Data Science and Artificial Intelligence (AI) Certification

1.2: Why learning SQL?

1.3: Explaining DataLake, DataWarehouse and DataMart

1.4: SQL Server vs MySQL vs PostgresSQL

2.1: Installation and Introduction to Azure Data Studio

2.2: Creating a Connection to a Database

2.3: Company Profile: Adventure Works?

2.4: SELECT and FROM Statements

2.5: SELECT Specific Columns

2.6: Creating a Column Alias

2.7: Using WHERE Statement to Filter Rows

2.8: Checking the Impact of a WHERE Filter

2.9: Using GROUP BY Statement to Combine Rows

2.10: Limiting Results to 1 Row for Testing

2.11: Using GROUP BY to Combine Rows

2.12: Using HAVING Statement to Filter Grouped Rows

2.13: Filtering Grouped Rows with HAVING

2.14: SQL Order of Operations

2.15: Using ORDER BY to Sort Query Rows

2.16: Filtering Rows TOP N

2.17: Filtering Rows TOP N Percent

2.18: Filtering Rows using OFFSET FETCH

2.19: Filtering Rows DISTINCT Values

3.1: Counting Rows with COUNT( ) Aggregation

3.2: How Aggregate Functions Respond to NULL Values

3.3: The Importance of Data Types

3.4: Numeric Data Types

3.5: Numeric Functions

3.6: Text or String Data Types

3.7: String Functions

3.8: Comparison Operators

3.9: Comparison Operators – Dealing with NULL

3.10: Logical Operators

3.11: Logical Operators – Common Errors

3.12: Advanced Logical Operators – IN and BETWEEN

3.13: Advanced Logical Operators – LIKE

3.14: Using IIF Statements to Create a Conditional Column

3.15: Using a CASE Statement for Multiple Conditions

3.16: Basic SQL Formatting

3.17: Using IIF in a WHERE Statement

3.18: Replacing NULL Using IIF and ISNULL

3.19: Using CAST to Change the Data Type

4.1: Date and Time Data Types

4.2: Date Parts

4.3: Date and Time Functions

4.4: The DATEADD Function

4.5: Working with Specific Dates

5.1: Fact and Dimension Tables

5.2: Relationships & Keys

5.3: The Star Schema

5.4: Snowflake Hybrid Schema

6.1: Relationships and ER Diagrams

6.2: Purpose of DW Relationships

6.3: Types of JOIN

6.4: A Basic INNER JOIN Using Sales and Customers

6.5: Returning Only the TOP 100 Customers

6.6: INNER JOIN the another Table

6.7: HAVING or WHERE Statement

6.8: When INNER JOIN Doesn’t Work

6.8: When INNER JOIN Doesn’t Work

6.9: Is INNER and LEFT Join are same in Industries?

6.10: RIGHT JOIN Application

6.11: USING Keyword

6.12: Appending Data with a UNION

6.13: Creating a UNION between Fact Tables

6.14: Identifying the Source of Each UNION Row

6.15: Using ORDER BY with a UNION

6.16: Creating a View

6.17: Querying a View

7.1: Installing MySQL

7.2: Importing Movie Dataset in MySQL

7.2: Importing Movie Dataset in MySQL

7.3: Subqueries – Scalar, Multi and Tables

7.3: Subqueries – Scalar, Multi and Tables

7.4: ANY, ALL Operators

7.5: Co-Related Subquery

7.5: Co-Related Subquery

7.6: Common Table Expression (CTE)

7.7: CTE Benefits & Other Applications

1.1: How Learning Power BI Can Help You in Your Career?

