This course was created with the
course builder. Create your online course today.
Start now
Create your course
with
Autoplay
Autocomplete
Previous Lesson
Complete and Continue
The Full Stack Data Scientist BootCamp®
DATA SCIENCE & MACHINE LEARNING WARM-UP
The Joy Of Data (59:15)
Recommended readings
CURRICULUM
Download Course Curriculum
1ST MONTH
Overview
WEEK 1 :: Data Science Overview
Lecture resources
The Big Picture (3:22)
Part 1: Data Science Overview (4:56)
What Is Data Science? (7:44)
DA vs DS vs AI vs ML (4:49)
Industries That Use and Hire Data Scientist (3:33)
Applications of Data Science (8:41)
Data Science Lifecycle and the Maturity Framework (3:46)
Who is a Data Scientist? (6:26)
Career Opportunities In Data Science (3:29)
Typical Backgrounds of Data Scientists (1:55)
Typical Salary of a Data Scientist (4:23)
The Ultimate Path To become a Data Scientist(Skills you need to develop)
COMPLETE SQL FOR DATA SCIENCE COURSE
Overview
ALL SQL Lecture resources
SQL : BEGINNER LEVEL
Introduction To SQL for Data Science (6:06)
Types of Databases (4:20)
What is a Query? (3:01)
What is SQL? (3:18)
SQL or SEQUEL? (2:59)
SQL Installation (1:36)
SQL Installation Guide For MacOS (4:53)
SQL Installation Guide For Windows (4:01)
Extra Help in Installing SQL (0:46)
Overview of SQL workbench (13:49)
SQL Commands
Introduction To SQL Commands (3:02)
SQL CRUD Commands (1:16)
Understanding and Creating SQL Databases
SQL Schema (1:53)
Inserting Comments in SQL (1:22)
Creating Databases (3:36)
Understanding and Creating SQL Tables
Overview of SQL Table (3:14)
Types Of SQL KEYS
Keys in SQL (1:37)
Primary Key (3:14)
Foreign Key (2:44)
Composite Key (1:11)
Super Key (1:52)
Alternate Key (1:16)
Data Types In SQL
SQL Data Types (7:38)
CREATE Table and INSERT Data into Table
CREATE Table (9:59)
INSERT Data (9:37)
SQL Constraints
Understanding SQL Constraints (6:00)
NOT NULL & UNIQUE Constraints (13:20)
DEFAULT Constraints (3:25)
PRIMAY KEY Constraint (4:15)
Alter SQL Constraint (3:22)
Adding and Dropping SQL Constraint (5:42)
Foreign Key Constraint (6:15)
WEEK 2 :: SQL INTERMEDIATE LEVEL
Creating Exiting Databases (6:15)
Overview Of Existing Databases (5:12)
The SELECT Statement in Details (9:52)
The ORDER BY Clause (2:01)
The WHERE Clause (4:46)
Operation with SELECT statement (7:10)
Aliasing in SQL (9:03)
Exercise 1 and solution (5:28)
The DISTINCT Keyword (3:28)
WHERE Clause with SQL Comparison operators (7:15)
Exercise 2 and Solution (3:17)
The AND, OR and NOT Operators (11:48)
Exercise 3 and Solution (5:35)
The IN Operator (3:02)
Exercise 4 and Solution (2:18)
The BETWEEN Operator (2:41)
Exercise 5 and Solution (3:16)
The LIKE Operator (7:59)
Exercise 6 and Solution (3:51)
The REGEXP Operator (10:31)
Exercise 7 and Solution (7:21)
IS NULL & IS NOT NULL Operator (3:12)
Exercise 8 and Solution (2:44)
The ORDER BY Clause in Details (4:37)
The LIMIT Clause (2:31)
Exercise 9 and Solution (2:53)
SQL JOINS
Introduction To SQL JOINS (8:59)
Exercise 10 and Solution (7:24)
Joining Across Multiple Databases (6:51)
Exercise 11 and Solution (8:27)
Joining Table to Itself (7:56)
Joining Across Multiple SQL Tables (11:16)
LEFT and RIGHT JOIN (6:32)
Exercise 12 and Solution (6:27)
Exercise 13 and Solution (7:24)
Working With Existing SQL Table
INSERTING Multiple Data Into Existing Table (3:12)
Creating A Copy of a Table (5:51)
Updating Existing Table (10:21)
Updating Multiple Records In Existing Table (5:21)
SQL VIEW
Create SQL VIEW (6:59)
Using SQL VIEW (4:40)
Alter SQL VIEW (4:13)
Drop SQL View (1:38)
SQL Data Summarization: Aggregation Functions
COUNT () Function (3:24)
SUM() Function (1:30)
AVG() Function (1:12)
SQL Combined Functions (2:20)
Advance SQL Functions
Count Function in Details (5:15)
The HAVING() Function (3:50)
LENGTH() Function (6:35)
CONCAT() Function (6:05)
INSERT() Function (8:34)
LOCATE() Function (5:23)
UCASE() & LCASE() Function (3:36)
WEEK 3 :: SQL ADVANCED LEVEL
Overview
SQL Stored Procedure
Create a Stored Procedure (5:46)
Stored Procedure with Single Parameter (5:48)
Stored Procedure with Multiple Parameter (5:20)
Alter Stored Procedure (3:18)
Drop Stored Procedure (1:31)
Triggers
Introduction to Triggers (5:38)
BEFORE Insert Triggers (7:05)
AFTER Insert Trigger (10:59)
DROP Triggers (1:42)
RECOMMENDATION
Recommendation
WEEK 4 :: BEGINNER : Python For Data Science
Lecture Resources 1
Lecture resources 2
Python Course Curriculum (6:20)
