Machine Learning(AI Foundation) Certification Training

  • Overview
  • Course Content
  • Drop us a Query

Machine Learning mainly focuses on the enhancement and development of the computer programs, which has the property to get changed when it comes in the interaction to the new data. However, this is a kind of artificial intelligence, the Introduction to Machine Learning course enlightens the candidates with the algorithms that proves to be helpful for the IP professionals in analyzing the data set with ease. In the training modules algorithms such as: regression, clustering, classification,  and recommendation have been introduced, all these helps the candidates in supervising the advanced data programing techniques.

After completing the introduction to machine learning certification the candidates would be able to:

  • Determine the various applications of machine learning algorithms
  • Develop an understanding classification data and models
  • Learn the how to implement the unsupervised learning algorithms, which  includes deep learning, clustering, and recommendation systems
  • How to perform the supervised learning techniques, such as: linear and logistic regression?
Target audience
  • Analytics professionals
  • Data Science professionals
  • Software professionals
  • Graduates looking to build a career in Data Science and machine learning

All who are willing to work in machine learning or artificial intelligence and switch in the field of analytics along with the professionals, who deal with eCommerce, search, and other online consumer based organizations.

Prerequisites

The candidates willing to join the introduction to machine learning training should have a prior acquaintance on fundamentals of of programming & matrix algebra.

Part 1: Set up

  • Generic Programming Structure, Jupter Notebooks, Tools, Python

Part 2: Regression

  • Simple Linear Regression
  • Multiple Linear Regression
  • Polynomial Regression
  • Gradient Descent
  • Stochiastic Gradient Descent
  • Support Vector Regression (SVR)
  • Decision Tree Regression
  • Random Forest Regression
  • Evaluating Regression Models Performance
  • Hands-on Assignments

Part 3: 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
  • Hands-on Assignments

Part 4: Clustering

  • K-Means Clustering
  • Hierarchical Clustering
  • Hands-on Assignments

Part 5: Association Rule Learning

  • Apriori
  • Eclat
  • Hands-on Assignments

Part 6: Reinforcement Learning

  • Upper Confidence Bound (UCB)
  • Thompson Sampling

Part 7: Natural Language Processing(Introduction)

Part 8: Deep Learning(Introduction)

  • Artificial Neural Networks
  • Convolutional Neural Networks

Part 9: Dimensionality Reduction

  • Principal Component Analysis (PCA)
  • Linear Discriminant Analysis (LDA)
  • Kernel PCA

Part 10: Model Selection & Boosting

  • Model Selection
  • Interview Prep : Grooming Session
  • Bonus Lecture : Review

A Few Things You'll Love!

What our Students Speak

+