12.55 GB | 00:16:53 | mp4 | 1280X720 | 16:9
Genre:eLearning |Language:English

Files Included :
1 -Course Introduction (9.19 MB)
2 -Machine Learning Introduction (31.98 MB)
3 -Install Anaconda and Python on Windows (54.45 MB)
4 -Install Anaconda in Linux (23.58 MB)
5 -Jupyter Notebook Introduction and Keyboard Shortcuts (102.94 MB)
1 -Logistic Regression Introduction (20.23 MB)
10 -Data Types Correction and Mapping (67.11 MB)
11 -One-Hot Encoding (61.51 MB)
12 -Train Test Split (54.19 MB)
13 -Model Building Training and Evaluation (76.12 MB)
14 -Feature Selection - Recursive Feature Elimination (140.86 MB)
15 -Accuracy, F1-Score, P, R, AUC ROC Curve Part 1 (43.43 MB)
16 -Accuracy, F1-Score, P, R, AUC ROC Curve Part 2 (51.75 MB)
17 -Accuracy, F1-Score, P, R, AUC ROC Curve Part 3 (57.37 MB)
18 -ROC Curve and AUC Part 1 (78.28 MB)
19 -ROC Curve and AUC Part 2 (51.29 MB)
2 -Sigmoid Function (11.59 MB)
20 -ROC Curve and AUC Part 3 (73.49 MB)
3 -Decision Boundary (10.72 MB)
4 -Titanic Dataset Introduction (56.23 MB)
5 -Dataset Loading (65.44 MB)
6 -EDA - Heatmap and Density Plot (49.25 MB)
7 -Missing Age Imputation Part 1 (52.04 MB)
8 -Missing Age Imputation Part 2 (90.37 MB)
9 -Imputation of Missing Embark Town (67.08 MB)
1 -SVM Introduction (25.08 MB)
10 -Linear SVM Model on Scaled Feature (56.58 MB)
11 -Polynomial, Sigmoid, RBF Kernels in SVM (37.36 MB)
2 -SVM Kernels (28.4 MB)
3 -Breast Cancer Dataset Introduction (63.93 MB)
4 -Dataset Loading (37.67 MB)
5 -Cancer Data Visualization Part 1 (56.64 MB)
6 -Cancer Data Visualization Part 2 (114.49 MB)
7 -Data Standardization (45.47 MB)
8 -Train Test Split (37.17 MB)
9 -Linear SVM Model Building and Training (76.09 MB)
1 -Cross Validation Regularization and Hyperparameter Optimization Introduction (28.35 MB)
10 -K-Fold and LeaveOneOut Cross Validation (66.9 MB)
11 -Grid Search Hypyerparameter Tuning (80.81 MB)
12 -Random Grid Search Hyperparameter Tuning (29.11 MB)
2 -ML Model Training Process (39.91 MB)
3 -Breast Cancer Dataset Loading (59.77 MB)
4 -Data Visualization (72.34 MB)
5 -Train Test Split (40.14 MB)
6 -Linear Regression and SVM Model Training (35.46 MB)
7 -Regularization Introduction (56.61 MB)
8 -Manual Hyperparameter Adjustment (74.24 MB)
9 -Types of Cross Validation (42.03 MB)
1 -KNN Introduction (26.04 MB)
2 -How KNN Works (43.66 MB)
3 -Wine Dataset Laoding (42.01 MB)
4 -Data Visualization (66.7 MB)
5 -Train Test Split and Standardization (45.81 MB)
6 -KNN Model Building and Training (18.98 MB)
7 -Hyperparameter Tuning (53.95 MB)
8 -Pros and Cons of KNN (10.78 MB)
1 -Decision Tree Introduction (34.27 MB)
10 -Diabetes Dataset Loading (66.59 MB)
11 -Decision Tree Regression (50.12 MB)
2 -How Decision Tree Works (43.97 MB)
3 -What is Attribute Selection Measures - ASM (42.75 MB)
4 -Dataset Loading (38.69 MB)
5 -Dataset Visualization (64.24 MB)
6 -Train Test Split (20.36 MB)
7 -Model Training and Evaluation (27.2 MB)
