Autoplay
Autocomplete
Previous Lesson
Complete and Continue
Foundations of Artificial Intelligence
Python
Google Colab - Part 1 (17:36)
Google Colab - Part 2 (4:43)
Accessing and Methods In Tuples (7:26)
Accessing Dictionaries (4:45)
Accessing Lists (8:03)
Anaconda Installation (6:53)
Arithmetic Operators (6:05)
Break and Continue Statement (9:58)
Conditionals (8:18)
Creating Dictionaries (8:44)
Creating Lists (8:48)
Creating Sets (4:07)
Creating Tuples (4:57)
Data Types - Integer (8:00)
Data Types - Boolean (7:37)
Data Types - Float (5:19)
Data Types - String (11:28)
Introduction to Dictionaries (9:01)
elif & else (8:52)
Expression Evaluation (12:39)
For Loop With Range (11:06)
For Loop With Variables (6:28)
Functions (10:13)
Functions with Arguments (8:09)
Functions with Multiple Arguments (6:54)
Functions with Multiple Keyword Arguments (8:53)
Functions Without Arguments (9:14)
If Statements (8:31)
Indexing and Slicing In Strings (13:54)
Jupyterv2 (5:33)
Introduction to Lists (6:37)
Logical Operations in Conditionals (11:20)
Loops (5:02)
Methods in Dictionaries (14:00)
Methods in List - Part 1 (13:06)
Methods in List - Part 2 (10:11)
Methods in Sets - Part 1 (8:26)
Methods in Sets - Part 2 (7:29)
Nested if Statements (14:03)
Python Properties and Applications (18:08)
Introduction to Recursion (4:19)
Recursion Summation Function - Part 1 (5:13)
Recursion Summation Function - Part 2 (4:58)
Recursion-_Base_Case (5:13)
Scope Of A Function (7:08)
Introduction to Sets (6:23)
String Access Using Loops (7:14)
Introduction to Tuples (6:18)
Understanding Programming (15:18)
Variables and Values (9:42)
While (8:46)
Descriptive Statistics
Statistics & Data (6:07)
Qualitative Data (5:25)
Quantitative Data (9:03)
Sample and Population (8:01)
Sampling Techniques (11:34)
Data Types (8:52)
Measures of Central Tendency (Mean, Median and Mode) (16:15)
Measures of Dispersion (Variance, Sd and IQR) and Skewness (21:23)
Bar Plot (4:30)
Pie Chart (2:32)
Histograms (3:31)
Box Whiskers - Plot (2:51)
Scatter Plots (2:25)
Normal Distribution (8:31)
Correlation (7:23)
T-Distribution and Degree of Freedom (6:04)
One Sample T-Test (5:29)
Z-Test (5:25)
Independent Sample T-Test (6:25)
Paired T-Test (5:09)
One Way Anova (15:45)
Two Way Anova (4:01)
Chi-Square Test (8:29)
Statistical Model using Python (12:54)
Introduction to Probability (6:57)
Null and Alternative Hypothesis (3:45)
Confidence Interval (12:49)
One Tailed v/s Two Tailed (4:53)
Significance Value - P Value (3:11)
Type i and Type ii Error (4:55)
Statistical Power (2:29)
Binomial Distributions (7:47)
Central Limit Theorem (13:31)
Bays Inference (4:52)
Poissons Distributions (7:04)
Important Concept Event Set, Subset, Samplespace (5:17)
Important Concepts Intersection Union Complement (7:14)
Disjoint, Non-disjoint and Independent Event (8:39)
Conditional Probability (4:06)
F-Distribution (2:55)
Contingency Table (7:52)
Pearsons Correlation Coefficient (8:48)
Graphs Hands On (7:05)
Data Type (8:52)
Machine Learning
Supervised Learning: Regression (11:58)
Supervised Learning : Classification (14:50)
What is a Decision Tree (4:41)
Decision Tree in Brief (5:23)
Terminologies Used (6:01)
What is Random Forest? (2:58)
Working Philosophy (6:27)
Why The Name - Part 1 (8:42)
Why The Name - Part 2 (5:42)
Terminologies & Real-Life Examples (4:00)
What is K-Nearest Neighbour? (3:06)
Walk Through Sci-Kit Website (3:57)
Basics of Support Vector Machine (4:38)
Kernel, Gamma & C Value (6:12)
