Machine Learning using Python Certification Training in Brampton, Canada

• What is Data Science?
• What does Data Science involve?
• Era of Data Science
• Business Intelligence vs Data Science
• Life cycle of Data Science
• Tools of Data Science
• Introduction to Python

• Data Analysis Pipeline
• What is Data Extraction
• Types of Data
• Raw and Processed Data
• Data Wrangling
• Exploratory Data Analysis
• Visualization of Data

• Python Revision (numpy, Pandas, scikit learn, matplotlib)
• What is Machine Learning?
• Machine Learning Use-Cases
• Machine Learning Process Flow
• Machine Learning Categories
• Linear regression
• Gradient descent

• What is Classification and its use cases?
• What is Decision Tree?
• Algorithm for Decision Tree Induction
• Creating a Perfect Decision Tree
• Confusion Matrix
• What is Random Forest?

• Introduction to Dimensionality
• Why Dimensionality Reduction
• PCA
• Factor Analysis
• Scaling dimensional model
• LDA

• What is Naïve Bayes?
• How Naïve Bayes works?
• Implementing Naïve Bayes Classifier
• What is Support Vector Machine?
• Illustrate how Support Vector Machine works?
• Hyperparameter optimization
• Grid Search vs Random Search
• Implementation of Support Vector Machine for Classification

• What is Clustering & its Use Cases?
• What is K-means Clustering?
• How K-means algorithm works?
• How to do optimal clustering
• What is C-means Clustering?
• What is Hierarchical Clustering?
• How Hierarchical Clustering works?

• What are Association Rules?
• Association Rule Parameters
• Calculating Association Rule Parameters
• Recommendation Engines
• How Recommendation Engines work?
• Collaborative Filtering
• Content Based Filtering

• What is Reinforcement Learning
• Why Reinforcement Learning
• Elements of Reinforcement Learning
• Exploration vs Exploitation dilemma
• Epsilon Greedy Algorithm
• Markov Decision Process (MDP)
• Q values and V values
• Q – Learning
• α values

• What is Time Series Analysis?
• Importance of TSA
• Components of TSA
• White Noise
• AR model
• MA model
• ARMA model
• ARIMA model
• Stationarity
• ACF & PACF

• What is Model Selection?
• Need of Model Selection
• Cross – Validation
• What is Boosting?
• How Boosting Algorithms work?
• Types of Boosting Algorithms
• Adaptive Boosting

Goal: In this module, you will learn how to approach and implement a Project end to end, and a Subject Matter Expert will share his experience and insights from the industry to help you kickstart your career in this domain. Finally, we will be having a Q&A and doubt clearing session.

Objectives: At the end of this module, you should be able to:

• How to approach a project
• Hands-On project implementation
• What Industry expects
• Industry insights for the Machine Learning domain
• QA and Doubt Clearing Session