8 -Tree Visualization (36.16 MB)
9 -Hyperparameter Optimization (33.56 MB)
1 -Ensemble Learning Bagging and Boosting Introduction (37.24 MB)
2 -Random Forest Introduction (35.52 MB)
3 -Dataset Introduction (34.1 MB)
4 -Data Visualization (74.34 MB)
5 -Train Test Split and One-Hot Encoding (22.83 MB)
6 -Random Forest Classifier Training and Evaluation (59.5 MB)
7 -Data Loading for Random Forest Regression (66.64 MB)
8 -Random Forest Regression Model Building (19.57 MB)
9 -Hyperparameter Optimization (36.81 MB)
1 -Boosting Algorithms Introduction (55.49 MB)
10 -CatBoost Hyperparameter Optimization (76.8 MB)
2 -Heart-Disease Dataset Understanding (84.2 MB)
3 -Data Visualization Part 1 (73.81 MB)
4 -Train Test Split (30.59 MB)
5 -AdaBoost Model Training (46.49 MB)
6 -AdaBoost Hyperparameter Tuning (28.79 MB)
7 -XGBoost Introduction (29.7 MB)
8 -XGBoost Model Training and Hyperparameter Tuning (63.96 MB)
9 -CatBoost Model Training (39.91 MB)
1 -Introduction to Unsupervised Learning (34.82 MB)
10 -Clusters Visualization (78.18 MB)
11 -Decision Boundary Visualization (139.93 MB)
12 -Putting Everything Together (117.18 MB)
13 -Selecting Optimum Number of Clusters (55.51 MB)
14 -Clustering for Annual Income vs Spending Score (53.84 MB)
15 -3D Clustering Part 1 (36.82 MB)
16 -3D Clustering Part 2 (62.67 MB)
2 -Introduction to K-Means (43.81 MB)
3 -How to Choose Best Number of Clusters (50.48 MB)
4 -K-Means Clustering with Scikit-Learn (28.19 MB)
5 -Application of Unsupervised Learning (39.91 MB)
6 -Customers Data Loading (34.74 MB)
7 -Data Visualization (76.06 MB)
8 -K-Means Clustering Data Preparation (55.23 MB)
9 -K-Means Clustering for Age and Spending Score (40.35 MB)
1 -DBSCAN Introduction (46.82 MB)
2 -Generate Dataset (19.22 MB)
3 -DBSCAN Clustering (46.97 MB)
4 -Spectral Clustering (59.31 MB)
5 -Spectral Clustering Coding (30.05 MB)
1 -Hierarchical Clustering Introduction (23.42 MB)
2 -Important Terms in Hierarchical Clustering (26.96 MB)
3 -Stock Market Data Loading (47.29 MB)
4 -Hierarchical Clustering Coding (31.63 MB)
1 -Arithmatic Operations in Python (40.76 MB)
10 -10 Set (29.47 MB)
11 -Dictionary (31.28 MB)
12 -Conditional Statements - If Else (38.27 MB)
13 -While Loops (23.25 MB)
14 -For Loops (32.89 MB)
15 -Functions (43.03 MB)
16 -Working with Date and Time (61.33 MB)
17 -File Handling Read and Write (65.61 MB)
2 -Data Types in Python (28.27 MB)
3 -Variable Casting (21.86 MB)
4 -Strings Operation in Python (39.04 MB)
5 -String Slicing in Python (23.54 MB)
6 -String Formatting and Modification (29.84 MB)
7 -Boolean Variables and Evaluation (15.43 MB)
8 -List in Python (37.54 MB)
9 -Tuple in Python (27.74 MB)
1 -PCA Introduction (21.49 MB)
10 -Classification Comparison with and without PCA (51.27 MB)
2 -How PCA is Done (56.9 MB)
3 -MNIST Dataset Loading and Understanding (56.22 MB)
4 -PCA Applications (10.94 MB)
5 -PCA Coding (63.63 MB)
6 -PCA Compression Analysis (25.54 MB)
