20.82 GB | 13min 7s | mp4 | 1280X720 | 16:9
Genre:eLearning |Language:English
Files Included :
1 Why should you learn Python.mp4 (65.68 MB)
10 Identity and Membership Operators.mp4 (39.22 MB)
12 Quiz Solution.mp4 (34.21 MB)
13 String Formatting.mp4 (51.35 MB)
14 String Methods.mp4 (43.29 MB)
15 User Input.mp4 (41.04 MB)
17 Quiz Solution.mp4 (53.11 MB)
18 If, elif, and else.mp4 (65.9 MB)
19 For and While.mp4 (53.07 MB)
2 Installing Python and Jupyter Notebook.mp4 (33.48 MB)
20 Break and Continue.mp4 (40.72 MB)
22 Quiz Solution.mp4 (49.04 MB)
3 Naming Convention for Variables.mp4 (102.24 MB)
4 Built in Data Types and Type Casting.mp4 (119.86 MB)
5 Scope of Variables.mp4 (77.16 MB)
7 Quiz Solution.mp4 (46.52 MB)
8 Arithmetic and Assignment Operators.mp4 (78.04 MB)
9 Comparison, Logical, and Bitwise Operators.mp4 (62.41 MB)
1 Introduction to Logistic Regression.mp4 (106.4 MB)
10 Industry Relevance of Logistic Regression.mp4 (59.89 MB)
2 Implementing Logistic Regression using Sklearn.mp4 (87.01 MB)
3 Feature Selection using RFECV.mp4 (42.15 MB)
4 Hyperparameter tuning using Grid search.mp4 (58.75 MB)
5 Applying Cross Validation.mp4 (56.73 MB)
6 How to analyze performance of a classification model.mp4 (146.18 MB)
7 Using accuracy score to analyze the performance of model.mp4 (55.54 MB)
8 Using ROC-AUC score to analyze the performance of model.mp4 (147.63 MB)
9 Real time prediction using logistic regression.mp4 (74.65 MB)
1 Introduction to Support Vector machines.mp4 (108.17 MB)
2 The kermel trick for support vector machine.mp4 (70.38 MB)
3 Implementing support vector machine using sklearn.mp4 (67.43 MB)
4 Introduction to K nearest neighbors.mp4 (104.32 MB)
5 Implementing KNN using Sklearn.mp4 (33.23 MB)
6 Introduction to Naive Bayes.mp4 (174.72 MB)
7 Implementing Naive Bayes using sklearn.mp4 (61.96 MB)
8 When should we apply SVM, KNN and Naive bayes.mp4 (69.77 MB)
1 Intuition for decision trees.mp4 (81.99 MB)
2 Attribute selection method- Gini Index and Entropy.mp4 (218.66 MB)
3 Advantages and Issues with Decision trees.mp4 (53.37 MB)
4 Implementing Decision tree using Sklearn.mp4 (35.8 MB)
5 Understanding the concept of Bagging.mp4 (65.99 MB)
6 Introduction to Random forest.mp4 (68.09 MB)
7 Understanding the parameters of Random forest.mp4 (53.66 MB)
8 Implementing random forest using Sklearn.mp4 (47.88 MB)
1 Understading the concept of boosting.mp4 (57.14 MB)
2 Intuition for Adaboost and Gradient Boosting.mp4 (153.3 MB)
3 Implementing AdaBoost using sklearn.mp4 (90.82 MB)
4 Implementing Gradient Boosting using sklearn.mp4 (66.93 MB)
5 Getting High level intuition for XGBoost.mp4 (41.07 MB)
6 Implementing XGBoost using sklearn.mp4 (65.14 MB)
7 Introudction to Ensembling techniques.mp4 (134.02 MB)
1 Why Imbalanced Data needs extra attention.mp4 (53.62 MB)
10 Implementing Synthetic Sampling using Imblearn.mp4 (57.45 MB)
11 Implementing Neighbors based Sampling using Imblearn.mp4 (64.02 MB)
12 Combination of Oversampling and Under sampling.mp4 (55.76 MB)
13 Implementing Ensemble Models for Imbalanced Data.mp4 (54.88 MB)
14 Introduction to XG Boost for Imbalanced Data.mp4 (43.54 MB)
15 Comparing the Results.mp4 (41.5 MB)
2 Using Resampling Techniques to Balance the Data.mp4 (70.55 MB)
3 Solving a Real World Problem.mp4 (56.98 MB)
4 Preparing the Data for Predictive Modelling.mp4 (57.93 MB)
5 Applying Logistic Regression using Sklearn.mp4 (71.14 MB)
6 Applying Random Forest using Sklearn.mp4 (42.65 MB)
8 Implementing Random Over Sampling using Imblearn.mp4 (54.41 MB)
9 Implementing Random Under Sampling using Imblearn.mp4 (57.54 MB)
1 Introduction to Clustering.mp4 (57.84 MB)
10 Clustering Multiple Dimensions.mp4 (50.01 MB)
12 Introduction to Hierarchal Clustering.mp4 (88.49 MB)
13 Introduction to Dendrograms.mp4 (41.78 MB)
14 Implementing Hierarchial Clustering.mp4 (52.35 MB)
15 Introduction to DBSCAN Clustering.mp4 (52.38 MB)
16 Implementing DBSCAN Clustering.mp4 (47.87 MB)
2 Types of Clustering.mp4 (65.18 MB)
3 Applications of Clustering.mp4 (55.95 MB)
5 Using the Elbow Method for Choosing the Best Value for K.mp4 (67.06 MB)
6 Introduction to K Means Clustering.mp4 (49.29 MB)
7 Solving a Real World Problem.mp4 (71 MB)
8 Implementing K Means on the Mall Dataset.mp4 (71.57 MB)
9 Using Silhouette Score to analyze the clusters.mp4 (96.34 MB)
1 Why High Dimensional Datasets are a Problem.mp4 (79.22 MB)
11 Introduction to Recursive Feature Selection.mp4 (56.62 MB)
12 Implementing Recursive Feature Selection.mp4 (50.92 MB)
13 Introduction the Boruta Algorithm.mp4 (52.48 MB)
14 Implementing the Boruta Algorithm.mp4 (43.2 MB)
16 Introduction to Principal Component Analysis.mp4 (73.79 MB)