1.2: Downloading: Power BI Desktop

1.3: Adjusting Options and Settings

1.4: Power BI Desktop Interface & Workflow

1.4: Basic Table Transformations

2.1: Power BI Front-End vs. Back-End

2.2: Types of Data Connectors (Files, Databases, Folders)

2.3: The Power Query Editor

2.4: Basic Table Transformations

2.5: Data QA & Profiling Tools

2.6: Text Tools

2.7: Numerical Tools

2.8: Date & Time Tools

2.9: Change Type with Locale

2.10: Conditional Columns

2.11: Calculated Column Best Practices

2.12: Grouping & Aggregating

2.13: Merging Queries

2.14: Appending Queries

2.15: Appending Files from a Folder (Connecting to Folder)

2.16: Data Source Settings

2.17: Data Source Parameters

2.18: Refreshing Queries

3.1: Database Normalization

3.2: Explaining Data Normalization (Need) in Excel

3.3: Expanded Tables

3.4: Context Transition

3.5: Evaluation Order

3.6: Fact & Dimension Tables

3.7: Primary & Foreign Keys

3.8: Relationships vs. Merged Tables

3.9: Creating Table Relationships

3.10: Managing & Editing Relationships

3.11: Star & Snowflake Schemas

3.12: Active & Inactive Relationships

3.13: Relationship Cardinality

3.14: Connecting Multiple Fact Tables

3.15: Hiding Fields from Report View

3.16: Data Formats & Categories

3.17: Creating Hierarchies

4.1: Intro to DAX Calculated Columns

4.2: Common Text Functions

4.3: Basic Date & Time Functions

4.4: Conditional & Logical Functions

4.5: The SWITCH Function

5.1: Intro to DAX Measures

5.2: Implicit vs. Explicit Measures

5.2: Implicit vs. Explicit Measures

5.3: Dedicated Measure Tables

5.4: Understanding Filter Context and Filter Flow

5.5: Step-by-Step DAX Measure Calculation

5.6: Common DAX Function Categories

5.7: Basic Math & Stats Functions

5.8: Counting Functions

5.9: Joining Data with RELATED

5.10: The CALCULATE Function

5.11: DAX Measure Totals

5.12: The ALL Function

5.13: The FILTER Function

5.14: Iterator (X) Functions

5.15: Time Intelligence Functions

5.16: Variables

6.1: The 3 Key Questions

6.2: Sketching the Dashboard Layout

6.3: Adding Report Pages & Objects

6.4: Cards & Multi-Row Cards

6.5: Building & Formatting Charts

6.6: Line Charts

6.7: Trend Lines & Forecasts

6.8: KPI Cards

6.9: Bar & Donut Charts

6.10: Basic Filtering Options

6.11: Table & Matrix Visuals

6.12: Conditional formatting

6.13: Top N Filtering

6.14: Top N Text Cards

6.15: Map Visuals

6.16: Report Slicers

6.17: Gauge Charts

6.18: Advanced Conditional Formatting

6.19: Area Charts

6.20: Drill Up & Drill Down

6.21: Drillthrough Filters

6.22: Editing Report Interactions

6.23: Adding Bookmarks

6.24: Custom Navigation Buttons

6.25: Slicer Panels

6.26: Numeric Range Parameters

6.27: Fields Parameters

6.28: Custom Tool Tips

7.1: Anomaly Detection

7.2: Smart Narratives

7.3: Q&A Visuals

7.4: Decomposition Trees

7.5: Key Influencers

9.10: Project - Power BI: 365 Media Feathers (Guided Project)

1.1: Introduction to Programming

1.2: Why Python?

1.3: Why Jupyter Notebook?