Install and Write Your First Python Code (14:19)
Introduction To Jupyter Notebook & Jupyter Lab (15:40)
Working with Code Vs Markdown (15:32)
Introduction To Google Colab
Datasets
Download datasets
Hands-On With Python
Lecture Resources
Python Hands-On: Introduction (1:00)
Hands-On With Python: Keywords And Identifiers (12:53)
Hands-On Coding: Python Comments (7:09)
Hands-On Coding: Python Docstring (3:25)
Hands-On Coding: Python Variables (9:03)
Hands-On Coding: Rules and Naming Conventions for Python Variables (7:39)
Python Output(), Input() and Import() Functions
Hands-On Coding: Output() Function In Python (2:29)
Hands-On Coding: Input() Function In Python (7:56)
Hands-On Coding: Import() Function In Python (4:52)
Python Operators
Hands-On Coding: Arithmetic Operators (2:12)
Hands-On Coding: Comparison Operators (1:53)
Hands-On Coding: Logical Operators (7:38)
Hands-On Coding: Bitwise Operators (7:51)
Hands-On Coding: Assignment Operators (3:34)
Python Hands-On: Special Operators (2:00)
Hands-On Coding: Membership Operators (2:51)
Python Flow Control
If Statement (4:20)
If...Else Statement (2:11)
ELif Statement (6:02)
For loop (3:13)
While loop (5:16)
Break Statement (3:22)
Continue Statement (3:56)
WEEK 2: Python Functions
User Define Functions (12:46)
Arbitrary Arguments (5:13)
Function With Loops (2:12)
Lambda Function (8:43)
Built-In Function (6:40)
Python Global and Local Variables
Global Variable (2:00)
Local Variable (4:26)
Working With Files In Python
Python Files (7:58)
The Close Method (1:19)
The With Statement (2:30)
Writing To A File In Python (7:22)
Python Modules
Python Modules (6:23)
Renaming Modules (1:28)
The from...import Statement (2:09)
Python Packages and Libraries
Python Packages and Libraries (4:54)
PIP Install Python Libraries (6:35)
Data Types In Python
Lecture resources
Lesson 1: Integer & Floating Point Numbers (3:46)
Lesson 2: Complex Numbers & Strings (3:50)
Lesson 3: LIST (2:38)
Lesson 4: Tuple & List Mutability (4:59)
Lesson 5: Tuple Immutability (3:25)
Lesson 6: Set (2:53)
Lesson 7: Dictionary (4:58)
Extra Content
LIST (9:45)
Working On List (7:09)
Splitting Function (10:44)
Range In Python (9:06)
List Comprehension In Python (6:16)
WEEK 3: Numpy
Lecture resources
Introduction To Numpy (11:30)
Numpy: Creating Multi-Dimensional Arrays (1:53)
Numpy: Arange Function (5:53)
Numpy: Zeros, Ones and Eye functions (4:46)
Numpy: Reshape Function (1:23)
Numpy: Linspace (2:23)
Numpy: Resize Function (5:23)
Numpy:Generating Random Values With random.rand (3:04)
Numpy:Generating Random Values With random.randn (2:26)
Numpy:Generating Random Values With random.randint (3:40)
Numpy: Indexing & Slicing (17:00)
Numpy: Broadcasting (1:17)
Numpy: How To Create A Copy Dataset (4:28)
Numpy- DataFrame Introduction (15:25)
Numpy Assignment
Numpy Assignment
WEEK 4: Pandas
Pandas Lecture resources
Pandas- Series 1 (19:21)
Pandas- Series 2 (11:05)
Pandas- Loc & iLoc (7:48)
Pandas- DataFrame Introduction (4:17)
Pandas- Operations On Pandas DataFrame (9:10)
Pandas- Selection And Indexing On Pandas DataFrame (3:12)
Pandas- Reading A Dataset Into Pandas DataFrame (8:32)
Pandas- Adding A Column To Pandas DataFrame (4:33)
Pandas- How To Drop Columns And Rows In Pandas DataFrame (11:03)
Pandas- How To Reset Index In Pandas Dataframe (3:32)
Pandas- How To Rename A Column In Pandas Dataframe (6:29)
Pandas- Tail(), Column and Index (2:56)
Pandas- How To Check For Missing Values or Null Values(isnull() Vs Isna()) (6:16)
Pandas- Pandas Describe Function (5:40)
Pandas- Conditional Selection With Pandas (9:14)
Pandas- How To Deal With Null Values (7:14)
Pandas- How To Sort Values In Pandas (3:10)
Pandas- Pandas Groupby (0:37)
Pandas- Count() & Value_Count() (2:14)
Pandas- Concatenate Function (6:47)
Pandas- Join & Merge(Creating Dataset) (3:45)
Pandas-Join (9:49)
Pandas- Merge (7:55)
Data Visualisation: MatplotIib And Seaborn
Lecture resources
Matplotlib | Subplots (29:40)
Seaborn | Scatterplot | Correlation | Boxplot | Heatmap (43:56)
Univariate | Bivariate | Multivariate Data Visualisation (28:16)
PROJECT: Python Assignment
Assignment
Project Solution
PYTHON PROJECTS
Lecture Resources
PROJECT 1: Analyse The Top Movie Streaming | NETFLIX | Amazon Prime | Hulu | Disney World (117:59)
PROJECT 2: Analysis of UBER Data (44:16)
2ND MONTH
Overview
FULL STATISTICS FOR DATA SCIENCE
Lecture Resources
Statistics For Data Science Curriculum