17 Implementing PCA.mp4 (55.52 MB)
18 Introduction to t-SNE.mp4 (81.27 MB)
19 Implementing t-SNE.mp4 (36.11 MB)
2 Methods to solve the problem of High Dimensionality.mp4 (57.16 MB)
20 Introduction to Linear Discriminant Analysis.mp4 (48.9 MB)
21 Implementing LDA.mp4 (36.74 MB)
22 Difference between PCA, t-SNE, and LDA.mp4 (64.79 MB)
3 Solving a Real World Problem.mp4 (98.82 MB)
5 Introduction to Correlation using Heatmap.mp4 (71.4 MB)
6 Removing Highly Correlated Columns using Correlation.mp4 (48.87 MB)
8 Introduction to Variance Inflation Filtering.mp4 (48.66 MB)
9 Implementing VIF using statsmodel.mp4 (47.84 MB)
1 Introduction to Recommender systems.mp4 (40.53 MB)
11 Quiz Solution.mp4 (48.5 MB)
12 Introduction to Collaborative Filtering.mp4 (80.86 MB)
13 Preprocessing the Data for Collaborative Filtering.mp4 (72.39 MB)
14 Implementation of User Based Collaborative Filtering.mp4 (62.15 MB)
15 Interpreting the Results obtained from User Based Filtering.mp4 (63.59 MB)
16 Implementation of Item Based Collaborative Filtering.mp4 (63.55 MB)
18 Quiz Solution.mp4 (55.62 MB)
19 Introduction to SVD.mp4 (112.02 MB)
2 What are it's Use Cases.mp4 (45.05 MB)
20 Implementing SVD using Surprise.mp4 (40.63 MB)
21 Interpreting Results Obtained from SVD.mp4 (46.01 MB)
22 Comparing Content, and Collaborative Based Filtering.mp4 (61.99 MB)
24 Quiz Solution.mp4 (47.94 MB)
25 Case Study for Netflix.mp4 (56.38 MB)
26 Case Study for Youtube.mp4 (58.14 MB)
3 Types of Recommender Systems.mp4 (56.54 MB)
4 Evaluating Recommender Systems.mp4 (53.15 MB)
5 Introduction to Content Based Filtering.mp4 (59 MB)
6 Preprocessing the Data for Content Based Filtering.mp4 (76.67 MB)
7 Filtering Movies Based on Genres.mp4 (58.73 MB)
8 Introduction to Transactional Encoder.mp4 (63.39 MB)
9 Recommending Similar Movies to Watch.mp4 (56.31 MB)
1 What is a Time Series Data.mp4 (34.91 MB)
10 Time Series Decomposition.mp4 (89.93 MB)
11 Splitting Time Series Data.mp4 (63.5 MB)
13 Basic Forecasting Techniques.mp4 (55.48 MB)
14 Metrics for Time series Forecasting.mp4 (78.7 MB)
15 Simple Moving Averages.mp4 (50.11 MB)
16 Simple Exponential Smoothing.mp4 (66.62 MB)
17 Holt and Holt Winter Exponential Smoothing.mp4 (73.13 MB)
19 Introduction to Auto Regressive Models.mp4 (34.71 MB)
2 Types of Forecasting.mp4 (45.3 MB)
20 Checking for Stationarity Part 1.mp4 (65 MB)
21 Checking for Stationarity using Statistical Methods Part 2.mp4 (75.44 MB)
22 Checking for Stationary Implementation.mp4 (38.1 MB)
23 Converting Non-Stationary Series into Stationary.mp4 (48.1 MB)
24 Converting Non-Stationary Series into Stationary Implementation.mp4 (48.17 MB)
25 Auto Correlation and Partial Correlation.mp4 (76.85 MB)
26 Auto Correlation and Partial Correlation Implementation.mp4 (38.48 MB)
27 The Simple Auto Regressive Model.mp4 (63.42 MB)
28 The Simple Auto Regressive Model Implementation.mp4 (64.98 MB)
29 Moving Average Model.mp4 (35.3 MB)
3 Regression Vs Time Series.mp4 (82.95 MB)
30 Moving Average Model Implementation.mp4 (23.23 MB)
32 Understanding ARMA Model.mp4 (56.79 MB)
33 Implementing ARMA Model.mp4 (48.21 MB)
34 Understanding ARIMA Model.mp4 (55.87 MB)
35 Implementing ARIMA Model.mp4 (33.2 MB)
36 Understanding SARIMA Model.mp4 (69.94 MB)
37 Implementing SARIMA Model.mp4 (38.13 MB)
39 Understanding ARIMAX Model.mp4 (66.51 MB)
4 Applications of Time Series.mp4 (47.29 MB)
40 Implementing ARIMAX Model.mp4 (44.76 MB)
41 Understanding SARIMAX Model.mp4 (43.84 MB)
42 Implementing SARIMAX Model.mp4 (59.96 MB)
44 How to Choose the Right Model.mp4 (35.14 MB)
45 Choosing the Right for Model Smaller Datasets.mp4 (52.3 MB)
46 Choosing the Right Model for Larger Datasets.mp4 (36.31 MB)
47 Best Practices while Choosing a Time series Model.mp4 (43.02 MB)
49 Why do we Evaluate Performance.mp4 (31.77 MB)
5 Components of Time Series.mp4 (51.96 MB)
50 Mean Forecast Error.mp4 (52.91 MB)
51 Mean Absolute Error.mp4 (35.56 MB)
52 Mean Absolute Percentage Error.mp4 (29.76 MB)
53 Root Mean Squared Error.mp4 (29.34 MB)
7 Getting Time Series data.mp4 (71.08 MB)
8 Handling Missing Values.mp4 (116.47 MB)
9 Handling Outlier Values.mp4 (64.43 MB)
1 Setting up the Environment.mp4 (41.71 MB)
10 Feature Engineering.mp4 (50.43 MB)
11 Categorical Encoding.mp4 (37.44 MB)
12 Data Processing.mp4 (67.65 MB)
13 Feature Scaling.mp4 (42.28 MB)
14 Predictive Modelling.mp4 (44.65 MB)
15 Performance Analysis.mp4 (77.16 MB)
16 Improvements Possible.mp4 (41.87 MB)
17 Major Takeaways from the Project.mp4 (28.99 MB)
2 Understanding the Dataset.mp4 (95.88 MB)
3 Understanding the Problem Statement.mp4 (59.78 MB)
4 Performing Descriptive Statistics.mp4 (61.68 MB)