1.4: Installing Python and Jupyter

1.5: Understanding Jupyter’s Interface – the Notebook Dashboard

2.1: Variables

2.2: Numbers and Boolean Values in Python

2.3: Python Strings

2.4: Using Arithmetic Operators in Python

2.5: The Double Equality Sign

2.6: How to Reassign Values

2.7: Add Comments

2.8: Understanding Line Continuation

2.9: Indexing Elements

2.10: Structuring with Indentation

2.11: Comparison Operators

2.12: Logical and Identity Operators

3.1: The IF Statement

3.2: The ELSE Statement

3.3: The ELIF Statement

4.1: Defining a Function in Python

4.2: How to Create a Function with a Parameter

4.3: Defining a Function in Python – Part II

4.4: How to Use a Function within a Function

4.5: Conditional Statements and Functions

4.6: Functions Containing a Few Arguments

4.7: Built-in Functions in Python

5.1: Lists

5.2: Using Methods

5.3: List Slicing

5.4: Tuples

5.6: Dictionaries

6.1: For Loops

6.2: While Loops and Incrementing

6.3: Lists with the range() Function

6.4: Conditional Statements and Loops

6.5: Conditional Statements, Functions, and Loops

6.6: How to Iterate over Dictionaries

7.1: Introduction to NumPy

7.2: NumPy Arrays

7.3: NumPy Array Indexing

7.4: NumPy Operations

8.1: Introduction to Pandas

8.2: Panda Series

8.3: DataFrames – Creating a DataFrame

8.4: DataFrames – Basic Properties

8.5: DataFrames – Working with Columns

8.6: DataFrames – Working with Rows

8.7: Conditional Filtering

8.8: Pandas – Apply on Single Column and Multiple Columns

8.9: Statistical Information and Sorting

8.10: Missing Data – Overview and Pandas Operations

8.11: Group By – Individual vs Multi Index

8.12: Combining DataFrames – Concatenation

8.13: Combining DataFrames – Inner Merge

8.14: Combining DataFrames – Outer Merge

8.15: Text Methods for String Data

8.16: Time Methods for Date and Time Data

8.17: Input and Output – CSV/ HTML/ Excel and SQL Databases

8.18: Pandas Pivot Tables

10.1: Introduction to Matplotlib

10.2: Matplotlib Basics

10.3: Understanding the Figure Object

10.4: Implementing Figures and Axes

10.5: Figure Parameters

10.6: Subplots Functionality

10.7: Styling - Legends

10.8: Styling - Colors and Styles

10.8: Styling - Colors and Styles

10.9: Advanced Matplotlib Commands (Optional)

12.1: Introduction to Seaborn

12.2: Distribution Plots

12.3: Categorical Plots

12.4: Matrix Plots

12.5: Grids

12.6: Regression Plots

12.7: Style and Color

13.1: Intro to Plotly and Dash

13.2: Plotly Charts

13.3: Interactive Elements

13.4: Dashboard Layouts

13.5: Advanced Topics

15.1: Gathering Data

15.2: Cleaning Data

15.3: Exploratory Data Analysis

17.1: Set and Frozenset

17.2: Lists, Dict and Set Comprehensions

17.3: PEP8 Naming Convention

17.4: Code Debugging Using PyCharm

18.1: Working with JSON

18.2: Generators and Iterators

18.3: Decorators

19.1: What is API?

19.2: Calling APIs With requests Package

19.3: Building APIs With FastAPI

20.1: Logging

20.2: Automated Testing with Pytest

20.3: MySQL Setup: Windows

20.4: MySQL Setup: Linux, Mac

20.6: Data Validation with Pydantic

20.6: Working with MySQL in Python

1.1: Descriptive vs. Inferential Statistics

1.2: Measures of Central Tendency: Mean, Median, Mode

1.3: Percentile

1.4: Analysis: Shoe Sales (Using Mean, Median, Percentile)

1.5: Measures of Dispersion: Range, IQR

1.6: Box or Whisker Plot

1.7: Outlier Treatment Using IQR and Box Plot

1.8: Measures of Dispersion: Variance and Standard Deviation

1.9: Analysis: Stock Returns Volatility (Using Variance and Std Dev)

1.10: Correlation

1.11: Correlation vs Causation

2.1: Probability Basics

2.2: Addition and Multiplication Rule

2.3: Conditional Probability and Bayes Theorem

3.1: What Is a Distribution?

3.2: Skewness

3.3: Normal Distribution

3.4: Detect Outliers Using Normal Distribution

3.5: Z Score

3.6: Standard Normal Distribution (SND)

4.1: Random Sampling & Sample Bias

4.2: The Law of Large Numbers

4.3: Central Limit Theorem, Sampling Distribution

4.4: Case Study: Solar Panels

4.5: Standard Error

4.6: Z Score Table (Z-Table)