Why Statistics Is Important For Data Science (10:00)
How Much Maths Do I Need To Know? (2:40)
Statistical Methods Deep Dive
Statistical Methods Deep Dive (6:30)
Types Of Statistics (3:42)
Common Statistical Terms (5:16)
Data
What Is Data? (1:57)
Data Types (12:01)
Data Attributes and Data Sources (2:40)
Structured Vs Unstructured Data
Recommend Statistics For Data Science
Recommended Stats Lecture
Frequency Distribution
Frequency Distribution (15:02)
Central Tendency
Central Tendency (2:51)
Mean,Median, Mode (16:07)
Measures of Dispersion
Measures of Dispersion (2:04)
Variance and Standard Deviation (2:00)
Example of Variance and Standard Deviation (5:56)
Variance and Standard Deviation In Python (2:00)
Coefficient of Variations
Coefficient of Variations (5:47)
The Five Number Summary & The Quartiles
The Five Number Summary (4:45)
The Quartiles: Q1 | Q2 | Q3 | IQR (9:43)
The Normal Distribution
Introduction To Normal Distribution (9:51)
Skewed Distributions (7:58)
Central Limit Theorem (10:55)
Correlation
Introduction to Correlation (11:41)
Scatterplot For Correlation (1:53)
Correlation is NOT Causation (1:17)
3RD MONTH
Overview
WEEK 1 :: Probability
Why Probability In Data Science? (6:19)
Probability Key Concepts (10:28)
Mutually Exclusive Events (3:46)
Independent Events (6:13)
Rules For Computing Probability (13:14)
Baye's Theorem
Baye's Theorem Overview (1:07)
Hypothesis Testing
Introduction To Hypothesis (3:50)
Null Vs Alternative Hypothesis (1:34)
Setting Up Null and Alternative Hypothesis (0:37)
One-tailed Vs Two-tailed test (1:40)
Key Points On Hypothesis Testing (3:08)
Type 1 vs Type 2 Errors (6:01)
Process Of Hypothesis testing (1:46)
P-Value (2:28)
Alpha-Value or Alpha Level (3:15)
Confidence Level (2:00)
STATISTICS PROJECT
Implementation of the Stats Concepts
Project Assignment
Project Solution
GITHUB For Data Science
Introduction to Github for Data Science (1:38)
Setting up Github account for Data Science projects (3:25)
Create Github Profile for Data Science (22:44)
Create Github Project Description for Data Science (21:28)
ARTIFICIAL INTELLIGENCE(AI) and MACHINE LEARNING(ML)
Overview
WEEK 2 :: FULL MACHINE LEARNING COURSE
Resources
Introduction To Machine Learning (1:33)
Overview of Machine Learning Curriculum (11:19)
Practical Understanding Of Machine Learning (PART 1) (10:29)
Practical Understanding Of Machine Learning (PART 2) (3:43)
Applications of Machine Learning (9:07)
Machine Learning Life Cycle (20:49)
USE CASE
The Microsoft Data Science Project (19:44)
Setting Up Your Environment for Machine Learning (5:04)
Machine Learning Algorithms
How Machine Learning Algorithms Learn (9:17)
Difference Between Algorithm and Model (6:32)
Supervised vs Unsupervised ML (16:30)
Dependent vs Independent Variables (3:18)
Working with Machine Learning Data
Lecture resources
Considerations When Loading Data (6:22)
Loading Data from a CSV File (7:01)
Loading Data from a URL (1:23)
Loading Data from a Text File (3:50)
Loading Data from an Excel File (3:36)
Skipping Rows while Loading Data (2:51)
Peek at your Data (3:12)
Dimension of your Data (2:01)
Checking Data Types of your Dataset (3:53)
Descriptive Statistics of your Dataset (4:48)
Class Distribution of your Dataset (4:49)
Correlation of your Dataset (6:24)
Skewness of your Dataset (1:51)
Missing Values in your Dataset (2:10)
Histogram of Dataset (4:48)
Density Plot of Dataset (3:32)
Box and Whisker Plot (3:02)
Correlation Matrix (4:55)
Scatter Matrix(Pairplot) (3:30)
WEEK 3 :: SUPERVISED MACHINE LEARNING ALGORITHMS
Overview
Supervised ML resources
Regression
What is Regression? (7:33)
Linear Regression
Introduction to Linear Regression (5:35)
Conceptual Understanding of Linear Regression (19:59)
Planes and Hyperplane (2:14)
MSE vs RMSE (10:13)
LAB SESSION: Linear Regression
Training Data vs Validation Data vs Testing Data (9:16)
Splitting Dataset into Training and Testing (20:46)
Linear Regression LAB 1 (40:59)
Linear Regression LAB 2(PART 1) (34:31)
Linear Regression LAB 2(PART 2) (26:29)
Logistic Regression Algorithm
Regressor Algorithm Vs Classifier Algorithm (9:47)
Introduction To Logistic Regression Algorithm (4:01)
Limitations of Linear Regression (19:45)
PART 2: Intuitive Understanding Of Logistic Regression (22:48)
The Mathematics Behind Logistic Regression Algorithm (22:52)
LAB SESSION 1: Practical Implementation of Logistic Regression Algorithm (23:34)
