Machine Learning Course in Canada

• Python Basic Building
• Python Keywords and identifiers
• Comments, indentation, statements
• Variables and data types in Python
• Standard Input and Output

• Operators
• Control flow: if else elif
• Control flow: while loop
• Control flow: for loop
• Control flow: break & continue
• Python Data Structures

• Strings
• Lists, Lists comprehension
• Tuples, Sets
• Dictionary, Dictionary Comprehension

• Python Functions
• Python Builtin Functions.
• Python Userdefined Functions.
• Python Recursion Functions.
• Python Lambda Functions.
• Python Exception Handling, 
• Logging And Debugging

• Exception Handling 
• Custom Exception Handling
• Logging With Python
• Debugging With Python
• Python OOPS
• Python Objects And Classes
• Python Constructors
• Python Inheritance
• Abstraction In Python
• Polymorphism in Python
• Encapsulation in Python

• File Handling
• Create 
• Read
• Write
• Append

• Introduction to NumPy
• NumPy Array
• Creating NumPy Array
• Array Attributes, 
• Array Methods
• Array Indexing, 
• Slicing Arrays
• Array Operation
• Iteration through Arrays

• Introduction to Pandas
• Pandas Series
• Creating Pandas Series
• Accessing Series Elements
• Filtering a Series
• Arithmetic Operations
• Series Ranking and Sorting
• Checking Null Values
• Concatenate a Series

• Data Frame Manipulation
• Pandas Dataframe 
• Introduction Dataframe Creation
• Reading Data from Various Files
• Understanding Data
• Accessing Data Frame Elements using Indexing
• Dataframe Sorting
• Ranking in Dataframe
• Dataframe Concatenation
• Dataframe Joins
• Dataframe Merge
• Reshaping Dataframe
• Pivot Tables, 
• Cross Tables
• Dataframe Operations

• Checking Duplicates
• Dropping Rows and Columns
• Replacing Values
• Grouping Dataframe
• Missing Value Analysis & Treatment
• Visualization using Matplotlib
• Plot Styles & Settings
• Line Plot, 
• Multiline Plot
• Matplotlib Subplots
• Histogram, Boxplot
• Pie Chart ,Scatter Plot
• Visualization using Seaborn
• Strip Plot ,Distribution Plot
• Joint Plot, 
• Violin Plot, 
• Swarm Plot
• Pair Plot,
• Count Plot
• Heatmap
• Visualization using Plotly
• Boxplot
• Bubble Chart
• Violin Plot
• 3D Visualization

• EDA and Feature Engineering
• Introduction of EDA
• Dataframe Analysis using Groupby
• Advanced Data Explorations

• Working with SQL Using MySQL 
• Work Bench / SQL Server"
• USE, DESCRIBE, 
• SHOW TABLES
• SELECT, INSERT
• UPDATE & DELETE
• CREATE TABLE
• ALTER: ADD, MODIFY, DROP
• DROP TABLE, TRUNCATE, DELETE
• LIMIT, OFFSET
• ORDER BY
• DISTINCT
• WHERE Clause
• HAVING Clause
• Logical Operators
• Aggregate Functions: COUNT, MIN, MAX, AVG, SUM
• GROUP BY
• SQL Primary And Foreign Key
• Join and Natural Join
• Inner, Left, Right and Outer joins

• Advance SQL
• Subqueries/Nested Queries/Inner Queries
• SQL Function And Stored Procedures
• SQL Window Function
• CTE In SQL
• Normalization In SQL

• Basic Math
• Linear Algebra
• Probability
• Calculus
• Develop a comprehensive understanding of coordinate geometry and linear algebra.
• Build a strong foundation in calculus, including limits, derivatives, and integrals.

• Descriptive Statistics
• Sampling Techniques
• Measure of Central Tendency
• Measure of Dispersion
• Skewness and Kurtosis
• Random Variables
• Bassells Correction Method
• Percentiles and Quartiles
• Five Number Summary
• Gaussian Distribution
• Lognormal Distribution
• Binomial Distribution
• Bernoulli Distribution

• Inferential Statistics
• Standard Normal Distribution 
• ZTest
• TTest
• ChiSquare Test
• ANOVA / FTest
• Introduction to Hypothesis Testing
• Null Hypothesis
• Alternet Hypothesis

• Probability Theory
• What is Probability?
• Events and Types of Events
• Sets in Probability
• Probability Basics using Python
• Conditional Probability
• Expectation and Variance

• Introduction to Machine Learning
• Machine Learning Modelling Flow
• "Supervised and Unsupervised 
• Types of Machine Learning Algorithms

• Linear Regression using OLS
• Introduction of Linear Regression
• Types of Linear Regression
• OLS Model
• Math behind Linear Regression
• Decomposition Variability
• Metrics to Evaluate Model
• Feature Scaling
• Feature Selection
• Regularisation Techniques
• Ridge Regression 
• Lasso Regression
• ElastivNet Regression

• Optimisation Techniques
• What is Optimisation?
• Gradient Descent
• Adagrad Algorithm
• Adam Algorithm
• Linear Regression with SGD
• Prerequisites

• Introduction to Stochastic Gradient Descent (SGD)
• Preparation for SGD
• Workflow of SGD
• Implementation of SGD on Linear Regression

• Logistic Regression
• Maximum Likelihood Estimation
• "Logistic Regression Using Sigmoid 
• Activation Function"
• Performance Metrics 
• Confusion Matrix
• Precision, Recall, F1Score
• Receiver Operating Characteristic Curve