4.7: Confidence Interval

4.8: Confidence Interval: Estimate Car Miles

5.1: Null vs Alternate Hypothesis

5.2: Z Test, Rejection Region

5.3: Housing Inflation Test: Rejection Region

5.4: p-Value

5.5: Housing Inflation Test: p-Value

5.6: One-Tailed vs Two-Tailed Test

5.7: Type 1 and Type 2 Errors

5.8: Statistical Power & Effect Size

5.9: A/B Testing

5.10: A/B Testing Using Z Test

5.11: A/B Testing: Drug Trial

6.1: T-test and Student’s t-distribution

6.2: Case Study - Exam Score

6.3: Chi-squared Distribution

6.4: Chi-squared Test of Goodness of Fit

6.5: Chi-squared Test of Independence

1.1: Importance of Machine Learning in Your Career

1.1: Importance of Machine Learning in Your Career

1.2: AI Family Tree

1.3: What is Machine Learning?

1.4: Classification vs Regression

1.5: Supervised vs Unsupervised Learning

2.1: Understanding Ordinary Least Squares

2.2: Scikit-Learn Performance Evaluation - Regression

2.3: Bias Variance Trade-Off

2.4: Polynomial Regression - Choosing Degree of Polynomial

2.5: Polynomial Regression - Model Deployment

2.6: Regularization Overview

2.7: Feature Scaling

2.8: Introduction to Cross Validation

2.9: Regularization Data Setup

2.10: L2 Regularization - Ridge Regression Theory

2.11: L2 Regularization - Ridge Regression - Python Implementation

2.12: L1 Regularization - Lasso Regression - Background and Implementation

2.13: L1 and L2 Regularization - Elastic Net

3.1: Introduction to Classification

3.2: Logistic Regression: Binary Classification

3.3: Model Evaluation: Accuracy, Precision and Recall

3.4: Model Evaluation: F1 Score, Confusion Matrix

3.5: Logistic Regression: Multiclass Classification

3.6: Cost Function: Log Loss

3.7: Support Vector Machine (SVM)