LAB SESSION 2: Practical Implementation of Logistic Regression Algorithm (28:39)
LAB SESSION 3: Practical Implementation of Logistic Regression Algorithm (21:13)
Naive Bayes Algorithm
Introduction to Naive Bayes Algorithm (4:43)
The Mathematics Behind Naive Bayes Algorithm (28:53)
LAB SESSION: Building Naive Bayes Model (37:06)
K-Nearest Neigbhor Algorithm (KNN)
Introduction To K-Nearest Neighbor Classification (13:31)
K-Nearest Neighbor Classification-Distance Measures (21:06)
Exploratory Data Analysis (EDA) (26:39)
LAB SESSION: Building K-Nearest Neighbor Model (17:09)
K-Nearest Neighbor Classification-Choosing K (17:45)
Support Vector Machine (SVM) Algorithm
Introduction to Support Vector Machine (SVM) algorithm (1:16)
Mathematics of SVM and Intuitive Understanding of SVM Algorithm (24:25)
Non-Linearly Separable Vectors (11:53)
LAB SESSION PART 1: Support Vector Machine (SVM) (9:44)
LAB SESSION PART 2: Support Vector Machine (SVM) (19:10)
Machine Learning Algorithm Performance Metrics
Lecture Resources
Overview (1:44)
Confusion Matrix: True Positive | False Positive | True Negative | False Neg. (23:50)
Accuracy (13:04)
Precision (5:28)
Recall (4:06)
The Tug of War between Precision and Recall (8:16)
F 1 Score (1:32)
Classification Report (3:24)
ROC and AUC
LAB SESSION: AUC and ROC (9:28)
Decision Tree Algorithm
Lecture resources
Decision Tree Overview (1:28)
CART: Introduction To Decision Tree (15:24)
Purity Metrics: Gini Impurity | Gini Index (15:57)
Calculating Gini Impurity (PART 1) (10:43)
Calculating Gini Impurity (PART 2) (7:20)
Information Gain (8:52)
Overfitting in Decision Trees (4:20)
Prunning (2:29)
LAB SESSION: Prunning
WEEK 4 :: Ensemble Techniques
Lecture Resources
Introduction To Ensemble Techniques (8:24)
Understanding Ensemble Techniques (2:54)
Difference b/n Random Forest & Decision Tree (4:55)
Why Random Forest Algorithm (5:26)
More on Random Forest Algorithm (3:04)
Introduction to Bootstrap Sampling | Bagging (3:35)
Understanding Bootstrap Sampling (2:54)
Diving Deeper into Bootstrap Sampling (9:57)
Bootstrap Sampling summary (8:10)
Bagging (5:43)
Boosting (8:07)
Adaboost : Introduction (4:35)
The Maths behind Adaboost algorithm (21:16)
Gradient Boost: Introduction (4:33)
Gradient Boosting : An Intuitive Understanding (15:08)
The Mathematics behind Gradient Boosting Algorithm (33:15)
XGBoost: Introduction (1:57)
Maths of XGBoost (PART 1) (18:10)
Maths of XGBoost (PART 2) (18:48)
LAB SESSION 1: Ensemble Techniques (23:27)
LAB SESSION 2: Ensemble Techniques (39:49)
Stacking: An Introduction (9:25)
LAB SESSION: Stacking (15:35)
Overfitting and Underfitting
Lecture resources
Overfitting and Underfitting (27:26)
LAB SESSION: Preventing Overfitting (PART 1) (37:51)
LAB SESSION: Preventing Overfitting (PART 2) (9:08)
Preventing Underfitting (25:09)
Bias vs Variance
Introduction (4:02)
Bias vs Variance (26:56)
The Bias Variance Tradeoff (12:21)
Summary (9:08)
PROJECT: Supervised Machine Learning
Ssupervised Machine Learning Project Assignment
Project Solution
4TH MONTH
Overview
UNSUPERVISED MACHINE LEARNING ALGORITHMS
All Unsupervised ML Resources
WEEK 3: K-Means Clustering Algorithm
Difference Between KNN and KMeans Algorithms (11:00)
What is K-Means Clustering? (14:32)
The Llyod's Method-Shifting the Centroids (9:09)
K-Means Algorithm-LAB SESSION (44:35)
Choosing K in Kmeans-The Elbow Method (12:48)
Hierarchical Clustering Algorithm
Introduction To Hierarchical clustering (26:35)
Hierarchical Clustering | Dendrograms(Cophenetic correlation) (11:46)
Hierarchical Clustering-LAB (42:29)
Principal Component Analysis (PCA)
PART 1: Understanding PCA (20:59)
PART 2: Understanding PCA (5:01)
PCA in Python (10:45)
PCA Further Read
PROJECT: Unsupervised Machine Learning
Project Assignment
Project Solution
Feature Engineering : Model Selection & Optimization
Lecture resources
KFold Cross Validation (25:15)
LAB SESSION: KFold Cross Validation (11:52)
Bootstrap Sampling (12:13)
Leave One Out Cross Validation(LOOCV) (4:49)
LAB SECTION: LOOCV (6:56)
Hyper-parameter Tuning: An Introduction (16:53)
Hyper-parameter Tuning: Continue (6:39)
GridSearchCV: An Introduction (39:48)
RandomSearchCV: An Introduction (2:31)
LAB SESSION 1: GridSearchCV (39:48)
LAB SESSION 2: GridSearchCV (13:43)
LAB SESSION: RandomSearchCV (14:38)
Regularization
Lasso(L1) and Ridge (L2) Regression
WEEK 3 :: WEB SCRAPING
Lecture Resources
Introduction To Web Scraping Libraries (1:31)
