Data Science FULL Course for Beginners in 27 HOURS – 2024 Edition
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00:00:00 – Data Science Full Course Intro
00:01:31 Data Science Maths in-depth
1 Introduction to Statistics
β’ Population & Sample
β’ Descriptive vs Inferential Statistics
β’ Basic Statistical Measures
2 Measure of Central Tendency (Median, Mean, Mode)
β’ Measures of Variability
β’ Percentage, Percentiles, and Quartiles
3 Probability
β’ Probability Distribution
β’ Normal Distribution
β’ Advanced Statistical Concepts
4 Covariance and Correlation
β’ Central Limit Theorem
β’ Hypothesis Testing
05:52:03 – Machine Learning Complete
1 Introduction to Machine Learning (ML)
2 Roadmap to Learning Machine Learning
3 Types of Data and Variables in ML
2 Data Cleaning:
β’ Identifying and Handling Missing Values
β’ One Hot Encoding & Dummy Variables
β’ Label Encoding
β’ Ordinal Encoding
β’ Outlier Detection and Removal
β’ Feature Scaling (Standardization and Normalization)
β’ Handling Duplicate Data
β’ Data Type Transformation
5 Feature Selection Techniques:
β’ Backward Elimination (using mixed)
β’ Forward Elimination (using mixed)
09:33:17 – Supervised Learning in ML
1 Train Test Split in Dataset
2 Regression Analysis:
β’ Linear Regression Algorithm (Simple Linear)
β’ Multiple Linear Regression
β’ Polynomial Regression
3 Cost Function in Regression
4 R Squared Score & Adjusted R Squared in Regression Analysis
12:15:30 – Classification in ML
1 Classification
2 Logistic Regression:
β’ Binary Classification (Practical)
β’ Binary Classification with Multiple Inputs (Practical)
β’ Binary Classification with Polynomial Inputs (Practical)
β’ Multiclass Classification (Practical)
3 Confusion Matrix
4 Imbalanced Dataset Handling
5 Naive Bayes Algorithm
15:18:11 – Non-Linear Supervised Algorithm in ML
1 Non-Linear Supervised Algorithms:
β’ Decision Tree (Classification)
β’ Decision Tree (Regression)
β’ K-Nearest Neighbors (Classification)
2 Hyperparameter Tuning
3 Cross-Validation
4 Unsupervised Learning
19:01:23 – Clustering in ML
1 Clustering
2 K-means Clustering
3 Hierarchical Clustering
4 DBSCAN Clustering Algorithm
5 Silhouette Score
20:52:57 – Association in ML
1 Association
2 Association Rule Learning
3 Apriori Algorithm
4 Frequent Pattern Growth Algorithm
22:15:34 – Ensemble Learning in ML
1 Ensemble Learning
2 Max Voting, Averaging & Weighted Average Voting
β’ Practical Implementation for Regression
β’ Practical Implementation for Classification
3 Bagging (with Bagging meta-estimator and Random forest)
23:25:50 – Deep Learning & AI Complete
1 Deep Learning Overview:
β’ Introduction to Deep Learning
β’ Neurons, Neural Networks, and Types of Deep Learning Networks
3 Perceptrons:
β’ Single Layer Perceptron
β’ Multilayer Perceptron (Artificial Neural Networks)
3 Training Process:
β’ Forward Propagation and Backpropagation
β’ Activation Functions
β’ Loss Functions
β’ Optimizers
4 Practical Applications
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