Tuning.mp4.idx2 825.9 KB
3. Week 2 Data Science Essentials/4. Day 3 Introduction to Pandas for Data Manipulation.mp4.idx2 813.6 KB
4. Week 3 Mathematics for Machine Learning/3. Day 2 Advanced Linear Algebra Concepts.mp4.idx2 812.2 KB
8. Week 7 Advanced Machine Learning Algorithms/5. Day 4 Introduction to XGBoost.mp4.idx2 809.7 KB
13. Week 12 Transformers and Attention Mechanisms/6. Day 5 Hands-On with Pre-Trained Transformers – BERT and GPT.mp4.idx2 808.4 KB
9. Week 8 Model Tuning and Optimization/7. Day 6 Automated Hyperparameter Tuning with GridSearchCV and RandomizedSearchCV.mp4.idx2 802.6 KB
10. Week 9 Neural Networks and Deep Learning Fundamentals/6. Day 5 Building Neural Networks with TensorFlow and Keras.mp4.idx2 796.2 KB
4. Week 3 Mathematics for Machine Learning/7. Day 6 Statistics Fundamentals.mp4.idx2 788.4 KB
5. Week 4 Probability and Statistics for Machine Learning/6. Day 5 Types of Hypothesis Tests.mp4.idx2 769.9 KB
11. Week 10 Convolutional Neural Networks (CNNs)/7. Day 6 Regularization and Data Augmentation for CNNs.mp4.idx2 768.6 KB
5. Week 4 Probability and Statistics for Machine Learning/2. Day 1 Probability Theory and Random Variables.mp4.idx2 765.2 KB
13. Week 12 Transformers and Attention Mechanisms/8. Day 7 Transformer Project – Text Summarization or Translation.mp4.idx2 764.2 KB
13. Week 12 Transformers and Attention Mechanisms/3. Day 2 Introduction to Transformers Architecture.mp4.idx2 754.6 KB
4. Week 3 Mathematics for Machine Learning/4. Day 3 Calculus for Machine Learning (Derivatives).mp4.idx2 748.6 KB
7. Week 6 Feature Engineering and Model Evaluation/6. Day 5 Creating and Transforming Features.mp4.idx2 747.3 KB
12. Week 11 Recurrent Neural Networks (RNNs) and Sequence Modeling/8. Day 7 RNN Project – Text Generation or Sentiment Analysis.mp4.idx2 738.5 KB
11. Week 10 Convolutional Neural Networks (CNNs)/5. Day 4 Building CNN Architectures with Keras and TensorFlow.mp4.idx2 732.0 KB
5. Week 4 Probability and Statistics for Machine Learning/7. Day 6 Correlation and Regression Analysis.mp4.idx2 719.6 KB
6. Week 5 Introduction to Machine Learning/7. Day 6 k-Nearest Neighbors (k-NN) Algorithm.mp4.idx2 715.7 KB
5. Week 4 Probability and Statistics for Machine Learning/3. Day 2 Probability Distributions in Machine Learning.mp4.idx2 707.5 KB
14. Week 13 Transfer Learning and Fine-Tuning/5. Day 4 Transfer Learning in NLP.mp4.idx2 700.5 KB
7. Week 6 Feature Engineering and Model Evaluation/4. Day 3 Encoding Categorical Variables.mp4.idx2 692.1 KB
8. Week 7 Advanced Machine Learning Algorithms/7. Day 6 Handling Imbalanced Data.mp4.idx2 688.4 KB
4. Week 3 Mathematics for Machine Learning/5. Day 4 Calculus for Machine Learning (Integrals and Optimization).mp4.idx2 679.2 KB
7. Week 6 Feature Engineering and Model Evaluation/5. Day 4 Feature Selection Techniques.mp4.idx2 674.0 KB
7. Week 6 Feature Engineering and Model Evaluation/3. Day 2 Data Scaling and Normalization.mp4.idx2 673.4 KB
10. Week 9 Neural Networks and Deep Learning Fundamentals/2. Day 1 Introduction to Deep Learning and Neural Networks.mp4.idx2 668.2 KB
9. Week 8 Model Tuning and Optimization/3. Day 2 Grid Search and Random Search.mp4.idx2 665.9 KB
8. Week 7 Advanced Machine Learning Algorithms/4. Day 3 Boosting and Gradient Boosting.mp4.idx2 664.3 KB
6. Week 5 Introduction to Machine Learning/6. Day 5 Model Evaluation and Cross-Validation.mp4.idx2 659.6 KB
7. Week 6 Feature Engineering and Model Evaluation/7. Day 6 Model Evaluation Techniques.mp4.idx2 652.2 KB
6. Week 5 Introduction to Machine Learning/3. Day 2 Introduction to Supervised Learning and Regression Models.mp4.idx2 651.0 KB