Library- Requests (1:55)
Library- BeautifulSoup (1:00)
Library- Selenium (1:11)
Library- Scrapy (1:35)
PROJECT: Wikipedia Web Scraping
Lecture Resources
Web Scraping On Wikipedia (30:58)
PROJECT: Online Book Store Web Scrapping
Lecture Resources
Critical Analysis Of Web Pages (6:04)
PART 1- Examining And Scraping Individual Entities From Source Page (22:51)
PART 2- Examining And Scraping Individual Entities From Source Page (10:11)
Data Preprocessing On Scraped Data (16:30)
Web Scraping: Building Amazon Auto Scraper
Lecture Resources
Building Amazon Web Scraper (0:54)
Installation of Libraries & Analysis of Amazon.com (14:44)
Building Amazon Generic Auto Scraper (27:52)
PROJECT: Job Board Data Web Scraping Automation With Python
Lecture Resources
Indian Institute Of Business(ISB)- Project Introduction (5:36)
Problem Statement & Dataset (5:26)
Demystify The Structure Of Web Page URLs (3:59)
Formulating Generic Web Page URLs (11:08)
Forming The Structure Of Web Page URLs (9:23)
Creating A DataFrame For Scraped Data (3:41)
Creating A Generic Auto Web Scraper (23:37)
Automate Spotify & Youtube with Python
Automate Spotify & Youtube with Python (10:00)
WEEK 4 :: RECOMMENDATION SYSTEMS
Lecture Resources
Recommendation System: An Overview (2:54)
Where Recommender Systems came from (2:49)
Applications of Recommendation Systems (6:25)
Why Recommender Systems? (9:27)
Types of Recommender Systems (0:50)
Popularity based Recommender Systems (4:43)
LAB SESSION: Popularity based Recommender (15:13)
Content-based Filtering: An Overview (7:48)
Cosine Similarity (12:04)
Cosine Similarity with Python (5:49)
Document Term Frequency Matrix (19:22)
LAB SESSION: Building Content-based Recommender Engine (34:05)
Collaborative Filtering: An Introduction (2:49)
LAB SESSION: Collaborative Filtering
Evaluation Metrics for Recommender Systems (4:41)
4TH MONTH
Overview
HANDS-ON PROJECTS
Overview
Projects Lecture Resources
Bank Note Analysis
Project resources
Introduction (2:32)
Exploratory Data Analysis (20:40)
Data Preparation (46:32)
PART 1: Model Building (19:53)
PART 2: Model Building (36:47)
Big Mart Sales Prediction
Lecture resources
Introduction (2:56)
Dataset Overview (49:48)
Feature Engineering & Feature Transformation (44:36)
Model Building (14:21)
Amazon.com Employee Access Challenge
Project resources
Introduction (2:12)
Exploratory Data Analysis (34:41)
Model Building (39:35)
Breast Cancer Detection using SVM and KNN : PART 1
Project resources
Introduction (2:59)
Dataset Summary (6:37)
Exploratory Data Analysis (45:21)
Building the Model (40:21)
Predicting Compressive Strength Of Concrete
Lecture resources
Introduction (2:48)
Importing Dataset & Exploratory Data Analysis(EDA) (44:15)
Feature Engineering And Model Building 1 (46:05)
Feature Engineering And Model Building 2 (26:46)
Stock Market Clustering
Lecture resources
Introduction (2:30)
Live Data Extraction From Yahoo Finance (20:28)
Performing Clustering (31:51)
5TH MONTH
Overview
CLOUD DEPLOYMENT
Overview
Streamlit For Building Machine Learning Apps
Streamlit Lecture resources
Demo (17:04)
PART 1: Introduction to STREAMLIT (21:27)
PART 1: Introduction to STREAMLIT (48:37)
PART 1: Introduction to STREAMLIT (24:42)
PART 1: Build Your First Machine Learning Web App (54:45)
PART 2: Build Your First Machine Learning Web App (38:42)
PART 3: Build Your First Machine Learning Web App (20:06)
PART 4: Build Your First Machine Learning Web App (52:41)
FLASK TUTORIAL
Introduction (2:54)
Installation and Initializing Flask (16:24)
Linking HTML files (17:30)
Linking CSS files (16:03)
WEEK 1 ::End-to-End Machine Learning with DEPLOYMENT : Predict Restaurant Rating
Flask Lecture resources
Predict Restaurant Rating (3:09)
Dataset overview (7:51)
Exploratory Data Analysis (EDA) (47:03)
Model Building (23:00)
Key Flask Concepts & Application Development Interface (API) (11:23)
Creating Folders (23:49)
Creating Folder Contents (16:19)
Final Deployment (17:58)
CLOUD :: Heroku Machine Learning Cloud Deployment
Lecture resources
Demo (5:42)
Introduction (2:34)
Dataset Preparation (16:16)
Feature Engineering (14:56)
1 Model Building & Hyper-parameter tuning (20:10)
2 Model Building & Hyper-parameter tuning (20:55)
3 Model Building & Hyper-parameter tuning (28:30)
4 Model Building & Hyper-parameter tuning (10:37)
CLOUD :: Amazon Web Service (AWS) Deployment
Lecture resources
AWS Deployment Introduction (2:47)
