Machine Learning(AI Foundation) Certification Training

Overview
Course Content
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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

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