26.36 GB | 00:15:24 | mp4 | 1920X1076 | 16:9
Genre:eLearning |Language:English

Files Included :
1 Introduction (35.71 MB)
2 What is Artificial Intelligence (163.06 MB)
3 Grand Search Auto (140.49 MB)
4 Explore the Frontier (61.61 MB)
5 Depth-First Search (69.64 MB)
6 Breadth-First Search (98.84 MB)
7 Greedy-Best First and A Search (73.91 MB)
1 Introduction (100.21 MB)
2 What is Feature Engineering (78.94 MB)
3 Handling Missing Data (104.77 MB)
4 Handling Outliers (70.61 MB)
5 One Hot Encoding (61.03 MB)
6 Define, Split and Scale Features (88.09 MB)
7 Measuring Survival Accuracy (32.44 MB)
1 Introduction (68.45 MB)
2 From Regression to Classification (85.54 MB)
3 Logistic Regression (65.45 MB)
4 Decision Trees (56.07 MB)
5 Random Forests (105.68 MB)
6 Support Vector Machines (43.69 MB)
7 Perceptrons (51.17 MB)
1 Introduction (150.75 MB)
2 What is Logistic Regression (64.34 MB)
3 The Sigmoid Formula and Function (49.36 MB)
4 Logistic Regression in 4 lines of Code (81.89 MB)
5 Implement Logistic Regression - Part 1 Data Preprocessing, Cleaning, and Encoding (160.35 MB)
6 Part 2 Implement Logistic Regression and Measure Performance (83.35 MB)
1 Introduction (85.85 MB)
2 Concepts Video (132.14 MB)
3 Entropy, Information Gain, and Gini Impurity (63.26 MB)
4 Import Libraries, Feature Engineering and One-Hot Encoding (155.72 MB)
5 Train, Test, Predict, and Measure Model Performance (121.13 MB)
1 Introduction (68.97 MB)
2 What is a Random Forest (58.31 MB)
3 Random Forest Concepts (73.81 MB)
4 Import Libraries, Feature Engineering and One-Hot Encoding (104.23 MB)
5 Train, Test, Predict, and Measure Model Performance (79.29 MB)
6 Bonus Hyperparameter Tuning Video (29.95 MB)
1 Introduction (90.19 MB)
2 What is Overfitting (78.82 MB)
3 Three Options for Handling Overfitting (75.07 MB)
4 Overfitting for Classification (60.34 MB)
5 Comparing Cost Functions (68.38 MB)
6 Perform Logistic Regression with Regularization (70.72 MB)
1 Introduction (78.5 MB)
2 What is a Support Vector Machine (73.25 MB)
3 Optimal Hyperplanes and the Margin (67.19 MB)
4 Data Loading and PreProcessing (151.34 MB)
5 Build and Evaluate the Model (73.41 MB)
6 Breast Cancer Wisconsin (Diagnostic) Dataset (42.67 MB)
1 Introduction (178.44 MB)
2 What is K-Nearest Neighbors (71.58 MB)
3 KNN vs Other Classifiers (68.2 MB)
4 What is Imbalanced Data (51.97 MB)
5 Data Loading and EDA (50.96 MB)
6 Data PreProcessing (81.02 MB)
7 Build and Evaluate the Model (80.38 MB)
1 Introduction (81.75 MB)
2 Neurons as the building blocks of neural networks (33.98 MB)
3 Perceptrons As Artificial Neurons (67.34 MB)
4 How Activation Functions Work (53.02 MB)
5 Why Linearly Separable Data Is Key (54.82 MB)
6 Build A Simple Binary Perceptron Classifier (111.95 MB)
7 Challenge Complete The Perceptron Function