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Course Curriculum

INTRODUCTION TO PYTHON FOR DATA SCIENCE AND MACHINE LEARNING FROM A-Z
Who is this course for? 00:03:00
Data Science + Machine Learning Marketplace 00:07:00
Data Science Job Opportunities 00:05:00
Data Science Job Roles 00:11:00
What is a Data Scientist? 00:17:00
How To Get a Data Science Job 00:19:00
Data Science Projects Overview 00:12:00
DATA SCIENCE AND MACHINE LEARNING CONCEPTS
Why We Use Python 00:04:00
What is Data Science? 00:14:00
What is Machine Learning? 00:15:00
Machine Learning Concepts and Algorithms 00:15:00
What is Deep Learning? 00:10:00
Machine Learning vs Deep Learning 00:12:00
PYTHON FOR DATA SCIENCE
What is Programming? 00:07:00
Why Python for Data Science? 00:05:00
What is Jupyter? 00:04:00
What is Google Colab? 00:04:00
Jupyter Notebook 00:19:00
Python Variables, Booleans 00:12:00
Getting Started with Google Colab 00:10:00
Python Operators 00:26:00
Python Numbers and Booleans 00:08:00
Python Strings 00:14:00
Python Conditional Statements 00:14:00
Python For Loops and While Loops 00:09:00
Python Lists 00:06:00
More about Lists 00:16:00
Python Tuples 00:12:00
Python Dictionaries 00:21:00
Python Sets 00:10:00
Compound Data Types and When to use each one? 00:13:00
Python Functions 00:15:00
Object-Oriented Programming in Python 00:19:00
STATISTICS FOR DATA SCIENCE
Intro to Statistics 00:08:00
Descriptive Statistics 00:07:00
Measure of Variability 00:13:00
Measure of Variability Continued 00:10:00
Measures of Variable Relationship 00:08:00
Inferential Statistics 00:16:00
Measure of Asymmetry 00:02:00
Sampling Distribution 00:08:00
PROBABILITY AND HYPOTHESIS TESTING
What Exactly is Probability? 00:04:00
Expected Values 00:03:00
Relative Frequency 00:06:00
NUMPY DATA ANALYSIS
Hypothesis Testing Overview 00:10:00
Intro NumPy Array Data Types 00:13:00
NumPy Arrays 00:09:00
NumPy Arrays Basics 00:12:00
NumPy Array Indexing 00:10:00
NumPy Array Computations 00:06:00
Broadcasting 00:05:00
PANDAS DATA ANALYSIS
Intro To Pandas 00:16:00
Intro To Pandas Continued 00:19:00
PYTHON DATA VISUALIZATION
Data Visualization Overview 00:25:00
Different Data Visualization Libraries in Python 00:13:00
Python Data Visualization Implementation 00:09:00
INTRODUCTION TO MACHINE LEARNING
Intro to Machine Learning 00:27:00
DATA LOADING AND EXPLORATION
Exploratory Data Analysis 00:14:00
Feature Scaling 00:08:00
DATA CLEANING
Data Cleaning 00:08:00
FEATURE SELECTING AND ENGINEERING
Feature Engineering 00:07:00
LINEAR AND LOGISTIC REGRESSION
Linear Regression Intro 00:09:00
Gradient Descent 00:06:00
Linear Regression + Correlation Methods 00:27:00
Linear Regression Implemenation 00:06:00
Logistic Regression 00:04:00
K NEAREST NEIGHBORS
KNN Overview 00:04:00
Parametic vs Non-Parametic Models 00:04:00
EDA on Iris Dataset 00:23:00
KNN – Intuition 00:03:00
Implement the KNN algorithm from scratch 00:12:00
Compare the Reuslt with Sklearn Library 00:04:00
Hyperparameter tuning using the cross-validation 00:11:00
The decision boundary visualization 00:05:00
Manhattan vs Euclidean Distance 00:12:00
Feature scaling in KNN 00:07:00
Curse of dimensionality 00:09:00
KNN use cases 00:04:00
KNN pros and cons 00:06:00
DECISION TREES
Decision Trees Section Overview 00:05:00
EDA on Adult Dataset 00:17:00
What is Entropy and Information Gain? 00:22:00
The Decision Tree ID3 algorithm from scratch Part 1 00:12:00
The Decision Tree ID3 algorithm from scratch Part 2 00:08:00
The Decision Tree ID3 algorithm from scratch Part 3 00:05:00
ID3 – Putting Everything Together 00:22:00
Evaluating our ID3 implementation 00:17:00
Compare with sklearn implementation 00:04:00
Visualizing the tree 00:11:00
Plot the Important Features 00:06:00
Decision Trees Hyper-parameters 00:12:00
Pruning 00:18:00
[Optional] Gain Ration 00:03:00
Decision Trees Pros and Cons 00:08:00
Project] Predict whether income exceeds $50K/yr – Overview 00:03:00
ENSEMBLE LEARNING AND RANDOM FORESTS
Ensemble Learning Section Overview 00:04:00
What is Ensemble Learning? 00:14:00
What is Bootstrap Sampling? 00:09:00
What is Bagging? 00:06:00
Out-of-Bag Error (OOB Error) 00:08:00
Implementing Random Forests from scratch Part 1 00:23:00
Implementing Random Forests from scratch Part 2 00:07:00
Compare with sklearn implementation 00:04:00
Random Forests Hyper-Parameters 00:05:00
Random Forests Pros and Cons 00:06:00
What is Boosting? 00:05:00
AdaBoost Part 1 00:05:00
AdaBoost Part 2 00:15:00
SUPPORT VECTOR MACHINES
SVM Outline 00:06:00
SVM intuition 00:12:00
Hard vs Soft Margins 00:14:00
C hyper-parameter 00:05:00
Kernel Trick 00:13:00
Kernel Types 00:19:00
SVM with Linear Dataset (Iris) 00:14:00
SVM with Non-linear Dataset 00:13:00
SVM with Regression 00:06:00
[Project] Voice Gender Recognition using SVM 00:05:00
K-MEANS
Unsupervised Machine Learning Intro 00:21:00
Unsupervised Machine Learning Continued 00:21:00
Data Standardization 00:20:00
PCA
PCA Section Overview 00:06:00
What is PCA? 00:10:00
PCA Drawbacks 00:04:00
PCA Algorithm Steps (Mathematics) 00:14:00
Covariance Matrix vs SVD 00:05:00
PCA – Main Applications 00:03:00
PCA – Image Compression 00:28:00
PCA – Image Compression 00:28:00
PCA – Image Compression 00:28:00
PCA – Feature Scaling and Screen Plot 00:10:00
PCA – Supervised vs Unsupervised 00:05:00
PCA – Visualization 00:08:00
DATA SCIENCE CAREER
Creating A Data Science Resume 00:07:00
Data Science Cover Letter 00:04:00
How to Contact Recruiters 00:05:00
Getting Started with Freelancing 00:05:00
Top Freelance Websites 00:06:00
Personal Branding 00:05:00
Networking 00:04:00
Importance of a Website 00:03:00
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