7 -Data Reconstruction (104.85 MB)
8 -Choosing Right Number of the Principle Components (56.42 MB)
9 -Data Reconstruction with 95% Information (34.11 MB)
1 -What is Neuron (20.86 MB)
10 -Customer Churn Dataset Loading (25.98 MB)
11 -Data Visualization Part 1 (50.23 MB)
12 -Data Visualization Part 2 (107.27 MB)
13 -Data Preprocessing (36.39 MB)
14 -Import Neural Networks APIs (37.02 MB)
15 -How to Get Input Shape and Class Weights (21.17 MB)
16 -Neural Network Model Building (60.89 MB)
17 -Model Summary Explanation (48.79 MB)
18 -Model Training (56.3 MB)
19 -Model Evaluation (16.1 MB)
2 -Multi-Layer Perceptron (55.15 MB)
20 -Model Save and Load (23.64 MB)
21 -Prediction on Real-Life Data (50.9 MB)
3 -Shallow vs Deep Neural Networks (13.87 MB)
4 -Activation Function (40.35 MB)
5 -What is Back Propagation (79.42 MB)
6 -Optimizers in Deep Learning (52.04 MB)
7 -Steps to Build Neural Network (64.09 MB)
8 -Install TensorfFlow in Windows (67.97 MB)
9 -Install TensorFlow in Linux (69.46 MB)
1 -Introduction to NLP (22.55 MB)
10 -Pair Plot (41.94 MB)
11 -Train Test Split (8.74 MB)
12 -TF-IDF Vectorization (34.68 MB)
13 -Model Evaluation and Prediction on Real Data (22.25 MB)
14 -Model Load and Store (22.06 MB)
2 -What are Key NLP Techniques (39.55 MB)
3 -Overview of NLP Tools (64.52 MB)
4 -Common Challenges in NLP (19.14 MB)
5 -Bag of Words - The Simples Word Embedding Technique (27.29 MB)
6 -Term Frequency - Inverse Document Frequency (TF-IDF) (20.01 MB)
7 -Load Spam Dataset (18.56 MB)
8 -Text Preprocessing (45.87 MB)
9 -Feature Engineering (33.71 MB)
1 -Numpy Introduction - Create Numpy Array (35.9 MB)
10 -Concatenation and Sorting (36.47 MB)
2 -Array Indexing and Slicing (48.72 MB)
3 -Numpy Data Types (52.86 MB)
4 -np nan and np inf (24.89 MB)
5 -Statistical Operations (18.84 MB)
6 -Shape(), Reshape(), Ravel(), Flatten() (20.53 MB)
7 -arange(), linspace(), range(), random(), zeros(), and ones() (55.01 MB)
8 -Where (28.54 MB)
9 -Numpy Array Read and Write (50.46 MB)
1 -Pandas Series Introduction Part 1 (33.66 MB)
10 -Arithmetic Operations (22.96 MB)
11 -NULL Values Handling (42.24 MB)
12 -DataFrame Data Filtering Part 1 (63.8 MB)
13 -DataFrame Data Filtering Part 2 (47.11 MB)
14 -14 Handling Unique and Duplicated Values (51.21 MB)
15 -Retrive Rows by Index Label (46.05 MB)
16 -Replace Cell Values (35.78 MB)
17 -Rename, Delete Index and Columns (31.11 MB)
18 -Lambda Apply (60.55 MB)
19 -Pandas Groupby (67.19 MB)
2 -Pandas Series Introduction Part 2 (22.38 MB)
20 -Groupby Multiple Columns (55.8 MB)
21 -Merging, Joining, and Concatenation Part 1 (16.45 MB)
22 -Concatenation (28.93 MB)
23 -Merge and Join (66.77 MB)
24 -Working with Datetime (57.38 MB)
25 -Read Stock Data from YAHOO Finance (28.41 MB)
3 -Pandas Series Read From File (30.77 MB)
4 -Apply Pythons Built in Functions to Series (48.34 MB)
5 -apply() for Pandas Series (33.21 MB)
6 -Pandas DataFrame Creation from Scratch (31.23 MB)
