Artificial Intelligence Training in Mississauga, CA

If
If- else
Nested if-else
For
While
Nested loops
Break
Continue
Pass

Accessing Strings
Basic Operations
String slices
Function and Methods

Accessing tuples
Operations
Working
Functions and Methods
Accessing list
Operations
Working with lists
Function and Methods

Defining a function
Calling a function
Types of functions
Function Arguments
Anonymous functions
Global and local variables
Importing module
Math module
Random module
Packages
Composition

Class and object
Attributes
Inheritance
Overloading
Overriding
Data hiding
Match function
Search function
Matching VS Searching
Modifiers
Patterns

Introduction
Architecture
CGI environment variable
GET and POST methods
Cookies
File upload
Connections
Executing queries
Transactions
Handling error

Socket
Socket Module
Methods
Client and server
Internet modules

Centers
Thread
Starting a thread
Threading module
Synchronizing threads
Multithreaded Priority Queue
Tkinter programming
Tkinter widgets

CNN History
Understanding CNNs
CNN Application

Intro to RNN Model
Long Short-Term memory (LSTM)
Recursive Neural Tensor Network Theory
Recurrent Neural Network Model

Applications of Unsupervised Learning
Restricted Boltzmann Machine
Collaborative Filtering with RBM

Introduction to Autoencoders and Applications
Autoencoders
Deep Belief Network

4. Supervised Learning

• Linear and Logistic Regression
• Decision Trees and Random Forests
• Model Evaluation Metrics (Accuracy, Precision, Recall, F1 Score)

5. Unsupervised Learning

• Clustering Techniques (K-Means, DBSCAN)
• Dimensionality Reduction (PCA, t-SNE)

6. Hands-on Project 1

• Develop a predictive model using real-world datasets (e.g., sales forecasting, spam detection).

7. Introduction to Neural Networks

• Perceptrons and Multilayer Perceptrons (MLPs)
• Backpropagation and Gradient Descent

8. Deep Learning Architectures

• Convolutional Neural Networks (CNNs)
• Recurrent Neural Networks (RNNs)
• Transformers and Attention Mechanisms

9. Deep Learning Tools

• TensorFlow Basics
• PyTorch Basics

10. Hands-on Project 2

• Implement image classification or natural language processing (NLP) using a neural network.

11. Natural Language Processing (NLP)

• Tokenization, Embeddings (Word2Vec, GloVe)
• Sequence Models for Text (LSTMs, BERT)

12. Computer Vision

• Image Augmentation
• Object Detection and Segmentation

13. Reinforcement Learning

• Fundamentals of RL
• Q-Learning and Deep Q-Networks

14. Generative AI

• Generative Adversarial Networks (GANs)
• Diffusion Models

15. Hands-on Project 3

• Build an end-to-end AI solution using advanced techniques.

16. Model Optimization

• Hyperparameter Tuning
• Transfer Learning

17. AI Deployment

• Model Serving and APIs (Flask, FastAPI)
• Scaling AI with Cloud Services (AWS, Azure, GCP)

18. Ethics and Regulation in AI

• Explainable AI (XAI)
• Privacy and Data Security in AI Systems

19. Special Topics

• Edge AI and IoT
• Quantum Computing for AI

20. Capstone Project

• Design, build, and deploy a comprehensive AI system addressing a real-world problem.

• Comprehensive understanding of AI concepts and applications.
• Practical experience through multiple hands-on projects and a capstone project.
• Readiness for AI-related roles in industry or research.

Introduction
Terminologies of statistics
Measures of Centers & Spread
Types of distributions

Overview
Data Types and Variables
Operators
if Statements and lops
Functions in R

Introduction
ML Fundamentals
Learning Technologies
Decision Tree
Random forest classifiers
Deep Learning
Time Series Analysis

What is Data Science?
Data Science, Machine Learning, AI?
The Data-Related Roles
The Data Science Ecosystem
Tools of the Trade
Who is a Data Scientist?
Data Scientists at Work
Examples of Data Science Projects
An Example of a Data Product
Applied Data Science at Google
Data Science Gotchas

Typical Data Processing Pipeline
Data Discovery Phase
Data Harvesting Phase
Data Priming Phase
Exploratory Data Analysis
Model Planning Phase
Model Building Phase
Communicating the Results
Production Roll-out
Data Logistics and Data Governance
Data Processing Workflow Engines
Apache Airflow
Data Lineage and Provenance
Apache NiFi

Descriptive Statistics
Non-uniformity of a Probability Distribution
Using NumPy for Calculating Descriptive Statistics Measures
Finding Min and Max in NumPy
Using pandas for Calculating Descriptive Statistics Measures
Correlation
Regression and Correlation
Covariance
Getting Pairwise Correlation and Covariance Measures
Finding Min and Max in pandas DataFrame