5 Missing Values Treatment.mp4 (38.66 MB)
6 Outlier Values Treatment.mp4 (42.49 MB)
7 Univariate Analysis.mp4 (53.13 MB)
8 Bivariate Analysis.mp4 (37.16 MB)
9 Multivariate Analysis.mp4 (39.94 MB)
1 Differences between Lists and Tuples.mp4 (48.66 MB)
11 Quiz Solution.mp4 (38.27 MB)
12 Introduction to Stacks and Queues.mp4 (48.69 MB)
13 Implementing Stacks and Queues using Lists.mp4 (36.5 MB)
14 Implementing Stacks and Queues using Deque.mp4 (41.6 MB)
16 Quiz Solution.mp4 (39.51 MB)
17 Time Complexity.mp4 (120.13 MB)
18 Linear Search.mp4 (95.52 MB)
19 Binary Search.mp4 (109.54 MB)
2 Operations on Lists.mp4 (44.4 MB)
20 Bubble Sort.mp4 (75.55 MB)
21 Insertion and Selection Sort.mp4 (120 MB)
22 Merge Sort.mp4 (115.44 MB)
24 Quiz Solution.mp4 (73.24 MB)
3 Operations on Tuples.mp4 (27.44 MB)
5 Quiz Solution.mp4 (37.09 MB)
6 Introduction to Dictionaries.mp4 (66.83 MB)
7 Nested Dictionaries.mp4 (60.55 MB)
8 Introduction to Sets.mp4 (75.49 MB)
9 Set Operations.mp4 (58.59 MB)
1 Setting up the Environment.mp4 (50.12 MB)
10 Applying Gradient Boosting Model.mp4 (70.38 MB)
11 Creating Ensembles of Models.mp4 (57.07 MB)
12 Comparing Performance of these Models.mp4 (36.54 MB)
13 More things to Try.mp4 (48.69 MB)
14 Major Takeaways from the Project.mp4 (57.64 MB)
2 Understanding the Dataset.mp4 (104.05 MB)
3 Understanding the Problem Statement.mp4 (61.8 MB)
4 Performing Univariate Analysis.mp4 (89.75 MB)
5 Performing Bivariate Analysis.mp4 (71.46 MB)
6 Performing Multivariate Analysis.mp4 (85.97 MB)
7 Preparing the data for Modelling.mp4 (90.86 MB)
8 Applying Linear Regression Model.mp4 (128.08 MB)
9 Applying Random Forest Model.mp4 (54.39 MB)
1 Understanding the Problem Statement.mp4 (45.49 MB)
10 Applying Logistic Regression.mp4 (52.39 MB)
11 Applying Gradient Boosting.mp4 (38.62 MB)
12 Summary.mp4 (44.17 MB)
2 Setting up the Environment.mp4 (68.6 MB)
3 Understanding the Dataset.mp4 (41.13 MB)
4 Performing Descriptive Statistics.mp4 (75.32 MB)
5 Data Cleaning.mp4 (66.97 MB)
6 Univariate Data Visualizations.mp4 (65.17 MB)
7 Bivariate Data Analysis.mp4 (70.21 MB)
8 Preparing the Data for Modelling.mp4 (42.83 MB)
9 Applying Resampling.mp4 (56.96 MB)
1 Setting up the Environment.mp4 (46.43 MB)
10 Summarizing the Key-Points.mp4 (40.45 MB)
2 Understanding the Dataset.mp4 (55.18 MB)
3 Understanding the Problem Statement.mp4 (35.4 MB)
4 Performing Descriptive Statistics.mp4 (73.57 MB)
5 Analyzing Agricultural Conditions.mp4 (39.18 MB)
6 Clustering Similar Crops.mp4 (63.62 MB)
7 Visualizing the Hidden Patterns.mp4 (27.79 MB)
8 Predictive Modelling.mp4 (40.38 MB)
9 Real Time Predictions.mp4 (27.66 MB)
1 Introduction to Functions.mp4 (40.22 MB)
10 List, set, and Dictionary Comprehensions.mp4 (54.58 MB)
12 Quiz Solution.mp4 (40.26 MB)
13 Introduction to Aggregate Functions.mp4 (30.63 MB)
14 Introduction to Analytical Functions.mp4 (34.68 MB)
16 Quiz Solution.mp4 (38.19 MB)
17 Solving the Factorial Problem using Recursion.mp4 (55.38 MB)
18 Solving the Fibonacci Problem using Recursion.mp4 (62.68 MB)
2 Default Parameters in Functions.mp4 (53.96 MB)
20 Quiz Solution.mp4 (38.06 MB)
21 Introduction to Classes and Objects.mp4 (39.53 MB)
22 Inheritance.mp4 (32.49 MB)
23 Encapsulation.mp4 (62.2 MB)
24 Polymorphism.mp4 (46.25 MB)
26 Quiz Solution.mp4 (40.47 MB)
3 Positional Arguments.mp4 (32.11 MB)
4 Keyword Arguments.mp4 (36.24 MB)
5 Python Modules.mp4 (42.7 MB)
7 Quiz Solution.mp4 (47.69 MB)
8 Lambda Functions.mp4 (53.14 MB)
9 Filter, Map, and Zip Functions.mp4 (79.87 MB)
1 Introduction to datetime.mp4 (37.49 MB)
10 Sets for Regular Expressions.mp4 (56.13 MB)
12 Quiz Solution.mp4 (32.82 MB)
13 Array Creation using Numpy.mp4 (50.91 MB)
14 Mathematical Operations using Numpy.mp4 (36.44 MB)
15 Built-in Functions in Numpy.mp4 (39.99 MB)
17 Quiz Solution.mp4 (57.6 MB)
18 Reading Datasets using Pandas.mp4 (65.75 MB)
19 Plotting Data in Pandas.mp4 (35.74 MB)
2 The date and time class.mp4 (33.55 MB)
20 Indexing, Selecting, and Filtering Data using Pandas.mp4 (68.92 MB)
21 Merging and Concatenating DataFrames.mp4 (76.57 MB)
22 Lambda, Map, and Apply Functions.mp4 (37.2 MB)
24 Quiz Solution.mp4 (54.71 MB)
3 The datetime class.mp4 (22.57 MB)
4 The timedelta class.mp4 (19.36 MB)
6 Quiz Solution.mp4 (44.07 MB)
7 Meta Characters for Regular Expressions.mp4 (74.03 MB)
8 Built-in Functions for Regular Expressions.mp4 (37.57 MB)
9 Special Characters for Regular Expressions.mp4 (40.92 MB)
1 Causes and Impact of Missing Values.mp4 (64.37 MB)
10 Finding out Outliers from the Data.mp4 (63.24 MB)
11 Using Winsorization to deal with Outliers.mp4 (50.55 MB)
12 Deleting and Capping the Outliers.mp4 (60.76 MB)