3.8: Data Pre-processing: Scaling

3.9: Sklearn Pipeline

3.10: Naive Bayes: Theory

3.11: Naive Bayes: SMS Spam Classification

3.12: Decision Tree: Theory

3.13: Decision Tree: Salary Classification

3.14: Handle Class Imbalance: Theory

3.15: Handle Class Imbalance Using imblearn: Churn Prediction

4.1: Introduction to KNN Section

4.2: KNN Classification - Theory and Intuition

4.3: KNN Coding with Python

4.4: KNN Coding with Python - Choosing K

4.5: KNN Classification Project Exercise and Solutions

5.1: Introduction to Support Vector Machines

5.2: History of Support Vector Machines

5.3: SVM - Theory and Intuition - Hyperplanes and Margins

5.4: SVM - Theory and Intuition - Kernel Intuition

5.5: SVM - Theory and Intuition - Kernel Trick and Mathematics

5.6: SVM with Scikit-Learn and Python - Classification

5.7: SVM with Scikit-Learn and Python - Regression Tasks

6.1: Introduction to Tree Based Methods

6.2: Decision Tree - History

6.3: Decision Tree - Terminology

6.4: Decision Tree - Understanding Gini Impurity

6.5: Constructing Decision Trees with Gini Impurity

6.6: Coding Decision Trees - The Data and Creating the Model

6.7: Introduction to Random Forests

6.7: Introduction to Random Forests

6.8: Random Forests - History and Motivation

6.9: Random Forests - Key Hyperparameters

6.10: Random Forests - Number of Estimators and Features in Subsets

6.11: Random Forests - Bootstrapping and Out-of-Bag Error

6.12: Coding Classification with Random Forest Classifier

6.13: Coding Regression with Random Forest Regressor - Data

6.14: Coding Regression with Random Forest Regressor - Basic Models

6.15: Coding Regression with Random Forest Regressor - Polynomials

6.16: Coding Regression with Random Forest Regressor - Advanced Models

7.1: Introduction to NLP and Naive Bayes

7.2: Naive Bayes Algorithm - Bayes Theorem

7.3: Naive Bayes Algorithm - Model Algorithm

7.4: Feature Extraction from Text - Theory and Intuition

7.5: Feature Extraction from Text - Coding Count Vectorization Manually

7.6: Feature Extraction from Text - Coding with Scikit-Learn

7.7: Natural Language Processing - Classification of Text

9.1: Unsupervised Learning Overview

10.1: Introduction to K-Means Clustering Section

10.2: Introduction to Hierarchical Clustering

10.2: Clustering General Overview

10.3: K-Means Clustering Theory

10.4: K-Means Clustering - Coding

10.5: K-Means Color Quantization

10.6: K-Means Clustering Exercise Overview and Solutions

10.8: Hierarchical Clustering - Theory and Intuition

10.9: Hierarchical Clustering - Coding - Data and Visualization

10.10: Hierarchical Clustering - Scikit-Learn

11.1: Introduction to DBSCAN

11.2: DBSCAN - Theory and Intuition

11.3: DBSCAN versus K-Means Clustering

11.4: DBSCAN - Hyperparameter Theory

11.5: DBSCAN - Hyperparameter Tuning Methods

11.6: DBSCAN - Outlier Project Exercise Overview and Solutions

12.1: Introduction to Principal Component Analysis

12.2: PCA Theory and Intuition

12.2: PCA Theory and Intuition

12.3: PCA - Manual Implementation in Python

12.4: PCA - SciKit-Learn

12.4: PCA - SciKit-Learn

17.1: What is ML Ops?

17.2: Importance of ML Ops in Your Career

17.3: ML Flow: Purpose and Overview

17.4: ML Flow: Experiment Tracking

17.5: ML Flow: Model Registry

17.6: ML Flow: Centralized Server Using Dagshub

17.7: What is API?

17.8: FastAPI Basics

17.9: Build FastAPI Server For Credit Risk Project

17.10: Git Version Control System

17.11: Introduction to ML Cloud Platforms

17.12: AWS Sagemaker: Account Setup

17.13: AWS Sagemaker: Sagemaker Studio

17.14: AWS Sagemaker: 4 Ways to Train Model

17.15: AWS Sagemaker: Built In Algorithms

17.16: AWS Sagemaker: Script Mode

17.17: Data Drift Detection Using PSI & CSI

17.18: PSI & CSI: Practical Implementation

1.1: What is Deep Learning

2.1: What You'll Need for ANN

2.2: Plan of Attack

2.3: The Neuron

2.4: The Activation Function

2.5: How do Neural Networks work?

2.6: How do Neural Networks learn?

2.7: Gradient Descent

2.8: Stochastic Gradient Descent

2.9: Backpropagation

2.10: Building an Artificial Neural Network (ANN)

3.1: What You'll Need for CNN

3.2: What are Convolutional Neural Networks?

3.3: Convolution Operations

3.4: ReLU Layer

3.5: Pooling

3.6: Flattening

3.7: Full Connection

3.8: Softmax & Cross-Entropy

3.9: Building a Convolutional Neural Networks (CNNs)

4.1: The idea behind Recurring Neural Networks (RNN)

4.2: The Vanishing Gradient Problem

4.3: LSTMs

4.4: Practical Intuition

4.5: EXTRA: LSTM Variations

4.6: Building a Recurrent Neural Networks (RNNs)

5.1: Attention Mechanism

5.2: Architecture Overview: Encoder-Decoder Structure

5.3: Multi-Head Attention

5.4: Positional Encoding

5.5: Feed-Forward Networks and Layer Normalization

5.6: Scalability and Parallelization

1.1: What is Natural Language Processing?