AWS Dataset Intro (2:13)
AWS Creating App.py File For Deployment (16:07)
PART 2: AWS Deployment (14:14)
CLOUD :: Microsoft Azure Deployment
Lecture resources
Azure Deployment (23:09)
WEEK 2 :: PROJECTS SESSION: MACHINE LEARNING
Overview
ML PROJECTS: Building a Netflix Recommendation System
Project files
Building a Netflix Recommendation System (6:15)
Data Preparation (PART 1) (18:10)
Data Preparation (PART 2) (26:32)
1 Data Preparation (PART 3&4) (14:53)
1 Data Preparation (PART 3&4) Con't (12:17)
Data Preparation (PART 5) (16:08)
Main.py (PART 1.1) (5:44)
Main.py (PART 1.2) (13:59)
Main.py (PART 2) (12:31)
Preparing HTML Files 1.1 (4:09)
Preparing HTML Files 1.2 (7:02)
Preparing HTML Files 2.1 (5:44)
Preparing HTML Files 2.2 (12:53)
Final Heroku Cloud Deployment (9:00)
Optional: How to Fix Errors when deploying (3:26)
5TH MONTH
Overview
ML PROJECTS: Building CRUD App
Project files
CRUD Project Overview (5:04)
Building CRUD App (28:26)
ML PROJECT: Building Covid-19 Report Dashboard for Berlin City
Project files
Project Overview (3:25)
Building a Covid Dashboard App for Berlin City (43:01)
ML PROJECTS: Building IPL Score Predictor App
Project files
ML Project: Building IPL Score Predictor App (1:48)
Dataset Overview (5:38)
Exploratory Data Analysis (9:45)
Dealing With Categorical Values (3:04)
Model Building (5:44)
App.py (9:23)
Index.html and style.css (25:43)
ML PROJECTS: Building a Sales Forcast App
Project files
Building A Sales Forecast App (3:01)
Exploratory Data Analysis (5:50)
Feature Creation (11:46)
Feature Correlation and Multicollinearity (16:36)
Dealing with Outliers (24:03)
Building the ML Model (13:38)
Deploy with Flask (11:50)
ML PROJECTS: Building A Breast Cancer Predictor App : PART 2
Project resources
ML Project: Building A Breast Cancer Predictor App (3:54)
Dataset Overview (5:42)
Exploratory Data Analysis (12:56)
EDA With Visualization (6:17)
Building ML Model (18:05)
Walkthrough Of App.py (10:44)
Walkthrough Of Index.html and Static files (10:45)
WEEK 4 :: SCIENTIFIC RESEARCH PAPER
Lecture Resources
Reading Scientific Paper: An Overview (2:12)
What you will learn (0:50)
What is a Scientific Research Paper? (3:20)
Importance of Reading Research Papers (9:50)
Components of a Research Paper (11:06)
PART 1: How to Read Scientific Research Papers (16:43)
Part 2: How to Read Scientific Research Papers (27:50)
Where to find Data Science research papers (12:52)
Assignment (4:47)
6TH MONTH
Overview
ARTIFICIAL INTELLIGENCE
Lecture resources
Artificial Intelligence: An Introduction (1:45)
The Big Picture of AI (4:54)
WEEK 1 :: DEEP LEARNING
Introduction To Deep Learning (8:25)
What you will learn (1:53)
What is Artificial Neural Network? (10:35)
Neurons and Perceptrons (8:54)
Machine Learning vs Deep Learning (3:22)
Why Deep Learning (9:39)
Applications of Deep Learning (7:24)
Artificial Neural Network
Neural Network: An Overview (0:37)
Architecture: Components of the Perceptron (6:42)
Fully Connected Neural Network (2:34)
Types of Neural Networks (1:22)
How Neural Networks work (5:15)
Propagation: Forward and Back Propagation (4:31)
Understanding Neural Network
Hands-on of Forward and Back Propagation (PART 1) (17:18)
Hands-on of Forward and Back Propagation (PART 2) (20:03)
Chain Rule in Backpropagation (23:46)
Optimizers In NN
WEEK 2 :: Activation Functions
Activation Functions: An Introduction (9:51)
Sigmoid Activation Function (6:47)
Vanishing Gradient (14:15)
TanH Activation Function (5:47)
ReLU Activation Function (7:23)
Leaky ReLU Activation Function (3:08)
ELU Activation Function (5:52)
SoftMax Activation Function (9:04)
Activation functions summary (2:40)
Tensorflow and Keras
Overview (2:03)
Introduction to Tensorflow (6:47)
Tensors and Dataflows in Tensorflow (4:03)
Tensorflow Versions (2:24)
Keras (3:29)
LAB SESSION: Deep Learning(ANN)
Lecture resources
LAB SESSION 1: Building your first Neural Network (15:08)
LAB SESSION 2: Building your first Neural Network (31:55)
Handling Overfitting in Neural Network (24:59)
L2 Regularisation (4:53)
Dropout for Overfitting in Neural Network (5:55)
Early Stopping for overfitting in NN (8:14)
ModelCheck pointing (7:13)
Load best weight (2:24)
Tensorflow Playground (7:44)
1 Building Your Third Neural Network with MNIST (29:40)
2 Building Your Third Neural Network with MNIST (16:03)
WEEK 3 :: FULL COMPUTER VISION COURSE
Lecture resources
COMPUTER VISION (CV): Beginner Level