7 -Read Files as DataFrame (56.15 MB)
8 -Columns Manipulation Part 1 (45.44 MB)
9 -Columns Manipulation Part 2 (47.52 MB)
1 -Matplotlib Introduction (31.99 MB)
10 -Subplot Part 2 (70.94 MB)
11 -Subplots (65.68 MB)
12 -Creating a Zoomed Sub-Figure of a Figure (59.32 MB)
13 -xlim and ylim, legend, grid, xticks, yticks (42.7 MB)
14 -Pie Chart and Figure Save (58.17 MB)
2 -Matplotlib Line Plot Part 1 (51.84 MB)
3 -IMDB Movie Revenue Line Plot Part 1 (29.57 MB)
4 -IMDB Movie Revenue Line Plot Part 2 (23.14 MB)
5 -Line Plot Rank vs Runtime Votes Metascore (23.39 MB)
6 -Line Styling and Putting Labels (40.98 MB)
7 -Scatter, Bar, and Histogram Plot Part 1 (53.31 MB)
8 -Scatter, Bar, and Histogram Plot Part 2 (66.37 MB)
9 -Subplot Part 1 (58.66 MB)
1 -Introduction (39.41 MB)
10 -cat plot (27.78 MB)
11 -Box Plot (10.55 MB)
12 -Boxen Plot (20.7 MB)
13 -Violin Plot (29.95 MB)
14 -Bar Plot (17.03 MB)
15 -Point Plot (9.29 MB)
16 -Joint Plot (11.58 MB)
17 -Pair Plot (24.11 MB)
18 -Regression Plot (13 MB)
19 -Controlling Ploted Figure Aesthetics (31.74 MB)
2 -Scatter Plot (22.14 MB)
3 -Hue, Style and Size Part1 (10.8 MB)
4 -Hue, Style and Size Part2 (26.82 MB)
5 -Line Plot Part 1 (17.45 MB)
6 -Line Plot Part 2 (50.77 MB)
7 -Line Plot Part 3 (42.31 MB)
8 -Subplots (31.67 MB)
9 -sns lineplot() and sns scatterplot() (28.01 MB)
1 -IRIS Dataset Introduction (26.62 MB)
10 -Hexbin Plot (41.18 MB)
11 -Pie Chart (81.36 MB)
12 -Scatter Matrix and Subplots (62.6 MB)
2 -Load IRIS Dataset (36.55 MB)
3 -Line Plot (59 MB)
4 -Secondary Axis (66.78 MB)
5 -Bar and Barh Plot (51.79 MB)
6 -Stacked Bar Plot (50.93 MB)
7 -Histogram (78.36 MB)
8 -Box Plot (44.29 MB)
9 -Area and Scatter Plot (74.67 MB)
1 -Introduction to Plotly and Cufflinks (31.09 MB)
2 -Plotly Line Plot (69.66 MB)
3 -Scatter Plot (27.96 MB)
4 -Stacked Bar Plot (81.62 MB)
5 -Box and Area Plot (30.55 MB)
6 -3D Plot (63.22 MB)
7 -Hist Plot, Bubble Plot and Heatmap (78.68 MB)
1 -Linear Regression Introduction (33.36 MB)
10 -Exploratory Data Analysis- Pair Plot (81.09 MB)
11 -Exploratory Data Analysis- Hist Plot (33.54 MB)
12 -Exploratory Data Analysis- Heatmap (46.33 MB)
13 -Train Test Split and Model Training (44.88 MB)
14 -How to Evaluate the Regression Model Performance (62.15 MB)
15 -Plot True House Price vs Predicted Price (44.41 MB)
16 -Plotting Learning Curves Part 1 (37.33 MB)
17 -Plotting Learning Curves Part 2 (55.97 MB)
18 -Machine Learning Model Interpretability- Residuals Plot (35.28 MB)
19 -Machine Learning Model Interpretability- Prediction Error Plot (23.3 MB)
2 -Regression Examples (33.82 MB)
3 -Types of Linear Regression (42.14 MB)
4 -Assessing the performance of the model (37.53 MB)
5 -Bias-Variance tradeoff (52.56 MB)
6 -What is sklearn and train-test-split (39.68 MB)
7 -Python Package Upgrade and Import (36.03 MB)
8 -Load Boston Housing Dataset (32.73 MB)
9 -Dataset Analysis (52.5 MB)]
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