13 Dealing with Outliers in a real-world scenario.mp4 (50.9 MB)
15 Quiz Solution.mp4 (56.09 MB)
16 Introduction to reindex, set index, reset index, and sort index Functions.mp4 (44.7 MB)
17 Introduction to Replace and Droplevel Function.mp4 (32.98 MB)
18 Introduction to Split and Strip Function.mp4 (37.82 MB)
19 Introduction to Stack, and Unstack Functions.mp4 (25.39 MB)
2 Types of Missing Values.mp4 (61.82 MB)
20 Introduction to Melt, Explode, and Squeeze Functions.mp4 (41.38 MB)
21 Data Cleaning on Big Mart Dataset.mp4 (38.3 MB)
22 Data Cleaning on Movie Dataset.mp4 (37.3 MB)
23 Data Cleaning on Melbourne Housing Dataset.mp4 (42.14 MB)
24 Data Cleaning on Naukri Dataset.mp4 (106.25 MB)
3 When should we delete the Missing values.mp4 (79.62 MB)
4 Imputing the Missing Values using the Business Logic.mp4 (73.91 MB)
5 Imputing Missing Values using MeanMedianMode.mp4 (55.96 MB)
6 Imputing Missing Values in a real-time scenario.mp4 (82.55 MB)
8 Quiz Solution.mp4 (49.16 MB)
9 How Outliers can be harmful for Machine Learning Models.mp4 (69.04 MB)
1 Univariate Analysis.mp4 (57.06 MB)
10 Statistical Charts.mp4 (38.38 MB)
11 Polar Charts.mp4 (29.3 MB)
12 Subplots.mp4 (34.8 MB)
13 3D Charts.mp4 (24.57 MB)
14 Waffle Charts.mp4 (29.36 MB)
15 Maps.mp4 (30.72 MB)
17 Quiz Solution.mp4 (48.84 MB)
18 Animation with Bubbleplot.mp4 (47.79 MB)
19 Animation with Facets.mp4 (26.71 MB)
2 Bivariate Analysis.mp4 (45 MB)
20 Animation with Scatter Maps.mp4 (22.65 MB)
21 Animation with Choropleth Maps.mp4 (30.58 MB)
23 Quiz Solution.mp4 (34.58 MB)
24 Introduction to Ipywidgets.mp4 (38.56 MB)
25 Interactive Univariate Analysis.mp4 (29.89 MB)
26 Interactive Bivariate Analysis.mp4 (33.86 MB)
27 Interactive Multivariate Analysis.mp4 (29.18 MB)
29 Quiz Solution.mp4 (53.83 MB)
3 Multivariate Analysis.mp4 (70.84 MB)
30 Sunburst Charts.mp4 (33.14 MB)
31 Parallel Co-ordinate Charts.mp4 (22.97 MB)
32 Funnel Charts.mp4 (39.14 MB)
33 Gantt Charts.mp4 (25.09 MB)
34 Ternary Charts.mp4 (20.37 MB)
35 Tree Maps.mp4 (21.46 MB)
36 Network Charts.mp4 (39.75 MB)
38 Quiz Solution.mp4 (38.52 MB)
5 Quiz Solution.mp4 (47.09 MB)
6 Scatter Plots.mp4 (45.16 MB)
7 Charts with Colorscale.mp4 (31.82 MB)
8 Bar, Line, and Area Charts.mp4 (48.54 MB)
9 Facet Grids.mp4 (37.93 MB)
1 Introduction to Feature Engineering.mp4 (60.04 MB)
10 Finding the Words, Characters, and Punctuation Count.mp4 (36.29 MB)
11 Counting Nouns and Verbs in the Text.mp4 (31.42 MB)
12 Counting Adjectives, Adverb, and Pronouns.mp4 (23.71 MB)
13 Introduction to Assign and Update Functions.mp4 (36.13 MB)
14 Introduction to at time and between time Functions.mp4 (30.23 MB)
15 Introduction to nlargest and nsmallest Functions.mp4 (35.33 MB)
16 Introduction to Expanding Function.mp4 (28.43 MB)
17 Introduction to Cumulative Functions.mp4 (31.11 MB)
19 Quiz Solution.mp4 (51.21 MB)
2 Removing Unnecessary Columns.mp4 (56.87 MB)
20 Feature Engineering on Employee Data.mp4 (57.14 MB)
21 Feature Engineering on FIFA Data.mp4 (44.76 MB)
22 Feature Engineering on Hotel Reviews.mp4 (35.06 MB)
23 Feature Engineering on Marketing Data.mp4 (58.59 MB)
24 Feature Engineering on Titanic Data.mp4 (49.63 MB)
26 Quiz Solution.mp4 (64.84 MB)
3 Decomposing Time and Date Features.mp4 (38.3 MB)
4 Decomposing Categorical Features.mp4 (38.28 MB)
5 Binning Numerical Features.mp4 (59.36 MB)
6 Aggregating Features.mp4 (56.88 MB)
7 Introduction to Feature Engineering on Text Data.mp4 (33.83 MB)
8 Reading and Summarizing the Text.mp4 (30.48 MB)
9 Finding the Length, Polarity and Subjectivity.mp4 (73.01 MB)
1 Types of Encoding Techniques.mp4 (60.89 MB)
10 Log transformation.mp4 (28.02 MB)
11 BoxCox transformation.mp4 (32.52 MB)
13 Train, Test and Validation Split.mp4 (44.24 MB)
14 Standardization and Normalization.mp4 (39.71 MB)
2 Label Encoding.mp4 (33.54 MB)
3 Feature Mapping for Ordinal Variables.mp4 (29.02 MB)
4 OneHot Encoding.mp4 (34.58 MB)
5 Binary and BaseN Encoding.mp4 (33.22 MB)
6 Mean and Frequency Encoding.mp4 (22.84 MB)
8 Introduction to Skewness and Normal Distribution.mp4 (37.55 MB)
9 Square and Cube Root Transformation.mp4 (39.42 MB)
1 Introduction to Linear Regression.mp4 (81.22 MB)
10 Industry relevance of linear regression.mp4 (49.88 MB)
2 Implementing Linear Regression using Sklearn.mp4 (73.45 MB)
3 Feature Selection using RFECV.mp4 (85.91 MB)
4 Data Transformation with Linear Regression.mp4 (57.52 MB)
5 Applying Cross Validation.mp4 (105.62 MB)
6 Analyzing the performance of Regression models.mp4 (108.97 MB)
7 R2 score and adjuted R2 score intuition.mp4 (107.03 MB)
8 MAE, RMSE, R2 and Adjusted R2 in code.mp4 (49 MB)
