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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