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CANADA
AUSTRALIA
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Data Science E-Learning
Home
Course Video
Description
Code and Assignment file: https://bit.ly/3iB5vUs
1. Introduction
1.1 Introduction to course
1.2 Introduction to Data Science
1.3 Software Installation
1.4 Libraries installation
1.5 Important Update
2. Basics of Python
2.1 Python Introduction
2.2 Data Types
2.3 Lists
2.4 Tuples
2.5 Sets
2.6 Dictionary
2.7 If Else and While Loop
2.8 For Loop & Break, Continue
2.9 Functions
2.10 Inheritance
3. Python for Data Science
3.1 NumPy - I
3.2 NumPy - II
3.3 NumPy - III
3.4 Pandas - I
3.5 Pandas - II
3.6 Matplotlib
4. Data Analysis and Visualization
4.1 Count Plot
4.2 2-D Scatter Plot
4.3 Pair Plot
4.4 Probability Density Function (PDF)
4.5 Box Plot
4.6 Violin Plot
4.7 Cross Sell Prediction - Data Analysis
4.8 Correlation & Covariance
4.9 Correlation Matrix
5. Dimensionality Reduction
5.1 PCA - Introduction
5.2 PCA - Geometric Intuition
5.3 PCA Overview
5.4 Column Normalization
5.5 Column Standardization
5.6 Covariance Matrix
5.7 Eigen Values
5.8 PCA for Data Visualization
5.9 PCA for Dimensionality Reduction
6. Machine Learning
6.1 Data Pre-processing
6.2 What is classification?
6.3 KNN - Introduction
6.4 KNN - Distance Measures
6.5 Cosine Similarity and Cosine Distance
6.6 KNN - Importance of K
6.7 Overfitting and Underfitting
6.8 KNN - Bias & Variance
6.9 KNN - Curse of Dimensionality
6.10 KNN - Limitations
6.11 KNN - KD Tree
6.12 KNN - KD Tree Limitations
6.13 KNN - Code Implementation
6.14 Classification Evaluation Metrics - I
6.15 Classification Evaluation Metrics - II
6.16 Classification Evaluation Metrics - III
6.17 Naive Bayes - Introduction
6.18 Naive Bayes - Laplace Smoothing
6.19 Naive Bayes - Bias & Variance
6.20 Naive Bayes - Code Implementation
6.21 Logistic Regression - Introduction
6.22 Logistic Regression - Squashing
6.23 Logistic Regression - Regularization
6.24 Logistic Regression - Multicollinearity
6.25 Logistic Regression - Forward Feature Selection
6.26 Feature Engineering
6.27 Logistic Regression - Bias & Variance
6.28 Logistic Regression - Code
6.29 What is Regression?
6.30 Linear Regression - Geometric Intuition
6.31 Gradient Descent
6.32 Regression Evaluation Metrics
6.33 Linear Regression - Code Implementation
6.34 Support Vector Machine
6.35 SVM - Kernel Trick
6.36 SVM - Time Complexity
6.37 SVM - Code Implementation
6.38 Decision Tree - Geometric Interpretation
6.39 Building Decision Tree - I
6.40 Building Decision Tree - II
6.41 Decision Tree - Code Implementation
6.42 What are Ensembles?
6.43 Random Forest - Explanation
6.44 Random Forest - Code Implementation
6.45 What is Boosting?
6.46 Residuals, Loss Function & Gradients
6.47 GBDT Intuition
6.48 GBDT Code Implementation
6.49 XGBoost Introduction
6.50 XGBoost Code Implementation
6.51 Supervised vs Unsupervised Learning
6.52 Clustering Evaluation Metrics
6.53 K-Means Geometric Intuition
6.54 K-Means Explained
6.55 K-Means++
6.56 K-Means Code Implementation
6.57 Hierarchical Clustering
6.58