9 Applying real time prediction on our model.mp4 (107.61 MB)
10 Identity and Membership Operators.mp4 (39.22 MB)
12 Quiz Solution.mp4 (34.21 MB)
13 String Formatting.mp4 (51.35 MB)
14 String Methods.mp4 (43.29 MB)
15 User Input.mp4 (41.04 MB)
17 Quiz Solution.mp4 (53.11 MB)
18 If, elif, and else.mp4 (65.9 MB)
19 For and While.mp4 (53.07 MB)
2 Installing Python and Jupyter Notebook.mp4 (33.48 MB)
20 Break and Continue.mp4 (40.72 MB)
22 Quiz Solution.mp4 (49.04 MB)
3 Naming Convention for Variables.mp4 (102.24 MB)
4 Built in Data Types and Type Casting.mp4 (119.86 MB)
5 Scope of Variables.mp4 (77.16 MB)
7 Quiz Solution.mp4 (46.52 MB)
8 Arithmetic and Assignment Operators.mp4 (78.04 MB)
9 Comparison, Logical, and Bitwise Operators.mp4 (62.41 MB)
1 Introduction to Logistic Regression.mp4 (106.4 MB)
10 Industry Relevance of Logistic Regression.mp4 (59.89 MB)
2 Implementing Logistic Regression using Sklearn.mp4 (87.01 MB)
3 Feature Selection using RFECV.mp4 (42.15 MB)
4 Hyperparameter tuning using Grid search.mp4 (58.75 MB)
5 Applying Cross Validation.mp4 (56.73 MB)
6 How to analyze performance of a classification model.mp4 (146.18 MB)
7 Using accuracy score to analyze the performance of model.mp4 (55.54 MB)
8 Using ROC-AUC score to analyze the performance of model.mp4 (147.63 MB)
9 Real time prediction using logistic regression.mp4 (74.65 MB)
1 Introduction to Support Vector machines.mp4 (108.17 MB)
2 The kermel trick for support vector machine.mp4 (70.38 MB)
3 Implementing support vector machine using sklearn.mp4 (67.43 MB)
4 Introduction to K nearest neighbors.mp4 (104.32 MB)
5 Implementing KNN using Sklearn.mp4 (33.23 MB)
6 Introduction to Naive Bayes.mp4 (174.72 MB)
7 Implementing Naive Bayes using sklearn.mp4 (61.96 MB)
8 When should we apply SVM, KNN and Naive bayes.mp4 (69.77 MB)
1 Intuition for decision trees.mp4 (81.99 MB)
2 Attribute selection method- Gini Index and Entropy.mp4 (218.66 MB)
3 Advantages and Issues with Decision trees.mp4 (53.37 MB)
4 Implementing Decision tree using Sklearn.mp4 (35.8 MB)
5 Understanding the concept of Bagging.mp4 (65.99 MB)
6 Introduction to Random forest.mp4 (68.09 MB)
7 Understanding the parameters of Random forest.mp4 (53.66 MB)
8 Implementing random forest using Sklearn.mp4 (47.88 MB)
1 Understading the concept of boosting.mp4 (57.14 MB)
2 Intuition for Adaboost and Gradient Boosting.mp4 (153.3 MB)
3 Implementing AdaBoost using sklearn.mp4 (90.82 MB)
4 Implementing Gradient Boosting using sklearn.mp4 (66.93 MB)
5 Getting High level intuition for XGBoost.mp4 (41.07 MB)
6 Implementing XGBoost using sklearn.mp4 (65.14 MB)
7 Introudction to Ensembling techniques.mp4 (134.02 MB)
1 Why Imbalanced Data needs extra attention.mp4 (53.62 MB)
10 Implementing Synthetic Sampling using Imblearn.mp4 (57.45 MB)
11 Implementing Neighbors based Sampling using Imblearn.mp4 (64.02 MB)
12 Combination of Oversampling and Under sampling.mp4 (55.76 MB)
13 Implementing Ensemble Models for Imbalanced Data.mp4 (54.88 MB)
14 Introduction to XG Boost for Imbalanced Data.mp4 (43.54 MB)
15 Comparing the Results.mp4 (41.5 MB)
2 Using Resampling Techniques to Balance the Data.mp4 (70.55 MB)
3 Solving a Real World Problem.mp4 (56.98 MB)
4 Preparing the Data for Predictive Modelling.mp4 (57.93 MB)
5 Applying Logistic Regression using Sklearn.mp4 (71.14 MB)
6 Applying Random Forest using Sklearn.mp4 (42.65 MB)
8 Implementing Random Over Sampling using Imblearn.mp4 (54.41 MB)
9 Implementing Random Under Sampling using Imblearn.mp4 (57.54 MB)
1 Introduction to Clustering.mp4 (57.84 MB)
10 Clustering Multiple Dimensions.mp4 (50.01 MB)
12 Introduction to Hierarchal Clustering.mp4 (88.49 MB)
13 Introduction to Dendrograms.mp4 (41.78 MB)
14 Implementing Hierarchial Clustering.mp4 (52.35 MB)
15 Introduction to DBSCAN Clustering.mp4 (52.38 MB)
16 Implementing DBSCAN Clustering.mp4 (47.87 MB)
2 Types of Clustering.mp4 (65.18 MB)
3 Applications of Clustering.mp4 (55.95 MB)
5 Using the Elbow Method for Choosing the Best Value for K.mp4 (67.06 MB)
6 Introduction to K Means Clustering.mp4 (49.29 MB)
7 Solving a Real World Problem.mp4 (71 MB)
8 Implementing K Means on the Mall Dataset.mp4 (71.57 MB)
9 Using Silhouette Score to analyze the clusters.mp4 (96.34 MB)
1 Why High Dimensional Datasets are a Problem.mp4 (79.22 MB)
11 Introduction to Recursive Feature Selection.mp4 (56.62 MB)
12 Implementing Recursive Feature Selection.mp4 (50.92 MB)
13 Introduction the Boruta Algorithm.mp4 (52.48 MB)
14 Implementing the Boruta Algorithm.mp4 (43.2 MB)
16 Introduction to Principal Component Analysis.mp4 (73.79 MB)
17 Implementing PCA.mp4 (55.52 MB)
18 Introduction to t-SNE.mp4 (81.27 MB)
