This 18 day (Weekdays – 3hrs.) OR 6-week (Sunday/Saturday) instructor led Machine Learning course is designed to provide professionals with extensive knowledge to accomplish their day to day job for Data science.
The scientific discipline of Machine Learning (ML) focuses on developing algorithms to find patterns or make predictions from empirical data. It is a classical sub-discipline within Artificial Intelligence (AI). The discipline is increasingly used by many professions and industries to optimize processes and implement adaptive systems. The course places machine learning in its context within AI and gives an introduction to the most important core techniques. We will cover the standard and most popular supervised learning algorithms, clustering algorithms, an introduction to Bayesian learning and the naive Bayes algorithm, support vector machines and kernels and neural networks with an introduction to Deep Learning.
This course has been designed in a way that helps you learn complex theory, algorithms and coding libraries in a simple way. We will walk you step-by-step into the World of Machine Learning. With every tutorial, you will develop new skills and improve your understanding of this challenging yet lucrative sub-field of Data Science. This course is fun and exciting, but at the same time, we dive deep into Machine Learning.
- To understand why Machine Learning is useful.
- To understand data preprocessing and its necessity.
- To learn how to design, use, and program for Regression algorithms.
- To learn how to design, use, and program for Classification algorithms.
- To learn how to design, use, and program for Clustering algorithms.
- To understand Association Rule Learning for large dataset
- To learn how to design, use, and program for Reinforcement learning.
- To understand what is Natural Language Processing.
- To understand Deep Learning and its most used models.
- To understand Dimensionality Reduction.
- To understand Model Selection & Boosting.
- Applications of Machine Learning
- Why Machine Learning is the Future
- Matrices and Vectors
- Addition and Scalar Multiplication
- Matrix-Vector Multiplication
- Matrix-Matrix Multiplication
- Matrix Multiplication Properties
- Inverse, and Transpose
- Get the dataset
- Importing the Libraries
- Importing the Dataset
- Missing Data
- Categorical Data
- Splitting the Dataset into the Training set and Test set
- Feature Scaling
- Data Preprocessing Template
- Simple Linear Regression
- Multiple Linear Regression
- Polynomial Regression
- Support Vector Regression (SVR)
- Decision Tree Regression
- Random Forest Regression
- Evaluating Regression Models Performance
- 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
- K-Means Clustering
- Hierarchical Clustering
- Upper Confidence Bound (UCB)
- Thompson Sampling
- Artificial Neural Networks
- Convolutional Neural Networks
- Principal Component Analysis (PCA)
- Linear Discriminant Analysis (LDA)
- Kernel PCA
- Model Selection
- Instructor led online training is an ideal vehicle for delivering training to individuals anywhere in the world at any time.
- This innovative approach presents live content with instructor delivering the training online.
- Candidates will be performing labs remotely on our labs on cloud in presence of an online instructor.
- Rstforum uses microsoft lync engine to deliver instructor led online training.
- Advances in computer network technology, improvements in bandwidth, interactions, chat and conferencing, and realtime audio and video offers unparalleled training opportunities.
- Instructor led online training can helps today’s busy professionals to perform their jobs and upgrade knowledge by integrating self-paced instructor led online training in their daily routines.
Labs on cloud