Python for Data Science & Machine Learning → Baseline Model Evaluation (Part-1) - NBICT LAB – Data Science and Machine Learning Courses

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Python for Data Science & Machine Learning

20 Sections
150 Lessons
Lifetime

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1. DS & ML Concepts Clarification
1
- 1.1
  Data Science & Machine Learning Concepts Clarification
  83 Minutes
2. Python Basics
8
- 2.1
  Environment Setup
  70 Minutes
- 2.2
  Lists in Python (Part-1)
  94 Minutes
- 2.3
  Lists in Python (Part-2)
  96 Minutes
- 2.4
  Flow Control in Python (Part-1)
  43 Minutes
- 2.5
  Flow Control in Python (Part-2)
  92 Minutes
- 2.6
  Flow Control in Python (Part-3)
  90 Minutes
- 2.7
  A Short Program (Guess the Number)
  86 Minutes
- 2.8
  Functions in Python
  86 Minutes
3. Data Visualization
5
- 3.1
  Boxplot in Python Using Matplotlib
  76 Minutes
- 3.2
  Histogram and Barplot in Python Using Matplotlib
  77 Minutes
- 3.3
  Line Plot in Python Using Matplotlib
  75 Minutes
- 3.4
  Styling Plots for Publication with Matplotlib (Part-1)
  58 Minutes
- 3.5
  Styling Plots for Publication with Matplotlib (Part-2)
  84 Minutes
4. Regression Analyses
29
- 4.1
  Simple Linear Regression (Part-1)
  106 Minutes
- 4.2
  Simple Linear Regression (Part-2)
  106 Minutes
- 4.3
  Simple Linear Regression (Part-3)
  100 Minutes
- 4.4
  Simple Linear Regression (Part-4)
  93 Minutes
- 4.5
  Simple Linear Regression (Part-5)
  98 Minutes
- 4.6
  Simple Linear Regression (Part-6)
  83 Minutes
- 4.7
  Multiple Linear Regression (Part-1)
  98 Minutes
- 4.8
  Multiple Linear Regression (Part-2)
  97 Minutes
- 4.9
  Multiple Linear Regression (Part-3)
  75 Minutes
- 4.10
  Multiple Linear Regression (Part-4)
  80 Minutes
- 4.11
  Multiple Linear Regression (Part-5)
  70 Minutes
- 4.12
  Multiple Linear Regression (Part-6)
  37 Minutes
- 4.13
  Multiple Linear Regression (Part-7)
  72 Minutes
- 4.14
  Polynomial Regression (Part-1)
  68 Minutes
- 4.15
  Polynomial Regression (Part-2)
  55 Minutes
- 4.16
  Support Vector Regression (Part-1)
  48 Minutes
- 4.17
  Support Vector Regression (Part-2)
  83 Minutes
- 4.18
  Decision Tree Regression (Part-I)
  100 Minutes
- 4.19
  Decision Tree Regression (Part-II)
  107 Minutes
- 4.20
  Decision Tree Regression (Part-III)
  94 Minutes
- 4.21
  Decision Tree Regression (Part-IV)
  92 Minutes
- 4.22
  Decision Tree Regression (Part-V)
  80 Minutes
- 4.23
  ✍Homework: Decision Tree Regression
  60 Minutes
- 4.24
  Random Forest Regression (Part-I)
  95 Minutes
- 4.25
  Random Forest Regression (Part-II)
  76 Minutes
- 4.26
  Regression Model Selection (Part-I)
  95 Minutes
- 4.27
  Regression Model Selection (Part-II)
  90 Minutes
- 4.28
  Regression Model Selection (Part-III)
  105 Minutes
- 4.29
  Quiz – 1 – Regression (PDSML Premium Workshop)
  10 Minutes10 Questions
5. Classification
16
- 5.1
  Logistic Regression (Part-1)
  93 Minutes
- 5.2
  Logistic Regression (Part-2)
  68 Minutes
- 5.3
  Logistic Regression (Part-3)
  98 Minutes
- 5.4
  K-Nearest Neighbour
  69 Minutes
- 5.5
  Support Vector Machine (SVM)
  67 Minutes
- 5.6
  Kernel SVM (Part-I)
  106 Minutes
- 5.7