19 Implementing t-SNE.mp4 (36.11 MB)
2 Methods to solve the problem of High Dimensionality.mp4 (57.16 MB)
20 Introduction to Linear Discriminant Analysis.mp4 (48.9 MB)
21 Implementing LDA.mp4 (36.74 MB)
22 Difference between PCA, t-SNE, and LDA.mp4 (64.79 MB)
3 Solving a Real World Problem.mp4 (98.82 MB)
5 Introduction to Correlation using Heatmap.mp4 (71.4 MB)
6 Removing Highly Correlated Columns using Correlation.mp4 (48.87 MB)
8 Introduction to Variance Inflation Filtering.mp4 (48.66 MB)
9 Implementing VIF using statsmodel.mp4 (47.84 MB)
1 Introduction to Recommender systems.mp4 (40.53 MB)
11 Quiz Solution.mp4 (48.5 MB)
12 Introduction to Collaborative Filtering.mp4 (80.86 MB)
13 Preprocessing the Data for Collaborative Filtering.mp4 (72.39 MB)
14 Implementation of User Based Collaborative Filtering.mp4 (62.15 MB)
15 Interpreting the Results obtained from User Based Filtering.mp4 (63.59 MB)
16 Implementation of Item Based Collaborative Filtering.mp4 (63.55 MB)
18 Quiz Solution.mp4 (55.62 MB)
19 Introduction to SVD.mp4 (112.02 MB)
2 What are it's Use Cases.mp4 (45.05 MB)
20 Implementing SVD using Surprise.mp4 (40.63 MB)
21 Interpreting Results Obtained from SVD.mp4 (46.01 MB)
22 Comparing Content, and Collaborative Based Filtering.mp4 (61.99 MB)
24 Quiz Solution.mp4 (47.94 MB)
25 Case Study for Netflix.mp4 (56.38 MB)
26 Case Study for Youtube.mp4 (58.14 MB)
3 Types of Recommender Systems.mp4 (56.54 MB)
4 Evaluating Recommender Systems.mp4 (53.15 MB)
5 Introduction to Content Based Filtering.mp4 (59 MB)
6 Preprocessing the Data for Content Based Filtering.mp4 (76.67 MB)
7 Filtering Movies Based on Genres.mp4 (58.73 MB)
8 Introduction to Transactional Encoder.mp4 (63.39 MB)
9 Recommending Similar Movies to Watch.mp4 (56.31 MB)
1 What is a Time Series Data.mp4 (34.91 MB)
10 Time Series Decomposition.mp4 (89.93 MB)
11 Splitting Time Series Data.mp4 (63.5 MB)
13 Basic Forecasting Techniques.mp4 (55.48 MB)
14 Metrics for Time series Forecasting.mp4 (78.7 MB)
15 Simple Moving Averages.mp4 (50.11 MB)
16 Simple Exponential Smoothing.mp4 (66.62 MB)
17 Holt and Holt Winter Exponential Smoothing.mp4 (73.13 MB)
19 Introduction to Auto Regressive Models.mp4 (34.71 MB)
2 Types of Forecasting.mp4 (45.3 MB)
20 Checking for Stationarity Part 1.mp4 (65 MB)
21 Checking for Stationarity using Statistical Methods Part 2.mp4 (75.44 MB)
22 Checking for Stationary Implementation.mp4 (38.1 MB)
23 Converting Non-Stationary Series into Stationary.mp4 (48.1 MB)
24 Converting Non-Stationary Series into Stationary Implementation.mp4 (48.17 MB)
25 Auto Correlation and Partial Correlation.mp4 (76.85 MB)
26 Auto Correlation and Partial Correlation Implementation.mp4 (38.48 MB)
27 The Simple Auto Regressive Model.mp4 (63.42 MB)
28 The Simple Auto Regressive Model Implementation.mp4 (64.98 MB)
29 Moving Average Model.mp4 (35.3 MB)
3 Regression Vs Time Series.mp4 (82.95 MB)
30 Moving Average Model Implementation.mp4 (23.23 MB)
32 Understanding ARMA Model.mp4 (56.79 MB)
33 Implementing ARMA Model.mp4 (48.21 MB)
34 Understanding ARIMA Model.mp4 (55.87 MB)
35 Implementing ARIMA Model.mp4 (33.2 MB)
36 Understanding SARIMA Model.mp4 (69.94 MB)
37 Implementing SARIMA Model.mp4 (38.13 MB)
39 Understanding ARIMAX Model.mp4 (66.51 MB)
4 Applications of Time Series.mp4 (47.29 MB)
40 Implementing ARIMAX Model.mp4 (44.76 MB)
41 Understanding SARIMAX Model.mp4 (43.84 MB)
42 Implementing SARIMAX Model.mp4 (59.96 MB)
44 How to Choose the Right Model.mp4 (35.14 MB)
45 Choosing the Right for Model Smaller Datasets.mp4 (52.3 MB)
46 Choosing the Right Model for Larger Datasets.mp4 (36.31 MB)
47 Best Practices while Choosing a Time series Model.mp4 (43.02 MB)
49 Why do we Evaluate Performance.mp4 (31.77 MB)
5 Components of Time Series.mp4 (51.96 MB)
50 Mean Forecast Error.mp4 (52.91 MB)
51 Mean Absolute Error.mp4 (35.56 MB)
52 Mean Absolute Percentage Error.mp4 (29.76 MB)
53 Root Mean Squared Error.mp4 (29.34 MB)
7 Getting Time Series data.mp4 (71.08 MB)
8 Handling Missing Values.mp4 (116.47 MB)
9 Handling Outlier Values.mp4 (64.43 MB)
1 Setting up the Environment.mp4 (41.71 MB)
10 Feature Engineering.mp4 (50.43 MB)
11 Categorical Encoding.mp4 (37.44 MB)
12 Data Processing.mp4 (67.65 MB)
13 Feature Scaling.mp4 (42.28 MB)
14 Predictive Modelling.mp4 (44.65 MB)
15 Performance Analysis.mp4 (77.16 MB)
16 Improvements Possible.mp4 (41.87 MB)
17 Major Takeaways from the Project.mp4 (28.99 MB)
2 Understanding the Dataset.mp4 (95.88 MB)
3 Understanding the Problem Statement.mp4 (59.78 MB)
4 Performing Descriptive Statistics.mp4 (61.68 MB)
5 Missing Values Treatment.mp4 (38.66 MB)
6 Outlier Values Treatment.mp4 (42.49 MB)