Repairing and Normalizing Data
Dealing with the Missing Data
Sample Data Set
Getting Info on Null Data
Dropping a Column
Interpolating Missing Data in pandas
Replacing the Missing Values with the Mean Value
Scaling (Normalizing) the Data
Data Preprocessing with scikit-learn
Scaling with the scale() Function
The MinMaxScaler Object

Types of Machine Learning
Terminology: Features and Observations
Representing Observations
Terminology: Labels
Terminology: Continuous and Categorical Features
Continuous Features
Categorical Features
Common Distance Metrics
The Euclidean Distance
What is a Model
Supervised vs Unsupervised Machine Learning
Supervised Machine Learning Algorithms
Unsupervised Machine Learning Algorithms
Choosing the Right Algorithm
The scikit-learn Package
scikit-learn Estimators, Models, and Predictors
Model Evaluation
The Error Rate
Confusion Matrix
The Binary Classification Confusion Matrix
Multi-class Classification Confusion Matrix Example
ROC Curve
Example of an ROC Curve
The AUC Metric
Feature Engineering
Scaling of the Features
Feature Blending (Creating Synthetic Features)
The 'One-Hot' Encoding Scheme
Example of 'One-Hot' Encoding Scheme
Bias-Variance (UnderfittingvsOverfitting) Trade-off
The Modeling Error Factors
One Way to Visualize Bias and Variance
UnderfittingvsOverfitting Visualization
Balancing Off the Bias-Variance Ratio
Regularization in scikit-learn
Regularization, Take Two
Dimensionality Reduction
PCA and isomap
The Advantages of Dimensionality Reduction
The LIBSVM format
Life-cycles of Machine Learning Development
Data Splitting into Training and Test Datasets
ML Model Tuning Visually
Data Splitting in scikit-learn
Cross-Validation Technique
Hands-on Exercise
Classification (Supervised ML) Examples
Classifying with k-Nearest Neighbors
k-Nearest Neighbors Algorithm
Hands-on Exercise
Regression Analysis
Regression vs Correlation
Regression vs Classification
Simple Linear Regression Model
Linear Regression Illustration
Least-Squares Method (LSM)
Gradient Descent Optimization
Multiple Regression Analysis
Evaluating Regression Model Accuracy
The R2 Model Score
The MSE Model Score
Logistic Regression (Logit)
Interpreting Logistic Regression Results
Decision Trees and Terminology
Properties of Decision Trees
Decision Tree Classification in the Context of Information Theory
The Simplified Decision Tree Algorithm
Using Decision Trees
Random Forests
Hands-On Exercise
Support Vector Machines (SVMs)
Naive Bayes Classifier (SL)
Naive Bayesian Probabilistic Model in a Nutshell
Bayes Formula
Classification of Documents with Naive Bayes
Unsupervised Learning Type: Clustering
Clustering Examples
k-Means Clustering (UL)
k-Means Clustering in a Nutshell
k-Means Characteristics
Global vs Local Minimum Explained
Hands-On Exercise
XGBoost
Gradient Boosting
Hands-On Exercise
A Better Algorithm or More Data?

What is Python?
Additional Documentation
Which version of Python am I running?
Python Dev Tools and REPLs
IPython
Jupyter
Jupyter Operation Modes
Jupyter Common Commands
Anaconda
Python Variables and Basic Syntax
Variable Scopes
PEP8
The Python Programs
Getting Help
Variable Types
Assigning Multiple Values to Multiple Variables
Null (None)
Strings
Finding Index of a Substring
String Splitting
Triple-Delimited String Literals
Raw String Literals
String Formatting and Interpolation
Boolean and Boolean Operators
Numbers
Looking Up the Runtime Type of a Variable
Divisions
Assignment-with-Operation
Comments:
Relational Operators
The if-elif-else Triad
An if-elif-else Example
Conditional Expressions (a.k.a. Ternary Operator)
The While-Break-Continue Triad
The for Loop
try-except-finally
Lists
Main List Methods
Dictionaries
Working with Dictionaries
Sets
Common Set Operations
Set Operations Examples
Finding Unique Elements in a List
Enumerate
Tuples
Unpacking Tuples
Functions
Dealing with Arbitrary Number of Parameters
Keyword Function Parameters
The range Object
Random Numbers
Python Modules
Importing Modules
Installing Modules
Listing Methods in a Module
Creating Your Own Modules
Creating a Runnable Application
List Comprehension
Zipping Lists
Working with Files
Reading and Writing Files
Reading Command-Line Parameters
Accessing Environment Variables
What is Functional Programming (FP)?
Terminology: Higher-Order Functions
Lambda Functions in Python
Example: Lambdas in the Sorted Function
Other Examples of Using Lambdas
Regular Expressions
Using Regular Expressions Examples
Python Data Science-Centric Libraries