Lecture resources
Working with Images (2:01)
The concept of Pixels (4:11)
Gray-Scale Image (5:12)
Color Image (4:29)
Different Image formats (3:47)
Image Transformation: Filtering (1:06)
Affine and Projective Transformation (3:21)
Image Feature Extraction (5:00)
LAB SESSION : working with images (23:07)
CPU vs GPU vs TPU
Lecture resources
Introduction to CPUs, GPUs and TPUs (13:29)
Accessing GPUs for Deep Learning (4:05)
CPU vs GPU speed (4:22)
COMPUTER VISION: Intermediate Level
Lecture resources
Understanding Convolution (PART 1) (13:12)
Understanding Convolution (PART 2) (7:03)
Convolution Operation (21:20)
Understanding : Filter/Kernel | Feature Map | Input Volume | Receptive Field (10:29)
Stride and Step Size (2:00)
Padding (8:11)
Pooling (16:36)
Understanding CNN Architecture (15:25)
LAB SESSION: CNN Lab 1 (45:49)
LAB SESSION: CNN Lab 2 (37:13)
WEEK 4 :: COMPUTER VISION: Advanced Level
Overview
Lecture resources
CNN Architectures
State-of-the-Art CNN architecture (6:19)
LeNet Architecture (28:27)
LAB SESSION: LeNet LAB (7:30)
AlexNet Architecture (2:36)
LAB SESSION: AlexNet LAB (11:38)
VGG Architecture and LAB (24:33)
GoogleNet or Inception Net (9:54)
Transfer Learning
Lecture resources
Understanding Transfer Learning (22:18)
Steps to perform transfer learning (4:30)
When to use Transfer learning and when NOT to use. (3:33)
LAB SESSION: Transfer Learning with VGG-16 (11:33)
Object Detection
Overview and Agenda (1:39)
Computer Vision Task (2:13)
Datasets Powering Object Detection (5:00)
Image Classification vs Image Localisation (11:09)
Challenges of Object Detection (4:22)
Performance Metrics for Object Detection
Intersection Over Union(IoU) (7:20)
Precision and Recall (6:13)
Mean Average Precision(mAP) (0:55)
Objection Detection Techniques
Lecture resources
Overview (0:47)
Brute Force Approach (1:28)
Sliding Window (1:49)
Region Proposal (4:33)
R-CNN (5:31)
Fast R-CNN (5:58)
ROI Pooling (9:01)
Faster R-CNN (12:05)
State-of-the-Art Algorithms (2:40)
YOLO (16:35)
LAB SESSION 1: YOLO LAB Overview (1:38)
LAB SESSION 2: YOLO (23:21)
LAB SESSION 3.1: YOLO (8:03)
LAB SESSION 3.2: YOLO (35:22)
SSD (5:56)
7TH MONTH
Overview
WEEK 1 :: OPENCV FULL TUTORIAL
Lecture resources
Introduction To OpenCV (3:43)
Opencv Installation (7:45)
Opencv Setup (2:16)
Reading Images (5:32)
Reading Video (9:04)
Live Streaming with OpenCV (4:00)
Stacking Images together (10:46)
OpenCV Join (9:03)
OpenCV Functions (5:39)
Image Detection Techniques (2:54)
Edge Detection (2:25)
Dilation and Erode (10:24)
OpenCV Conventions (4:19)
Adding Shapes (9:44)
Creating Lines (4:16)
Creating Shapes(Rectangle) (2:34)
Adding Text (4:28)
Warp Perspective (16:15)
IMAGE: Face Detection with OpenCV (11:05)
VIDEO: Face Detection with OpenCV (6:09)
PROJECTS: COMPUTER VISION PROJECTS
Overview
CV PROJECT: Car Parking Space Counter Using OpenCV
Project resources
Car Park Counter with OpenCV: Project Overview (2:50)
PART 1: Building Car Park Counter With OpenCV (38:06)
PART 2: Building Car Park Counter With OpenCV (33:21)
PART 3: Building Car Park Counter With OpenCV (21:01)
CV PROJECT(Kaggle): Fruit and Vegetable Classification
Project resources
PROJECT: Fruit and Vegetable Classification Overview (7:04)
Setup your First Kaggle Code Notebook (7:07)
Building Fruit and Vegetable Classifier with Kaggle Notebooks (46:42)
Deploy a Computer Vision Classifier App (21:44)
CV PROJECT: Predicting Lung Disease with Computer Vision
Project resources
Predicting Lung Disease (26:26)
WEEK 2 :: CV PROJECT: Nose Mask Detection with Computer Vision
Project resources
Data Preprocessing (12:01)
Training the CNN (4:51)
Detecting Face Mask (13:38)
CV PROJECT: Pose Detection
Project resources
Building a Pose Detector (3:28)
LAB: Building a Pose Detector 1 (8:06)
LAB: Building a Pose Detector 2 (16:39)
CV PROJECT: Building a virtual AI Keyboard
Project resources
CV Project : Building AI Virtual Keyboard (1:02)
Building AI Virtual Keyboard (PART 1) (11:26)
Building AI Virtual Keyboard (PART 2) (7:23)
Building AI Virtual Keyboard (PART 3) (11:24)
Building AI Virtual Keyboard (PART 4) (12:45)
Building AI Virtual Keyboard (PART 5) (3:44)
Building AI Virtual Keyboard (PART 6) (10:20)
Building AI Virtual Keyboard (PART 7) (5:21)
Building AI Virtual Keyboard (PART 8) (6:02)
CV PROJECT: Yolov4 Object Detection Using Webcam
Project resources
Yolov4 Object Detection Using Webcam (28:19)
WEEK 3 :: NATURAL LANGUAGE PROCESSING(NLP)