7 Univariate Analysis.mp4 (53.13 MB)
8 Bivariate Analysis.mp4 (37.16 MB)
9 Multivariate Analysis.mp4 (39.94 MB)
1 Differences between Lists and Tuples.mp4 (48.66 MB)
11 Quiz Solution.mp4 (38.27 MB)
12 Introduction to Stacks and Queues.mp4 (48.69 MB)
13 Implementing Stacks and Queues using Lists.mp4 (36.5 MB)
14 Implementing Stacks and Queues using Deque.mp4 (41.6 MB)
16 Quiz Solution.mp4 (39.51 MB)
17 Time Complexity.mp4 (120.13 MB)
18 Linear Search.mp4 (95.52 MB)
19 Binary Search.mp4 (109.54 MB)
2 Operations on Lists.mp4 (44.4 MB)
20 Bubble Sort.mp4 (75.55 MB)
21 Insertion and Selection Sort.mp4 (120 MB)
22 Merge Sort.mp4 (115.44 MB)
24 Quiz Solution.mp4 (73.24 MB)
3 Operations on Tuples.mp4 (27.44 MB)
5 Quiz Solution.mp4 (37.09 MB)
6 Introduction to Dictionaries.mp4 (66.83 MB)
7 Nested Dictionaries.mp4 (60.55 MB)
8 Introduction to Sets.mp4 (75.49 MB)
9 Set Operations.mp4 (58.59 MB)
1 Setting up the Environment.mp4 (50.12 MB)
10 Applying Gradient Boosting Model.mp4 (70.38 MB)
11 Creating Ensembles of Models.mp4 (57.07 MB)
12 Comparing Performance of these Models.mp4 (36.54 MB)
13 More things to Try.mp4 (48.69 MB)
14 Major Takeaways from the Project.mp4 (57.64 MB)
2 Understanding the Dataset.mp4 (104.05 MB)
3 Understanding the Problem Statement.mp4 (61.8 MB)
4 Performing Univariate Analysis.mp4 (89.75 MB)
5 Performing Bivariate Analysis.mp4 (71.46 MB)
6 Performing Multivariate Analysis.mp4 (85.97 MB)
7 Preparing the data for Modelling.mp4 (90.86 MB)
8 Applying Linear Regression Model.mp4 (128.08 MB)
9 Applying Random Forest Model.mp4 (54.39 MB)
1 Understanding the Problem Statement.mp4 (45.49 MB)
10 Applying Logistic Regression.mp4 (52.39 MB)
11 Applying Gradient Boosting.mp4 (38.62 MB)
12 Summary.mp4 (44.17 MB)
2 Setting up the Environment.mp4 (68.6 MB)
3 Understanding the Dataset.mp4 (41.13 MB)
4 Performing Descriptive Statistics.mp4 (75.32 MB)
5 Data Cleaning.mp4 (66.97 MB)
6 Univariate Data Visualizations.mp4 (65.17 MB)
7 Bivariate Data Analysis.mp4 (70.21 MB)
8 Preparing the Data for Modelling.mp4 (42.83 MB)
9 Applying Resampling.mp4 (56.96 MB)
1 Setting up the Environment.mp4 (46.43 MB)
10 Summarizing the Key-Points.mp4 (40.45 MB)
2 Understanding the Dataset.mp4 (55.18 MB)
3 Understanding the Problem Statement.mp4 (35.4 MB)
4 Performing Descriptive Statistics.mp4 (73.57 MB)
5 Analyzing Agricultural Conditions.mp4 (39.18 MB)
6 Clustering Similar Crops.mp4 (63.62 MB)
7 Visualizing the Hidden Patterns.mp4 (27.79 MB)
8 Predictive Modelling.mp4 (40.38 MB)
9 Real Time Predictions.mp4 (27.66 MB)
1 Introduction to Functions.mp4 (40.22 MB)
10 List, set, and Dictionary Comprehensions.mp4 (54.58 MB)
12 Quiz Solution.mp4 (40.26 MB)
13 Introduction to Aggregate Functions.mp4 (30.63 MB)
14 Introduction to Analytical Functions.mp4 (34.68 MB)
16 Quiz Solution.mp4 (38.19 MB)
17 Solving the Factorial Problem using Recursion.mp4 (55.38 MB)
18 Solving the Fibonacci Problem using Recursion.mp4 (62.68 MB)
2 Default Parameters in Functions.mp4 (53.96 MB)
20 Quiz Solution.mp4 (38.06 MB)
21 Introduction to Classes and Objects.mp4 (39.53 MB)
22 Inheritance.mp4 (32.49 MB)
23 Encapsulation.mp4 (62.2 MB)
24 Polymorphism.mp4 (46.25 MB)
26 Quiz Solution.mp4 (40.47 MB)
3 Positional Arguments.mp4 (32.11 MB)
4 Keyword Arguments.mp4 (36.24 MB)
5 Python Modules.mp4 (42.7 MB)
7 Quiz Solution.mp4 (47.69 MB)
8 Lambda Functions.mp4 (53.14 MB)
9 Filter, Map, and Zip Functions.mp4 (79.87 MB)
1 Introduction to datetime.mp4 (37.49 MB)
10 Sets for Regular Expressions.mp4 (56.13 MB)
12 Quiz Solution.mp4 (32.82 MB)
13 Array Creation using Numpy.mp4 (50.91 MB)
14 Mathematical Operations using Numpy.mp4 (36.44 MB)
15 Built-in Functions in Numpy.mp4 (39.99 MB)
17 Quiz Solution.mp4 (57.6 MB)
18 Reading Datasets using Pandas.mp4 (65.75 MB)
19 Plotting Data in Pandas.mp4 (35.74 MB)
2 The date and time class.mp4 (33.55 MB)
20 Indexing, Selecting, and Filtering Data using Pandas.mp4 (68.92 MB)
21 Merging and Concatenating DataFrames.mp4 (76.57 MB)
22 Lambda, Map, and Apply Functions.mp4 (37.2 MB)
24 Quiz Solution.mp4 (54.71 MB)
3 The datetime class.mp4 (22.57 MB)
4 The timedelta class.mp4 (19.36 MB)
6 Quiz Solution.mp4 (44.07 MB)
7 Meta Characters for Regular Expressions.mp4 (74.03 MB)
8 Built-in Functions for Regular Expressions.mp4 (37.57 MB)
9 Special Characters for Regular Expressions.mp4 (40.92 MB)
1 Causes and Impact of Missing Values.mp4 (64.37 MB)
10 Finding out Outliers from the Data.mp4 (63.24 MB)
11 Using Winsorization to deal with Outliers.mp4 (50.55 MB)
12 Deleting and Capping the Outliers.mp4 (60.76 MB)
13 Dealing with Outliers in a real-world scenario.mp4 (50.9 MB)