5. Week 4 Probability and Statistics for Machine Learning/4. Day 3 Statistical Inference - Estimation and Confidence Intervals.mp4.idx2 645.6 KB
6. Week 5 Introduction to Machine Learning/2. Day 1 Machine Learning Basics and Terminology.mp4.idx2 642.2 KB
10. Week 9 Neural Networks and Deep Learning Fundamentals/4. Day 3 Loss Functions and Backpropagation.mp4.idx2 639.4 KB
13. Week 12 Transformers and Attention Mechanisms/2. Day 1 Introduction to Attention Mechanisms.mp4.idx2 629.4 KB
3. Week 2 Data Science Essentials/6. Day 5 Data Aggregation and Grouping in Pandas.mp4.idx2 624.3 KB
8. Week 7 Advanced Machine Learning Algorithms/2. Day 1 Introduction to Ensemble Learning.mp4.idx2 620.8 KB
12. Week 11 Recurrent Neural Networks (RNNs) and Sequence Modeling/4. Day 3 Long Short-Term Memory (LSTM) Networks.mp4.idx2 620.4 KB
4. Week 3 Mathematics for Machine Learning/8. Day 7 Math-Driven Mini Project – Linear Regression from Scratch.mp4.idx2 620.0 KB
14. Week 13 Transfer Learning and Fine-Tuning/7. Day 6 Domain Adaptation and Transfer Learning Challenges.mp4.idx2 613.2 KB
14. Week 13 Transfer Learning and Fine-Tuning/2. Day 1 Introduction to Transfer Learning.mp4.idx2 612.6 KB
10. Week 9 Neural Networks and Deep Learning Fundamentals/3. Day 2 Forward Propagation and Activation Functions.mp4.idx2 606.3 KB
7. Week 6 Feature Engineering and Model Evaluation/2. Day 1 Introduction to Feature Engineering.mp4.idx2 597.3 KB
8. Week 7 Advanced Machine Learning Algorithms/3. Day 2 Bagging and Random Forests.mp4.idx2 592.4 KB
9. Week 8 Model Tuning and Optimization/2. Day 1 Introduction to Hyperparameter Tuning.mp4.idx2 567.7 KB
9. Week 8 Model Tuning and Optimization/5. Day 4 Regularization Techniques for Model Optimization.mp4.idx2 547.2 KB
9. Week 8 Model Tuning and Optimization/6. Day 5 Cross-Validation and Model Evaluation Techniques.mp4.idx2 535.9 KB
5. Week 4 Probability and Statistics for Machine Learning/5. Day 4 Hypothesis Testing and P-Values.mp4.idx2 483.7 KB
12. Week 11 Recurrent Neural Networks (RNNs) and Sequence Modeling/5. Day 4 Gated Recurrent Units (GRUs).mp4.idx2 293.5 KB
12. Week 11 Recurrent Neural Networks (RNNs) and Sequence Modeling/7. Day 6 Sequence-to-Sequence Models and Applications RUS.srt 78.9 KB
2. Week 1 Python Programming Basics/8. Day 7 Pythonic Code and Project Work RUS.srt 74.5 KB
2. Week 1 Python Programming Basics/3. Day 2 Control Flow in Python RUS.srt 65.3 KB
2. Week 1 Python Programming Basics/5. Day 4 Data Structures (Lists, Tuples, Dictionaries, Sets) RUS.srt 63.5 KB
12. Week 11 Recurrent Neural Networks (RNNs) and Sequence Modeling/2. Day 1 Introduction to Sequence Modeling and RNNs RUS.srt 61.1 KB
6. Week 5 Introduction to Machine Learning/4. Day 3 Advanced Regression Models – Polynomial Regression and Regularization RUS.srt 58.8 KB
14. Week 13 Transfer Learning and Fine-Tuning/6. Day 5 Fine-Tuning Techniques in NLP RUS.srt 51.4 KB
12. Week 11 Recurrent Neural Networks (RNNs) and Sequence Modeling/7. Day 6 Sequence-to-Sequence Models and Applications.srt 51.3 KB
3. Week 2 Data Science Essentials/5. Day 4 Data Cleaning and Preparation with Pandas RUS.srt 49.9 KB
2. Week 1 Python Programming Basics/8. Day 7 Pythonic Code and Project Work.srt 48.7 KB
3. Week 2 Data Science Essentials/7. Day 6 Data Visualization with Matplotlib and Seaborn RUS.srt 47.9 KB
11. Week 10 Convolutional Neural Networks (CNNs)/2. Day 1 Introduction to Convolutional Neural Networks RUS.srt 47.8 KB
10. Week 9 Neural Networks and Deep Learning Fundamentals/7. Day 6 Building Neural Networks with PyTorch RUS.srt 47.7 KB
6. Week 5 Introduction to Machine Learning/8. Day 7 Supervised Learning Mini Project RUS.srt 47.4 KB
