Getting Started with Artificial Intelligence & Machine Learning
Course Overview
- Learn Machine Learning principles with in-depth practical exposure to how projects are implemented at organizations in this Machine Learning course. You learn all about real-world applications of ML & the essentials of statistics and ML models with expert guidance from experienced industry mentors.
- Our ML training includes Cloudlabs integration so you gain hands-on experience working with KNIME tool. Create supervised learning models using regression, random forest classification and Develop unsupervised learning models using k-means clustering and association rule learning.
- We have chosen an approach for this course, so learners not only get an overview of the Machine Learning concepts but can easily gain first hand AI & ML experience without the need of knowing Programming or Advance Mathematics, through the Popular and User-friendly KNIME tool, suitable even for Non-Technical Professionals.
- Equip your team with the skills needed in Getting Started with building Machine Learning Applications for your Organization.
At the end of the training, participants will be able to:
Pre-requisite
Basic knowledge of Statistics is good to have.
Duration
2 days
Course Outline
- Introduction to Artificial Intelligence
- Applications, Industries, and growth
- Data science and Machine Learning
- Techniques used for AI
- AI for Everything
- Different methods used for AI
- Tradition Methods & New Methods
- AI Agents
- Challenges in operating AI systems
- Building your AI strategy and roadmap
- Types of Learning
- Phases in a ML Project (CRSIP-DM Methodology)
- The three basic problems in ML (Regression, Classification, Clustering)
- Bias Variance Trade off
- Evaluating a ML Model
- Demo of a Simple Linear Regression solution
- Revisiting the ML concepts using this demo
- Installation of KNIME
- Creating KNIME Workspace
- Ingesting data into KNIME environment
- Basic data manipulations
- Querying and Displaying data using KNIME
- Visualization in KNIME
- Understanding of Regression Problem Scenario
- Math behind Linear Regression (Ordinary Least Squares)
- Gradient Descent Algorithm
- Types of Regression (Simple Linear, Multiple, Polynomial)
- Evaluating a Regression Model (MSE, R Squared, Adj R Square)
- Predicting using a Regression Model
- Demo of Linear Regression using MS Excel
- Revisiting concepts of Regression Math using this demo
- Demo of Regression using KNIME (Boston Housing Prices Prediction)
- Understanding and communicating the Regression Model to others
- Real life applications of Regression
- Understanding of Classification Problem Scenario
- Introduction to Logistic Regression
- Sigmoid function
- Evaluating a Logistic Regression Model (Accuracy, Sensitivity)
- Predicting using a Logistic Regression Model
- KNIME Demo of Logistic Regression Model (Iris dataset)
- Understanding Decision Trees
- KNIME Demo of Decision Trees (Iris dataset)
- Comparing models of Logistic Regression and Decision Trees
- Understanding Random Forest
- Real life applications of Classification
- Significance of Unsupervised learning in ML
- Concept of distance measure (Euclidean distance, Manhattan distance)
- Introduction to Hierarchical Clustering
- Introduction to K Means Clustering
- Evaluating a Clustering output
- Demo of Hierarchical Clustering using KNIME
- Demo of K Means Clustering using KNIME
- Comparison of clusters from Hierarchical and K Means Demos
- Real life applications of Clustering
- Issues in acquisition of data
- Issues in data quality
- Identifying and handling outliers
- Importance missing data and techniques to handle
- Understanding Normalization and standardization
- Sensitivity of ML methods to distance and need for rescaling data
- Handling Categorical Data (e.g. One Hot Encoding)
- Feature Engineering
- Curse of dimensionality, Dimensionality Reduction
- Dimensionality Reduction, Data Compression
- Concept and Mathematical modelling
- Use Cases
- Programming using Python
- IRIS Data Analysis using PCA
- Introduction to Reinforcement Learning
- Exact Methods
- Approximate Methods
- Real life applications of Reinforcement Learning
- What is Deep Learning?
- Why Deep Learning?
- Feature Extraction
- Working of a Deep Network
- Types of Deep Networks
- Real life applications of Deep Learning
- Artificial Neural Networks (ANN)
- Convolutional Neural Networks (CNN)
- Recurrent Neural Networks (RNN)
- Introduction to Natural Language Processing
- NLP Example using Keras library
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