1.2: Spacy Basics

1.3: Tokenization

1.4: Stemming

1.5: Lemmatization

1.6: Stop Words

1.7: Phrase Matching and Vocabulary

2.1: Introduction to Text Classification

2.2: Classification Metrics

2.3: Confusion Matrix

2.4: Scikit-Learn Primer - How to Use SciKit-Learn

2.5: Scikit-Learn Primer - Code Along

2.6: Text Feature Extraction Overview

2.7: Text Feature Extraction - Code Along Implementations

2.8: Text Classification Code Along Project

3.1: Introduction to Semantics and Sentiment Analysis

3.2: Overview of Semantics and Word Vectors

3.3: Semantics and Word Vectors with Spacy

3.4: Sentiment Analysis Overview

3.5: Sentiment Analysis with NLTK

3.6: Sentiment Analysis Code Along Movie Review Project

4.1: The Basic Perceptron Model

4.2: Keras Basics

4.3: Text Generation with LSTMs with Keras and Python

4.4: Chat Bots Overview

4.5: Creating Chat Bots with Python

3.1: End To End ML Project With Deployment-Github And Code Set Up

3.2: Implementing Project Structure, Logging And Exception Handling

3.3: Discussing Project Problem Statement, EDA And Model Training

3.3: Discussing Project Problem Statement, EDA And Model Training

3.4: Data Ingestion Implementation

3.5: Data Transformation Using Pipelines Implementation

3.6: Model Trainer Implementation

3.7: Model Hyperparameter Tuning Implementation

3.8: Building Prediction Pipeline

3.9: ML Project Deployment Using AWS Beanstalk

3.10: Deployment EC2 Instance With ECR

3.11: End To End ML Project With Deployment-Github And Code Set Up

4.1: Transformer Types

4.2: Introduction to Transformers

4.3: Self Attention

4.4: Encoder Architecture

4.5: Contextual Embeddings

4.6: Decoder Architecture

4.7: Introduction to BERT

4.8: Configurations of BERT

4.9: BERT: Fine Tuning

4.10: BERT: Pre Tuning (Masked LM)

4.11: BERT: Input Embeddings

4.12: ARLM vs AELM

4.13: RoBERTa

4.14: DistilBERT

4.15: AlBERT

4.16: Introduction to GPT (Decoder Only)

4.17: GPT Architecture

4.18: GPT Masked Multi Head Attention

4.19: GPT Blocks

4.20: GPT Training

4.21: LLM Basics: Tokens

4.22: LLM Basics: Context Window

4.23: LLM Basics: Prompt

4.24: LLM Basics: Prompt Engineering

4.25: LLM Basics: Prompt Tuning

4.26: LLM Basics: Prompt Structures

4.27: RAGs: Introduction to RAG

4.28: RAGs: What and Why

4.29: RAGs: Use Cases

4.30: RAGs: Paper Explanation

4.31: RAGs: Architecture Explanation

4.32: RAGs: Detailed Architecture Walkthrough

4.33: RAGs: Practical Use Cases

4.34: LangChain

4.35: Introduction to Prompt Engineering

4.36: Types of Prompting

4.37: Few Shot Limitations

4.38: Chain of Thoughts Prompting

4.39: Vector Databases

4.40: Vector Database vs Vector Index

4.41: How Vector Databases works

4.42: Vector Database (Practicals)

4.43: LSH

4.44: Model Overview: Ollama

4.45: Model Testing: Ollama

4.46: Python Implementation: Ollama

4.47: RAG Systems: Ollama

4.48: RAG Systems (Practicals): Ollama

4.49: Model Overview: LLM APIs

4.50: RAG Systems with xAI: LLM APIs

4.51: RAG Systems with xAI (Practicals): LLM APIs

1.1: Introduction to Fine Tuning

1.2: RAGs vs Fine-Tuning

1.5: PEFT and Quantization

1.6: LoRA (Phase I - Low Rank)

1.7: LoRA (Phase II - Adapters)

1.8: LoRA (Phase III - Low Rank + Adapters)

1.9: qLoRA (Quanitzed Low Rank Adapters)

2.1: Deployment Basics

2.2: Introduction to Flask

2.3: Generative AI Projects

2.3: Flask Basic App

2.4: Model Building (Breast Cancer Prediction)

2.5: Flask App (Breast Cancer Prediction)

2.6: AWS

2.7: AWS Deployment (Breast Cancer Prediction)

3.1: ChatScholar (EdTech Project)

3.2: Research RAG Chatbot

3.3: Automated AI Claims Processing using Gen AI

3.4: Multi PDF RAG Chatbot built on Web Scraped Data

3.5: AI Career Coach: Part 1

3.6: AI Career Coach: Part 2

3.7: AI Career Coach: Part 3

3.8: Sustainability Chatbot (GROK AI)