• KNN
• Euclidean Distance
• Manhattan Distance
• Implementation for KNN

• SVM
• Support Vector Regression
• Support Vector Classification
• Polynomial Kernel
• Cost Function
• GridSerchCV

• Decision Trees
• Decision Tree for Classification
• Decision Tree for Regression
• ID3 Algorithm
• CART Algorithm
• Entropy
• Gini Index
• Information Gain
• Decision Tree: Regression
• Mean Square Error
• PrePruning and PostPruning

• Naive Bayes
• Introduction to Bayes Theorem
• Explanation for naive bayes

• Ensemble Technique
• Bagging
• Random Forest Classifier
• Random Forest Regression
• Random Forest – Why & How?
• Feature Importance
• Advantages & Disadvantages

• Boosting
• Bootstrap Aggregating
• AdaBoost
• XgBoost
• Project For Random Forest
• Project Penguin Classification
• Project Texi Prediction

• Kmeans Clustering
• Prerequisites
• Cluster Analysis
• Kmeans
• Implementation of Kmeans
• Pros and Cons of Kmeans
• Application of Kmeans
• Elbow Method
• Model building for Kmeans Clustering

• Hierarchical Clustering
• Types of Hierarchical Clustering
• Dendrogram
• Pros and Cons of Hierarchical Clustering
• Model building for Hierarchical Clustering

• DBSCAN Clustering
• Introduction for DBSCAN Clustering
• implementation of DBSCAN

• Principal Components Analysis
• Prerequisites
• Introduction to PCA
• Principal Component
• Implementation of PCA
• Case study
• Applications of PCA
• Project on PCA

• Time Series Modelling
• Understand Time Series Data
• Visualising Time Series Components
• Exponential Smoothing
• ARIMA
• SARIMA
• SARIMAX
• Project on Forecasting
• Cloud Basics
• ML on Cloud

• Introduction of MLOps
• What and why MLOps
• MLOps fundamentals
• MLOps vs DevOps
• Why DevOps is not sufficient for MLOps
• Challenges in traditional ML Pipeline
• DevOps and MLOps tools and platform
• What is SDLC?
• Types of SDLC
• Waterfall vs AGILE vs DevOps vs MLOps

• MLOps Foundation
• Fundamental of Linux for MLOps and data scientist
• Important Linux Commands
• Source code managements using GIT
• GIT configuration and GIT commands
• YAML for Configuration Writing
• YAML vs JSON Schema
• Docker for Containers
• Docker Basic Command, Dockerhub, Dockerfile
• Cloud Computing and Cloud Infrastructure
• Cloud Service Provider- AWS, GCP, AZURE
• Data Managements and Versioning with DVC
• Monitoring, Alerting, Retraining With Grafana and
• prometheus
• Experiment tracking with MLFLOW
• Model Serving With BENTOML

• End to End project implementation with Deployment implementation with Deployment
• Understanding Machine learning Workflow and Project Setup
• Project Template Setup with GitHub
• Modular workflow Introduction and Implementation
• Understanding the Training Pipeline and Its Components

• Data Ingestion, Data Transformation Model Trainer Model
• Evaluation
• Creating Prediction Pipeline and End Point Creation
• Continues Integration, Continues Delivery and Continues
• Training understanding and Project Deployment

• Prompt Engineering
• Why Prompt Engineering?
• ChatGPT
• Few Standard Definitions:
• Label
• Logic
• Model Parameters (LLM Parameters)
• Basic Prompts and Prompt Formatting
• Elements of a Prompt:
• Context
• Task Specification
• Constraints
• General Tips for Designing Prompts:
• Be Specific
• Keep it Concise
• Be Contextually Aware
• Test and Iterate
• Prompt Engineering Use Cases
• Information Extraction
• Text Summarization
• Question Answering
• Code Generation
• Text Classification
• Prompt Engineering Techniques
• N-shot Prompting
• Zero-shot Prompting
• Chain-of-Thought (CoT) Prompting
• Generated Knowledge Prompting

In a Machine Learning course, training features related to project development typically include:

• Data Collection and Preprocessing Techniques: Students learn how to gather and preprocess data for training models. This involves techniques such as data cleaning, handling missing values, outlier detection, normalization, and feature scaling.
• Feature Engineering: Feature engineering is a crucial aspect of building effective machine learning models. Students learn various techniques for creating new features, transforming existing ones, handling categorical variables, and extracting meaningful information from raw data.
• Model Selection: Students are introduced to a variety of machine learning algorithms and techniques, including supervised learning (e.g., regression, classification), unsupervised learning (e.g., clustering, dimensionality reduction), and ensemble methods (e.g., random forests, gradient boosting). They learn how to choose the most appropriate model for different types of problems and datasets.
• Model Training and Evaluation: Students learn how to train machine learning models using training data and evaluate their performance using appropriate metrics. They understand the concepts of overfitting, underfitting, cross-validation, and hyperparameter tuning to ensure robust model performance.
• Project-based Learning: Hands-on projects are integral to the course, allowing students to apply their knowledge and skills to real-world problems. They work on end-to-end machine learning projects, from data exploration and preprocessing to model building, evaluation, and deployment.
• Tools and Libraries: Students gain proficiency in popular machine learning tools and libraries such as Python, scikit-learn, TensorFlow, and PyTorch. They learn how to use these tools effectively to implement machine learning algorithms, visualize data, and interpret model results.
• Ethical Considerations: The course covers ethical considerations in machine learning, including fairness, bias, privacy, and transparency. Students learn about the ethical implications of their models and how to mitigate potential biases and risks.

Overall, the training features in a Machine Learning course provide students with a comprehensive understanding of machine learning concepts and practical skills to tackle real-world problems effectively.