Data Science Full Course for Beginners

What Will You Learn?

• 00:00:00 - Data Science Full Course Intro
• 00:01:31 Data Science Maths in-depth
• 1 Introduction to Statistics
• • Population & Sample
• • Descriptive vs Inferential Statistics
• • Basic Statistical Measures
• 2 Measure of Central Tendency (Median, Mean, Mode)
• • Measures of Variability
• • Percentage, Percentiles, and Quartiles
• 3 Probability
• • Probability Distribution
• • Normal Distribution
• • Advanced Statistical Concepts
• 4 Covariance and Correlation
• • Central Limit Theorem
• • 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:
• • Identifying and Handling Missing Values
• • One Hot Encoding & Dummy Variables
• • Label Encoding
• • Ordinal Encoding
• • Outlier Detection and Removal
• • Feature Scaling (Standardization and Normalization)
• • Handling Duplicate Data
• • Data Type Transformation
• 5 Feature Selection Techniques:
• • Backward Elimination (using mixed)
• • Forward Elimination (using mixed)
• 09:33:17 - Supervised Learning in ML
• 1 Train Test Split in Dataset
• 2 Regression Analysis:
• • Linear Regression Algorithm (Simple Linear)
• • Multiple Linear Regression
• • 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:
• • Binary Classification (Practical)
• • Binary Classification with Multiple Inputs (Practical)
• • Binary Classification with Polynomial Inputs (Practical)
• • 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:
• • Decision Tree (Classification)
• • Decision Tree (Regression)
• • 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
• • Practical Implementation for Regression
• • Practical Implementation for Classification
• 3 Bagging (with Bagging meta-estimator and Random forest)
• 23:25:50 - Deep Learning & AI Complete
• 1 Deep Learning Overview:
• • Introduction to Deep Learning
• • Neurons, Neural Networks, and Types of Deep Learning Networks
• 3 Perceptrons:
• • Single Layer Perceptron
• • Multilayer Perceptron (Artificial Neural Networks)
• 3 Training Process:
• • Forward Propagation and Backpropagation
• • Activation Functions
• • Loss Functions
• • Optimizers
• 4 Practical Applications