15 Quiz Solution.mp4 (56.09 MB)
16 Introduction to reindex, set index, reset index, and sort index Functions.mp4 (44.7 MB)
17 Introduction to Replace and Droplevel Function.mp4 (32.98 MB)
18 Introduction to Split and Strip Function.mp4 (37.82 MB)
19 Introduction to Stack, and Unstack Functions.mp4 (25.39 MB)
2 Types of Missing Values.mp4 (61.82 MB)
20 Introduction to Melt, Explode, and Squeeze Functions.mp4 (41.38 MB)
21 Data Cleaning on Big Mart Dataset.mp4 (38.3 MB)
22 Data Cleaning on Movie Dataset.mp4 (37.3 MB)
23 Data Cleaning on Melbourne Housing Dataset.mp4 (42.14 MB)
24 Data Cleaning on Naukri Dataset.mp4 (106.25 MB)
3 When should we delete the Missing values.mp4 (79.62 MB)
4 Imputing the Missing Values using the Business Logic.mp4 (73.91 MB)
5 Imputing Missing Values using MeanMedianMode.mp4 (55.96 MB)
6 Imputing Missing Values in a real-time scenario.mp4 (82.55 MB)
8 Quiz Solution.mp4 (49.16 MB)
9 How Outliers can be harmful for Machine Learning Models.mp4 (69.04 MB)
1 Univariate Analysis.mp4 (57.06 MB)
10 Statistical Charts.mp4 (38.38 MB)
11 Polar Charts.mp4 (29.3 MB)
12 Subplots.mp4 (34.8 MB)
13 3D Charts.mp4 (24.57 MB)
14 Waffle Charts.mp4 (29.36 MB)
15 Maps.mp4 (30.72 MB)
17 Quiz Solution.mp4 (48.84 MB)
18 Animation with Bubbleplot.mp4 (47.79 MB)
19 Animation with Facets.mp4 (26.71 MB)
2 Bivariate Analysis.mp4 (45 MB)
20 Animation with Scatter Maps.mp4 (22.65 MB)
21 Animation with Choropleth Maps.mp4 (30.58 MB)
23 Quiz Solution.mp4 (34.58 MB)
24 Introduction to Ipywidgets.mp4 (38.56 MB)
25 Interactive Univariate Analysis.mp4 (29.89 MB)
26 Interactive Bivariate Analysis.mp4 (33.86 MB)
27 Interactive Multivariate Analysis.mp4 (29.18 MB)
29 Quiz Solution.mp4 (53.83 MB)
3 Multivariate Analysis.mp4 (70.84 MB)
30 Sunburst Charts.mp4 (33.14 MB)
31 Parallel Co-ordinate Charts.mp4 (22.97 MB)
32 Funnel Charts.mp4 (39.14 MB)
33 Gantt Charts.mp4 (25.09 MB)
34 Ternary Charts.mp4 (20.37 MB)
35 Tree Maps.mp4 (21.46 MB)
36 Network Charts.mp4 (39.75 MB)
38 Quiz Solution.mp4 (38.52 MB)
5 Quiz Solution.mp4 (47.09 MB)
6 Scatter Plots.mp4 (45.16 MB)
7 Charts with Colorscale.mp4 (31.82 MB)
8 Bar, Line, and Area Charts.mp4 (48.54 MB)
9 Facet Grids.mp4 (37.93 MB)
1 Introduction to Feature Engineering.mp4 (60.04 MB)
10 Finding the Words, Characters, and Punctuation Count.mp4 (36.29 MB)
11 Counting Nouns and Verbs in the Text.mp4 (31.42 MB)
12 Counting Adjectives, Adverb, and Pronouns.mp4 (23.71 MB)
13 Introduction to Assign and Update Functions.mp4 (36.13 MB)
14 Introduction to at time and between time Functions.mp4 (30.23 MB)
15 Introduction to nlargest and nsmallest Functions.mp4 (35.33 MB)
16 Introduction to Expanding Function.mp4 (28.43 MB)
17 Introduction to Cumulative Functions.mp4 (31.11 MB)
19 Quiz Solution.mp4 (51.21 MB)
2 Removing Unnecessary Columns.mp4 (56.87 MB)
20 Feature Engineering on Employee Data.mp4 (57.14 MB)
21 Feature Engineering on FIFA Data.mp4 (44.76 MB)
22 Feature Engineering on Hotel Reviews.mp4 (35.06 MB)
23 Feature Engineering on Marketing Data.mp4 (58.59 MB)
24 Feature Engineering on Titanic Data.mp4 (49.63 MB)
26 Quiz Solution.mp4 (64.84 MB)
3 Decomposing Time and Date Features.mp4 (38.3 MB)
4 Decomposing Categorical Features.mp4 (38.28 MB)
5 Binning Numerical Features.mp4 (59.36 MB)
6 Aggregating Features.mp4 (56.88 MB)
7 Introduction to Feature Engineering on Text Data.mp4 (33.83 MB)
8 Reading and Summarizing the Text.mp4 (30.48 MB)
9 Finding the Length, Polarity and Subjectivity.mp4 (73.01 MB)
1 Types of Encoding Techniques.mp4 (60.89 MB)
10 Log transformation.mp4 (28.02 MB)
11 BoxCox transformation.mp4 (32.52 MB)
13 Train, Test and Validation Split.mp4 (44.24 MB)
14 Standardization and Normalization.mp4 (39.71 MB)
2 Label Encoding.mp4 (33.54 MB)
3 Feature Mapping for Ordinal Variables.mp4 (29.02 MB)
4 OneHot Encoding.mp4 (34.58 MB)
5 Binary and BaseN Encoding.mp4 (33.22 MB)
6 Mean and Frequency Encoding.mp4 (22.84 MB)
8 Introduction to Skewness and Normal Distribution.mp4 (37.55 MB)
9 Square and Cube Root Transformation.mp4 (39.42 MB)
1 Introduction to Linear Regression.mp4 (81.22 MB)
10 Industry relevance of linear regression.mp4 (49.88 MB)
2 Implementing Linear Regression using Sklearn.mp4 (73.45 MB)
3 Feature Selection using RFECV.mp4 (85.91 MB)
4 Data Transformation with Linear Regression.mp4 (57.52 MB)
5 Applying Cross Validation.mp4 (105.62 MB)
6 Analyzing the performance of Regression models.mp4 (108.97 MB)
7 R2 score and adjuted R2 score intuition.mp4 (107.03 MB)
8 MAE, RMSE, R2 and Adjusted R2 in code.mp4 (49 MB)
9 Applying real time prediction on our model.mp4 (107.61 MB)
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