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Python Data Science
1. Introduction
Who is this Course for (2:43)
DS + ML Marketplace (6:55)
Data Science Job Opportunities (4:24)
Data Science Job Roles (10:23)
What is a Data Scientist (17:00)
How To Get a Data Science Job (18:39)
Data Science Projects Overview (11:52)
2. Data Science & Machine Learning Concepts
Why We Use Python (3:14)
What is Data Science (13:24)
What is Machine Learning (14:22)
ML Concepts & Algorithms (14:42)
Machine Learning vs Deep Learning (11:09)
What is Deep Learning (9:44)
3. Python for Data Science
What is Programming (6:03)
Why Python for Data Science? (3:14)
What is Jupyter (3:54)
What is Google Colab (3:27)
Python Variables, Booleans and None (11:47)
Getting Started with Colab (9:07)
Python Operators (25:26)
Python Numbers and Booleans (7:47)
Python Strings (13:12)
Python Conditional Statements (13:53)
Python For Loops and While Loops (8:07)
Python Lists (5:10)
More About Python Lists (15:08)
Python Tuples (11:25)
Python Dictionaries (20:19)
Python Sets (9:41)
Compound Data Types & When to use each one? (22:39)
Python Functions (14:23)
Object Oriented Programming in Python (18:47)
4. Statistics for Data Science
Intro to Statistics (7:10)
Descriptive Statistics (6:35)
Measure of Variability (12:19)
Measure of Variability Continued (9:35)
Measures of Variable Relationship (7:37)
Inferential Statistics (15:18)
Measures of Asymmetry (1:57)
Sampling Distribution (7:34)
5. Probability & Hypothesis Testing
5.1 What Exactly Probability (3:44)
5.2 Expected Values (2:38)
5.3 Relative Frequency (5:15)
5.4 Hypothesis Testing Overview (9:09)
6. NumPy Data Analysis
NumPy Arrays (8:21)
NumPy Array Basics (11:36)
NumPy Array Indexing (9:10)
NumPy Array Data Types (12:58)
NumPy Array Computations (5:53)
Broadcasting (4:32)
7. Pandas Data Analysis
7.1 Intro to Pandas (15:52)
7.2 Intro to Panda Continued (18:05)
8. Python Data Visualization
8.1 Data Visualization Overview (24:49)
8.2 Different Data Visualization Libraries in Python (12:48)
8.3 Python Data Visualization Implementation (8:27)
9. Machine Learning
Intro to Machine Learning (26:03)
15. Decision Trees
15.1 Decision Trees Section Overview (4:11)
15.2 EDA on Adult Dataset (16:53)
15.3 What is Entropy and Information Gain (21:50)
15.4 The Decision Tree ID3 algorithm from scratch Part 1 (11:32)
15.5 The Decision Tree ID3 algorithm from scratch Part 2 (7:35)
15.6 The Decision Tree ID3 algorithm from scratch Part 3 (4:07)
15.7 ID3 - Putting Everything Together (21:23)
15.8 Evaluating our ID3 implementation (16:53)
15.9 Compare with Sklearn implementation (8:51)
15.10 Visualizing the Tree (10:15)
15.11 Plot the features importance (5:51)
15.12 Decision Trees Hyper-parameters (11:39)
15.13 Pruning (17:11)
15.14 [Optional] Gain Ration (2:49)
15.15 Decision Trees Pros and Cons (7:31)
15.16 [Project] Predict whether income exceeds $50Kyr - Overview (2:33)
16. Ensemble Learning and Random Forests
Ensemble Learning Section Overview (3:46)
What is Ensemble Learning? (13:06)
What is Bootstrap Sampling? (8:25)
What is Bagging? (5:20)
Out-of-Bag Error (OOB Error) (7:47)
Implementing Random Forests from scratch Part 1 (22:34)
Implementing Random Forests from scratch Part 2 (6:10)
Compare with sklearn implementation (3:41)
Random Forests Hyper-Parameters (4:23)
Random Forests Pros and Cons (5:25)
What is Boosting? (4:41)
AdaBoost Part 1 (4:10)
AdaBoost Part 2 (14:33)
17. Support Vector Machines
SVM - Outline (5:15)
SVM - SVM intuition (11:38)
SVM - Hard vs Soft Margin (13:25)
SVM - C Hyper-Parameter (4:17)
SVM - Kernel Trick (12:18)
SVM - Kernel Types (18:13)
SVM - with Linear Dataset (13:35)
SVM - Non-Linear Dataset (12:50)
SVM- Multi _ Regression (5:51)
SVM - Project Overview (Voice Gender Recognition) (4:26)
19. PCA
PCA - Section Overview (5:12)
What is PCA (9:36)
PCA - Drawbacks (3:31)
PCA - Algorithm Steps (13:12)
PCA - Covariance Matrix vs SVD (4:58)
PCA - Main Applications (2:50)
PCA - Image Compression (27:00)
PCA - Data Preprocessing (14:31)
PCA - BiPlot and The Screen Plot (17:27)
PCA - Feature Scaling and Screeplot (9:29)
PCA - Supervised vs unsupervised (4:55)
PCA - Visualization (7:31)
20. Data Science Career
Creating a Data Science Resume (6:45)
Data Science Cover Letter (3:33)
How to Contact Recruiters (4:20)
Getting Started with Freelancing (4:13)
Top Freelance Websites (5:35)
Personal Branding (4:02)
Networking Do's and Don'ts (3:45)
Importance of a Website (2:56)
5.4 Hypothesis Testing Overview
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