  Kernel SVM (Part-II)
  102 Minutes
- 5.8
  Kernel SVM (Part-III)
  95 Minutes
- 5.9
  Naive Bayes (Part-I)
  40 Minutes
- 5.10
  Naive Bayes (Part-II)
  50 Minutes
- 5.11
  Decision Tree Classification (Part-I)
  47 Minutes
- 5.12
  Decision Tree Classification (Part-II)
  90 Minutes
- 5.13
  Decision Tree Classification (Part-III)
  80 Minutes
- 5.14
  Decision Tree Classification (Part-IV)
  85 Minutes
- 5.15
  Random Forest Classification (Part-I)
  43 Minutes
- 5.16
  Random Forest Classification (Part-II)
  85 Minutes
6. Clustering
5
- 6.1
  K-Means Clustering (Part-1)
  52 Minutes
- 6.2
  K-Means Clustering (Part-2)
  90 Minutes
- 6.3
  K-Means Clustering (Part-3)
  71 Minutes
- 6.4
  Hierarchical Clustering (Part-1)
  55 Minutes
- 6.5
  Hierarchical Clustering (Part-2)
  58 Minutes
7. Association Rule Learning
3
- 7.1
  Association Rule Learning – Apriori (Part-I)
  55 Minutes
- 7.2
  Association Rule Learning – Apriori (Part-II)
  56 Minutes
- 7.3
  Association Rule Learning – Apriori (Part-III)
  91 Minutes
8. Reinforcement Learning
4
- 8.1
  Upper Confidence Bound (Part-I)
  33 Minutes
- 8.2
  Upper Confidence Bound (Part-II)
  76 Minutes
- 8.3
  Upper Confidence Bound (Part-III)
  90 Minutes
- 8.4
  Thompson Sampling
  67 Minutes
9. Dimensionality Reduction
5
- 9.1
  Principal Component Analysis (Part-1)
  82 Minutes
- 9.2
  Principal Component Analysis (Part-2)
  79 Minutes
- 9.3
  Principal Component Analysis (Part-3)
  92 Minutes
- 9.4
  Kernel PCA
  79 Minutes
- 9.5
  Linear Discriminant Analysis (LDA)
  52 Minutes
10. Natural Language Processing
1
- 10.1
  Natural Language Processing (NLP)
  59 Minutes
11. Model Selection and Boosting
2
- 11.1
  k-Fold Cross Validation
  53 Minutes
- 11.2
  Model Selection and XGBoost
  51 Minutes
12. Deep Learning
15
- 12.1
  Convolutional Neural Network (CNN Part-I)
  73 Minutes
- 12.2
  Convolutional Neural Network (CNN Part-II)
  70 Minutes
- 12.3
  Convolutional Neural Network (CNN Part-III)
  47 Minutes
- 12.4
  Convolutional Neural Network (CNN Part-IV)
  46 Minutes
- 12.5
  Deep Learning with CNN (Part-V)
  84 Minutes
- 12.6
  Artificial Neural Network (ANN Part-I)
  71 Minutes
- 12.7
  Artificial Neural Network (ANN Part-II)
  77 Minutes
- 12.8
  Artificial Neural Network (ANN Part-III)
  62 Minutes
- 12.9
  Artificial Neural Network (ANN Part-IV)
  52 Minutes
- 12.10
  Artificial Neural Network (ANN Part-V)
  98 Minutes
- 12.11
  Deep Learning with CNN (Bonus Part-I)
  85 Minutes
- 12.12
  Deep Learning with CNN (Bonus Part-II)
  82 Minutes
- 12.13
  Deep Learning with CNN (Bonus Part-III)
  60 Minutes
- 12.14
  Deep Learning with CNN (Bonus Part-IV)
  76 Minutes
- 12.15
  Deep Learning with CNN (Bonus Part-V)
  60 Minutes
13. Customer Churn Analysis
11
- 13.1
  Making the Environment Ready
  85 Minutes
- 13.2
  Importing Libraries and Dataset
  90 Minutes
- 13.3
  Data Preprocessing (Part-1)
  90 Minutes
- 13.4
  Data Preprocessing (Part-2)
  90 Minutes
- 13.5
  Data Preprocessing (Part-3)
  90 Minutes
- 13.6
  Data Preprocessing (Part-4)
  60 Minutes
- 13.7
  What is Logistic Regression?
  70 Minutes
- 13.8
  Training the Baseline Model for Churn Analysis
  90 Minutes
- 13.9
