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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)
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
Sentence Parsing
Sentence Parsing (3:43)
Chunking & Chinking & Syntax Tree (6:40)
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)
Confusion Matrix: True Positive | False Positive | True Negative | False Neg.
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