Lecture resources
Overview (0:50)
Recapitulation (3:22)
What is NLP? (3:39)
Applications of NLP (9:21)
The Must-Know NLP Terminologies (2:40)
Word (0:39)
Tokens and Tokenizations (6:20)
Corpus (1:32)
Sentence and Document (2:17)
Vocabulary (0:36)
Stopwords (3:15)
Hands-On NLP: Text Pre-processing
Lecture resources
Tokenization with NLTK , SpaCy and Gensim (15:24)
Removing Stopwords with NLP Libraries (23:11)
Text Pre-processing: Normalization
Lecture resources
Text Normalization (5:09)
Stemming and Lemmatization (2:48)
LAB SESSION: Stemming and Lemmatization (13:55)
WEEK 4 :: Part Of Speech (POS) Tagging
Lecture resources
Understanding POS Tagging (7:55)
LAB SESSION: Part of Speech Tagging (5:03)
Chunking (11:05)
Sentence Parsing
Sentence Parsing (3:43)
Chunking & Chinking & Syntax Tree (6:40)
Hands-On Text Pre-processing
Advanced Text Preprocessing (29:26)
Frequency of Words | Bi-Gram | N-Grams (3:36)
More on Stemming and Lemmatization (8:32)
Introduction To Statistical NLP Techniques
Lecture resources
Bag of Words (BoW) (10:55)
TF-IDF (12:04)
Language Modeling
Understanding language modeling
8TH MONTH
Overview
WEEK 1 :: INTERMEDIATE LEVEL: Word Embeddings
Understanding Word Embeddings (13:34)
Feature Representations (13:06)
Word2Vec
Lecture resources
The Challenge with BoW and TF-IDF (6:19)
Understanding Word2Vec (3:30)
LAB SESSION: Word2Vec (13:08)
CBOW and Skip-Gram (5:52)
GloVe
Understanding GloVe (8:11)
Glove Lab
Sequential Models
Sequential Model: An Introduction (5:02)
Traditional ML vs Sequential Modeling (7:31)
WEEK 2 :: ADVANCED LEVEL: Recurrent Neural Network (RNN)
What is a Recurrent Neural Network (RNN) ? (8:15)
Types of RNNs (3:46)
Use Cases of RNNs (5:10)
Vanilla Neural Network (NN) vs Recurrent Neural Network (RNN) (4:10)
Backpropagation Through Time (BTT) (4:35)
Mathematics Behind BTT (5:16)
Vanishing and Exploding Gradient (2:45)
The problem of Long Term Dependencies (5:06)
Bidirectional RNN (BRNN) (2:32)
Gated Recurrent Unit(GRU) (1:52)
LSTM
Lecture resources
LSTM: An Introduction (11:26)
The LSTM Architecture (17:46)
LAB SESSION 1: LSTM (24:06)
LAB SESSION 2.1: Tweet Sentiment Analysis using RNN (21:05)
LAB SESSION 2.2: Tweet Sentiment Analysis using RNN (21:27)
LAB SESSION 3: Tweet Sentiment Analysis using LSTM (8:47)
Sequence To Sequence Models (Seq2Seq)
Lecture resources
Sequence To Sequence models: An introduction (7:32)
Encoder & Decoder (11:58)
LAB SESSION: Language Translation (1:19)
LAB SESSION 2: Language Translation (19:42)
WEEK 3 :: NLP PROJECT: Sentiment Analyzer
Lecture Resources
Building Sentiment Analyzer App (3:47)
LAB: Building Sentiment Analyzer App (19:12)
Name Entity Recognition (NER)
Lecture resources
NER : An Introduction (6:20)
Example of Name Entity Recognition (3:17)
How Name Entity Recognition works (3:38)
Applications of NER (4:29)
LAB SESSION: Hands-On Name Entity Recognition (0:37)
LAB SESSION 2: Name Entity Recognition (16:13)
LAB SESSION: Visualizing Name Entity Recognition (9:17)
Assignment (1:26)
NLP PROJECT: Building a Name Entity Recognition App
Project: Building a Name Entity Recognition Web App (4:12)
Project: Building your NER web App (21:08)
NLP PROJECT: AI Resume Analyzer App
Lecture resources
NLP Project: Building AI Resume Analyzer (9:13)
AI Resume Analyzer (PART 1A) (10:41)
AI Resume Analyzer (PART 1B) (17:12)
AI Resume Analyzer (PART 2) (7:57)
AI Resume Analyzer (PART 3) (22:19)
AI Resume Analyzer (PART 4) (16:33)
AI Resume Analyzer (PART 5) (14:20)
WEEK 4 :: Microsoft Power BI
Lecture resources
Power BI: An Introduction (11:50)
Installation (4:46)
Query Editor Overview (5:03)
Connectors and Get Data Into Power BI (8:51)
Clean up Messy Data (PART 1) (15:33)
Clean up Messy Data (PART 2) (6:20)
Clean up Messy Data (PART 3) (1:11)
Creating Relationships (8:40)
Explore Data Using Visuals (12:18)
Analyzing Multiple Data Tables Together (4:47)
Writing DAX Measure (Implicit vs. Explicit Measures) (6:41)
Calculated Column (6:10)
Measure vs. Calculated Column (7:36)
Hybrid Measures (9:28)
The 80/20 Rule (1:43)
Text, Image, Cards, Shape (13:33)
Conditional Formatting (4:02)
Line Chart, Bar Chart (4:41)
Top 10 Products/Customers (8:23)
9TH MONTH
Hackathons
INTERNSHIPS & JOBS
Internship Overview (8:55)
Instructor Guide To Finding Internships & Jobs (13:52)
Lecture Resources 1
Lesson content locked
If you're already enrolled,
you'll need to login
.
Enroll in Course to Unlock