14. Week 13 Transfer Learning and Fine-Tuning/3. Day 2 Transfer Learning in Computer Vision RUS.srt 47.1 KB
2. Week 1 Python Programming Basics/6. Day 5 Working with Strings RUS.srt 47.0 KB
2. Week 1 Python Programming Basics/7. Day 6 File Handling RUS.srt 46.9 KB
5. Week 4 Probability and Statistics for Machine Learning/8. Day 7 Statistical Analysis Project – Analyzing Real-World Data RUS.srt 45.6 KB
2. Week 1 Python Programming Basics/4. Day 3 Functions and Modules RUS.srt 45.4 KB
11. Week 10 Convolutional Neural Networks (CNNs)/8. Day 7 CNN Project – Image Classification on Fashion MNIST or CIFAR-10 RUS.srt 44.7 KB
6. Week 5 Introduction to Machine Learning/5. Day 4 Introduction to Classification and Logistic Regression RUS.srt 44.3 KB
9. Week 8 Model Tuning and Optimization/4. Day 3 Advanced Hyperparameter Tuning with Bayesian Optimization RUS.srt 43.7 KB
2. Week 1 Python Programming Basics/3. Day 2 Control Flow in Python.srt 43.3 KB
11. Week 10 Convolutional Neural Networks (CNNs)/3. Day 2 Convolutional Layers and Filters RUS.srt 42.8 KB
3. Week 2 Data Science Essentials/3. Day 2 Advanced NumPy Operations RUS.srt 42.7 KB
13. Week 12 Transformers and Attention Mechanisms/7. Day 6 Advanced Transformers – BERT Variants and GPT-3 RUS.srt 42.6 KB
12. Week 11 Recurrent Neural Networks (RNNs) and Sequence Modeling/6. Day 5 Text Preprocessing and Word Embeddings for RNNs RUS.srt 42.4 KB
3. Week 2 Data Science Essentials/8. Day 7 Exploratory Data Analysis (EDA) Project RUS.srt 42.3 KB
2. Week 1 Python Programming Basics/2. Day 1 Introduction to Python and Development Setup RUS.srt 42.0 KB
3. Week 2 Data Science Essentials/2. Day 1 Introduction to NumPy for Numerical Computing RUS.srt 41.7 KB
4. Week 3 Mathematics for Machine Learning/6. Day 5 Probability Theory and Distributions RUS.srt 41.6 KB
11. Week 10 Convolutional Neural Networks (CNNs)/4. Day 3 Pooling Layers and Dimensionality Reduction RUS.srt 41.5 KB
12. Week 11 Recurrent Neural Networks (RNNs) and Sequence Modeling/3. Day 2 Understanding RNN Architecture and Backpropagation Through Time (BPTT) RUS.srt 41.4 KB
2. Week 1 Python Programming Basics/5. Day 4 Data Structures (Lists, Tuples, Dictionaries, Sets).srt 40.9 KB
14. Week 13 Transfer Learning and Fine-Tuning/4. Day 3 Fine-Tuning Techniques in Computer Vision RUS.srt 40.5 KB
13. Week 12 Transformers and Attention Mechanisms/6. Day 5 Hands-On with Pre-Trained Transformers – BERT and GPT RUS.srt 40.3 KB
4. Week 3 Mathematics for Machine Learning/2. Day 1 Linear Algebra Fundamentals RUS.srt 39.9 KB
3. Week 2 Data Science Essentials/4. Day 3 Introduction to Pandas for Data Manipulation RUS.srt 39.3 KB
12. Week 11 Recurrent Neural Networks (RNNs) and Sequence Modeling/2. Day 1 Introduction to Sequence Modeling and RNNs.srt 38.7 KB
11. Week 10 Convolutional Neural Networks (CNNs)/6. Day 5 Building CNN Architectures with PyTorch RUS.srt 38.6 KB
9. Week 8 Model Tuning and Optimization/8. Day 7 Optimization Project – Building and Tuning a Final Model RUS.srt 38.5 KB
7. Week 6 Feature Engineering and Model Evaluation/8. Day 7 Cross-Validation and Hyperparameter Tuning RUS.srt 37.7 KB
10. Week 9 Neural Networks and Deep Learning Fundamentals/8. Day 7 Neural Network Project – Image Classification on CIFAR-10 RUS.srt 37.6 KB
6. Week 5 Introduction to Machine Learning/4. Day 3 Advanced Regression Models – Polynomial Regression and Regularization.srt 37.3 KB
14. Week 13 Transfer Learning and Fine-Tuning/8. Day 7 Transfer Learning Project – Fine-Tuning for a Custom Task RUS.srt 37.1 KB
13. Week 12 Transformers and Attention Mechanisms/5. Day 4 Positional Encoding and Feed-Forward Networks RUS.srt 37.1 KB
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