Artificial Intelligence Course in Canada

SQL Basics – 

• Fundamentals of Structured Query Language
• SQL Tables, Joins, Variables 

Advanced SQL –  

• SQL Functions, Subqueries, Rules, Views
• Nested Queries, string functions, pattern matching
• Mathematical functions, Date-time functions, etc. 

Deep Dive into User Defined Functions

• Types of UDFs, Inline table value, multi-statement table. 
• Stored procedures, rank function, triggers, etc. 

SQL Optimization and Performance

• Record grouping, searching, sorting, etc. 
• Clustered indexes, common table expressions.

Hands-on exercise: 

Writing comparison data between past year to present year with respect to top products, ignoring the redundant/junk data, identifying the meaningful data,  and identifying the demand in the future(using complex subqueries, functions, pattern matching concepts).

Extract Transform Load

• Web Scraping, Interacting with APIs 

Data Handling with NumPy

• NumPy Arrays, CRUD Operations, etc.
• Linear Algebra – Matrix multiplication, CRUD operations, Inverse, Transpose, Rank, Determinant of a matrix, Scalars, Vectors, Matrices.

Data Manipulation Using Pandas

• Loading the data, dataframes, series, CRUD operations, splitting the data, etc.

Data Preprocessing

• Exploratory Data Analysis, Feature engineering, Feature scaling, Normalization, standardization, etc.
• Null Value Imputations, Outliers Analysis and Handling, VIF, Bias-variance trade-off, cross validation techniques, train-test split, etc.

Data Visualization

• Bar charts, scatter plots, count plots, line plots, pie charts, donut charts, etc. with Python matplotlib.
• Regression plots, categorical plots, area plots, etc, with Python seaborn.

Descriptive Statistics – 

• Measure of central tendency, measure of spread, five points summary, etc. 

Probability 

• Probability Distributions, bayes theorem, central limit theorem. 

Inferential Statistics –  

• Correlation, covariance, confidence intervals, hypothesis testing, F-test, Z-test, t-test, ANOVA, chi-square test, etc.

Introduction to Machine learning 

• Supervised, Unsupervised learning.
• Introduction to scikit-learn, Keras, etc.

Regression 

• Introduction classification problems, Identification of a regression problem, dependent and independent variables.
• How to train the model in a regression problem.
• How to evaluate the model for a regression problem.
• How to optimize the efficiency of the regression model.

Classification 

• Introduction to classification problems, Identification of a classification problem, dependent and independent variables.
• How to train the model in a classification problem.
• How to evaluate the model for a classification problem.
• How to optimize the efficiency of the classification model.

Clustering 

• Introduction to clustering problems, Identification of a clustering problem, dependent and independent variables.
• How to train the model in a clustering problem.
• How to evaluate the model for a clustering problem.
• How to optimize the efficiency of the clustering model.

Supervised Learning 

• Linear Regression – Creating linear regression models for linear data using statistical tests, data preprocessing, standardization, normalization, etc.
• Logistic Regression – Creating logistic regression models for classification problems – such as if a person is diabetic or not, if there will be rain or not, etc.
• Decision Tree – Creating decision tree models on classification problems in a tree like format with optimal solutions.
• Random Forest – Creating random forest models for classification problems in a supervised learning approach.
• Support Vector Machine – SVM or support vector machines for regression and classification problems.
• Gradient Descent – Gradient descent algorithm that is an iterative optimization approach to finding local minimum and maximum of a given function.
• K-Nearest Neighbors – A simple algorithm that can be used for classification problems.
• Time Series Forecasting – Making use of time series data, gathering insights and useful forecasting solutions using time series forecasting.

Unsupervised Learning 

• K-means – The k-means algorithm that can be used for clustering problems in an unsupervised learning approach.
• Dimensionality reduction – Handling multi dimensional data and standardizing the features for easier computation.
• Linear Discriminant Analysis –  LDA or linear discriminant analysis to reduce or optimize the dimensions in the multidimensional data.
• Principal Component Analysis – PCA follows the same approach in handling the multidimensional data.

Performance Metrics

• Classification reports – To evaluate the model on various metrics like recall, precision, f-support, etc.
• Confusion matrix – To evaluate the true positive/negative, false positive/negative outcomes in the model.
• r2, adjusted r2, mean squared error, etc.

Artificial Intelligence Basics 

• Introduction to keras API and tensorflow

Neural Networks

• Neural networks
• Multi-layered Neural Networks
• Artificial Neural Networks 

Deep Learning 

• Deep neural networks
• Convolutional Neural Networks 
• Recurrent Neural Networks
• GPU in deep learning
• Autoencoders, restricted boltzmann machine