Duration
3 days
What you'll learn
- Understand the importance, principles, and fields of AI and ML
- Learn how can clean your data and prepare it for analysis
- How visualize your data and do exploratory analysis with key statistic
- Apply Supervised and Unsupervised Learning Machine Learning algorithms to real-world problems
- Learn how can you apply Artificial Intelligence and Machine Learning in your areas of functions to do predictions
Introduction
- Artificial Intelligence (AI) Overview
- What is Machine Learning (ML)?
- AI vs ML vs Data Science
- Relationship between Deep Learning (DL) and Machine Learning
- Tools/Platforms for ML, DL and AI
- Machine Learning Project End to End Pipeline
- How we can define a business problem
Data Pre processing
- Importing the Libraries
- Importing the Dataset
- For Python learners, summary of Object-oriented programming: classes & objects
- Categorical Data
- Splitting the Dataset into the Training set and Test set
Key Statistics
- 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
Machine Learning using Scikit Learn
- Types of Machine Learning
- Key Algorithms in Machine Learning
- Practical Applications of Machine Learning
- Various frameworks/Libraries popular for ML
- Concepts and Terms
- Why Scikit Learn?
Supervised Machine Learning
- Key Classification Algorithms
- Naïve Bayes Classifier
- D-tree and Random Forest Classifier
- SVM Classifier
- Confusion Matrix
- Accuracy
- Key Regression Algorithms
- Linear, Logistic Regressions
- Gradient Descent
- Loss function
- Bias vs Variance Tradeoff
- Evaluating Models
Un-Supervised Machine Learning
- Why Un-supervised learning is important?
- Where it can be applied?
- Principal Component Analysis
- Performing Clustering of data
Programme scope, delivery arrangements and suitability are confirmed during enquiry.