  Churn Analysis Model Evaluation (Part-1)
  90 Minutes
- 13.10
  Churn Analysis Model Evaluation (Part-2)
  90 Minutes
- 13.11
  Exploratory Descriptive Analysis on Churn Data
  90 Minutes
14. Customer Segmentation
4
- 14.1
  Customer Segmentation Data Preprocessing
  90 Minutes
- 14.2
  K-Means Clustering Intuition and the Elbow Method
  70 Minutes
- 14.3
  Finding the Optimum Number of Clusters
  80 Minutes
- 14.4
  Building the Clustering Model and Finalizing Clusters
  90 Minutes
15. Fake News Detection
5
- 15.1
  Building Fake News Detector (Part-1)
  60 Minutes
- 15.2
  What is TF-IDF Score?
  60 Minutes
- 15.3
  TF-IDF Score Python Verification
  58 Minutes
- 15.4
  Building Fake News Detector (Part-2)
  90 Minutes
- 15.5
  Building Fake News Detector (Part-3)
  90 Minutes
16. Human Disease Prediction
7
- 16.1
  Diabetes Prediction (Part-1)
  49 Minutes
- 16.2
  Diabetes Prediction (Part-2)
  35 Minutes
- 16.3
  Decision Tree Classification Intuition
  90 Minutes
- 16.4
  Diabetes Prediction (Part-3)
  60 Minutes
- 16.5
  Diabetes Prediction (Part-4)
  70 Minutes
- 16.6
  Heart Disease Prediction
  35 Minutes
- 16.7
  Breast Cancer Prediction
  45 Minutes
17. Image Classification for Medical Diagnosis
10
- 17.1
  Detecting Pneumonia from X-ray Scans (Part-1)
  60 Minutes
- 17.2
  Introduction to CNN (Part-1)
  35 Minutes
- 17.3
  Introduction to CNN (Part-2)
  50 Minutes
- 17.4
  Introduction to CNN (Part-3)
  50 Minutes
- 17.5
  Introduction to CNN (Part-4)
  50 Minutes
- 17.6
  Introduction to CNN (Part-5)
  30 Minutes
- 17.7
  Detecting Pneumonia from X-ray Scans (Part-2)
  40 Minutes
- 17.8
  Detecting Pneumonia from X-ray Scans (Part-3)
  35 Minutes
- 17.9
  Detecting Pneumonia from X-ray Scans (Part-4)
  52 Minutes
- 17.10
  Detecting Pneumonia from X-ray Scans (Part-5)
  53 Minutes
18. Plant Disease Detection
5
- 18.1
  Plant Disease Detection (Part-1)
  70 Minutes
- 18.2
  Plant Disease Detection (Part-2)
  57 Minutes
- 18.3
  Plant Disease Detection (Part-3)
  47 Minutes
- 18.4
  Plant Disease Detection (Part-4)
  80 Minutes
- 18.5
  Plant Disease Detection (Part-5)
  60 Minutes
19. Forecasting Rainfall
12
- 19.1
  Making the Environment Ready
  90 Minutes
- 19.2
  Data Cleaning (Part-A)
  90 Minutes
- 19.3
  Data Cleaning (Part-B)
  97 Minutes
- 19.4
  Encoding the Categorical Data
  94 Minutes
- 19.5
  Finalizing the Data Frame Structure
  60 Minutes
- 19.6
  Splitting the Dataset
  90 Minutes
- 19.7
  Feature Scaling and Training the Model
  87 Minutes
- 19.8
  Model Evaluation
  85 Minutes
- 19.9
  Building the ROC Curve (Part-A)
  75 Minutes
- 19.10
  Building the ROC Curve (Part-B)
  90 Minutes
- 19.11
  Making a Single Prediction
  85 Minutes
- 19.12
  Handling Imbalanced Data
  79 Minutes
20. Yield Prediction
3
- 20.1
  Making the Environment Ready
  71 Minutes
- 20.2
  Building a Simple Baseline Model
  56 Minutes
- 20.3
  Baseline Model Evaluation (Part-1)
  66 Minutes

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Building a Simple Baseline Model

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Address: HSTU, Dinajpur-5200, Bangladesh.

Email: nbict.lab@gmail.com, support@nbict.org

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