Neural Networks (16:57)
Ensemble Methods (11:00)
What Is Unsupervised Learning? (7:06)
What is K-Means & Clustering? (6:30)
Understanding PCA (8:23)
What is Market Basket Analysis (5:13)
ACF and PACF (8:40)
Analogy of TSA (6:24)
Deep Learning - ANN (66:41)
How does this work? (6:20)
How does it work (8:01)
Introduction to TSA (6:07)
Parts of TSA (8:52)
Matrix multiplication (8:58)
NLP PART 1 (9:51)
NLP PART 2 (16:53)
NLP PART 3 (15:14)
NLP PART 4 (16:51)
NLP PART 5 (11:31)
NLP PART 6 (13:44)
NLP PART 7 (17:01)
NLP PART 8 (14:44)
One tailed and two tailed tests (8:17)
Null and alternative hypothesis (10:04)
Open CV - Part 1 (9:00)
Open CV - Part 2 (12:23)
Open CV - Part 3 (24:37)
Open CV - Part 4 (13:04)
Open CV - Part 5 (9:10)
Open CV - Part 6 (5:05)
Open CV - Part 7 (14:34)
Open CV - Part 8 (5:31)
RNN - Part 1 (24:16)
RNN - Part 2 (12:50)
RNN - Part 3 (16:18)
Reinforcement learning (29:27)
Rolling Stats (7:37)
Scalar ,vector, matrix and arrays (12:39)
Scikit-Image - Part 1 (13:42)
Scikit-Image - Part 2 (13:53)
Scikit-Image - Part 3 (15:30)
Scikit-Image - Part 4 (16:13)
Significance Level and P Value (8:12)
Type I and Type II Errors (5:52)
Understand NLP In Detail (9:21)
What is Naive Bayes Theorem (7:25)
Deep Learning - CNN Part 1 (27:54)
Deep Learning - CNN Part 2 (27:02)
Case Study - 1 (3:05)
Case Study - 2 (7:20)
Case Study - 3 (8:18)
Case Study - 4 (10:35)
Case Study - 5 (6:56)
Case Study - 6 (9:03)
Case Study - 7 (7:42)
Case Study - 8 (5:19)
Case Study - 9 (9:29)
SQL
Introduction to SQL (18:30)
MySQL Workbench Installation for Windows (5:04)
MYSQL Workbench Installation For MAC (5:27)
Create Database (33:51)
Insert (8:46)
Alter (16:51)
Select (31:47)
String Functions (26:48)
Numeric and Temporal functions (21:40)
SQL functions- Order by, Limit (5:02)
Like and ILike (wildcards) (6:30)
Aggregate functions in SQL (5:21)
Group By (4:05)
Having (3:59)
SQL Joins (4:58)
Inner Join (7:47)
Full outer join (4:08)
Left Outer Join (5:27)
Right Outer Join (4:18)
Union (4:17)
Database normalization (5:48)
Types of normal forms-1 (10:07)
Types of Normal forms-2 (5:23)
Clustered and non clustered index (3:28)
Temporary Tables (5:47)
SQL views (5:23)
Subqueries (5:24)
AWS RDS (19:08)
Case Statement (6:41)
Coalesce & IFnull (4:31)
Collections and Documents (11:56)
Window Functions - 1 (9:51)
Window Functions - 2 (8:12)
Window Functions - 3 (11:03)
Window Functions - 4 (14:08)
Window Functions - 5 (9:44)
Update and delete document (6:14)
Import Export, DB Backup (17:38)
Introduction To NoSQL and Its Significance (10:57)
Joins (13:23)
JSON Datatypes (11:04)
JSON & Its Features (9:31)
Large Databases and Clustering (29:15)
Introduction to Reliability (34:03)
Metadata (29:15)
Privileges and Roles (14:55)
SQL Query Optimization (8:40)
Server Client Configurations (18:48)
Importing SQL Files In Workbench (3:25)
Transactions and Locking - 1 (6:51)
Transactions and Locking - 2 (5:39)
Transactions and Locking - 3 (14:38)
Transactions and Locking - 4 (11:39)
Triggers - 1 (5:21)
Triggers - 2 (12:05)
Triggers - 3 (10:15)
Triggers - 4 (9:44)
Intro to MongoDB installation (12:15)
Datatypes, Insert and Query Documents in MongoDB Database (9:11)
Common Table Expression (CTE) (4:24)
Cloud Exposure Fetching Data (7:59)
Quiz
Python - Quiz
Descriptive Statistics - Quiz
Machine Learning - Quiz
SQL - Quiz
Measures of Dispersion (Variance, Sd and IQR) and Skewness
Lesson content locked
If you're already enrolled,
you'll need to login
.
Enroll in Course to Unlock