Duration
2 days
What you'll learn
- Make powerful analysis with applying different type of analysis
- Exploratory data analysis
- Have a great intuition of many Machine Learning models
- Make accurate predictions
- Make robust Machine Learning models
- Create strong added value to your business
- Handle advanced techniques like Dimensionality Reduction
- Know which Machine Learning model to choose for each type of problem
Who this course is for
- Anyone interested in Machine Learning.
- Any people who are not that comfortable with coding but who are interested in Machine Learning and want to apply it easily on datasets.
- Any students in college who want to start a career in Data Science.
- Any data analysts who want to level up in Machine Learning.
- Any people who are not satisfied with their job and who want to become a Data Scientist.
- Any people who want to create added value to their business by using powerful Machine Learning tools.
Introduction
- Artificial Intelligence (AI) Overview
- What is Machine Learning (ML)?
- AI vs ML vs Data Science
- Tools/Platforms for ML, DL and AI
- Machine Learning Project End to End Pipeline
- How we can define a business problem
Key Statistics and Exploratory Analysis
- Summary Statistics
- Exploratory Data Analysis
- Numerical Computation using Python
Data Visualization
- Using MatPlot Lib
- Working with Seaborn
Data Cleaning and Feature Engineering
- What is Feature Engineering?
- Why Feature Engineering?
- How to apply Feature Engineering?
- Discussions on various scenarios
- Data Cleaning & Wrangling using Pandas
- Missing Values and Outlier
Regression
- Simple Linear Regression
- Multiple Linear Regression
- Polynomial Regression
- Support Vector Regression (SVR)
- Decision Tree Regression
- Ensemble methods
- Evaluating Regression Models Performance
Classification
- Logistic Regression
- K-Nearest Neighbors (K-NN)
- Support Vector Machine (SVM)
- Kernel SVM
- Naive Bayes
- Decision Tree Classification
- Random Forest Classification
- Evaluating Classification Models Performance
Clustering
- K-Means Clustering
- Hierarchical Clustering
Dimensionality Reduction
- Principal Component Analysis (PCA)
- Linear Discriminant Analysis (LDA)
- Kernel PCA
Programme scope, delivery arrangements and suitability are confirmed during enquiry.