- Duration: 1 To 2 Months
- Timings: Part Time, Flexible
Course details
Artificial intelligence (AI) has been one of the core research techniques in computer science since the beginning of the field, and in recent years an emergence in research and computational power has made AI development more accessible for developers.In this certification course, you will learn the foundation of Artificial Intelligence (AI) and the techniques used to make AI solve problems.
Why should one take Artificial Intelligence Certification?
This Course is intended for web developers, programmers and graduates wanting to excel in their chosen areas. It is also well suited for those who are already working and would like to take certification for further career progression.
Table of Contents
Building Your Own Prediction Models
- Classification Overview and Evaluation Techniques
- Decision Trees
- Prediction with Decision Trees and Student Performance Data
- Random Forests
- Predicting Bird Species with Random Forests
- The Problem of Text Classification
- Detecting YouTube Comment Spam with Bag of Words and Random Forests
- Word2Vec Models
- Detecting Positive/Negative Sentiment in User Reviews
- Neural Networks
- Identifying the Genre of a Song Using Audio Analysis and Neural Networks
- Revising the Spam Detector to Use Neural Networks
- Overview of Deep Learning and Convolutional Neural Networks
- Identifying Handwritten Mathematical Symbols with Convolutional Neural Networks
- Revising the Bird Species Identifier to Use Images
- Solving problems with computers
- Machine Learning: Why should you jump on the bandwagon?
- Plunging In - Machine Learning Approaches to Spam Detection
- Spam Detection with Machine Learning Continued
- Get the Lay of the Land: Types of Machine Learning Problems
- Random Variables
- Bayes Theorem
- Naive Bayes Classifier
- Naive Bayes Classifier: An example
- K-Nearest Neighbours
- K-Nearest Neighbours: A few wrinkles
- Support Vector Machines Introduced
- Support Vector Machines: Maximum Margin Hyperplane and Kernel Trick
- Artificial Neural Networks: Perceptrons Introduced
- Clustering: Introduction
- Clustering: K-Means and DBSCAN
- Association Rules Learning
- Dimensionality Reduction
- Principal Component Analysis
- Regression Introduced: Linear and Logistic Regression
- Bias Variance Trade-off
- Applying ML to Natural Language Processing
- Installing Python - Anaconda and Pip
- Natural Language Processing with NLTK
- Natural Language Processing with NLTK - See it in action
- Web Scraping with BeautifulSoup
- A Serious NLP Application: Text Auto Summarization using Python
- Autosummarize News Articles
- News Article Classification using K-Nearest Neighbors
- News Article Classification using Naive Bayes Classifier
- Scraping News Websites
- Feature Extraction with NLTK
- Classification with KNN
- Classification with Naive Bayes
- Document Distance using TF-IDF
- News Article Clustering with K-Means and TF-IDF
- Clustering with K Means
- Solve Sentiment Analysis using Machine Learning
- Sentiment Analysis - What's all the fuss about?
- ML Solutions for Sentiment Analysis - the devil is in the details
- Sentiment Lexicons (with an introduction to WordNet and SentiWordNet)
- Regular Expressions
- Regular Expressions in Python
- Put it to work: Twitter Sentiment Analysis
- Twitter Sentiment Analysis
- Using Tree Based Models for Classification
- Planting the seed - What are Decision Trees?
- Growing the Tree - Decision Tree Learning
- Branching out - Information Gain
- Decision Tree Algorithms
- Decision Trees predict Survival (Kaggle)
- Overfitting - the bane of Machine Learning
- Overfitting Continued
- Cross Validation
- Simplicity is a virtue – Regularization
- The Wisdom of Crowds - Ensemble Learning
- Ensemble Learning continued - Bagging, Boosting and Stacking
- Random Forests - Much more than trees
- Back on the Titanic - Cross Validation and Random Forests
- Solving Recommendation Problems
- What do Amazon and Netflix have in common?
- Recommendation Engines - A look inside
- What are you made of? - Content-Based Filtering
- With a little help from friends - Collaborative Filtering
- A Neighbourhood Model for Collaborative Filtering
- Top Picks for You! - Recommendations with Neighbourhood Models
- Discover the Underlying Truth - Latent Factor Collaborative Filtering
- Latent Factor Collaborative Filtering contd.
- Gray Sheep and Shillings - Challenges with Collaborative Filtering
- The Apriori Algorithm for Association Rules
- Numpy in Python
- Numpy and Scipy in Python
- Movielens and Pandas
- Data Analysis with Pandas
- Movie Recommendation with Nearest Neighbour CF
- Top Movie Picks (Nearest Neighbour CF)
- Movie Recommendations with Matrix Factorization
- Association Rules with the Apriori Algorithm
- Computer Vision - An Introduction
- Perceptron Revisited
- Deep Learning Networks Introduced
- Handwritten Digit Recognition
Job roles this course is suitable for:
Artificial Intelligence Professional , Artificial Intelligence Engineer , ARTIFICIAL INTELLIGENCE SPECIALISTAbout Vskills
Vskills is the largest certification body of India. We conducts skills testing and certification exam to improve employability. The certifications are quite popular and top companies hire Vskills certified professionals.Companies have benefitted by hiring pre-certified candidates from Vskills and also use the certifications for their in house employee appraisals. Certification helps in distinguishing individuals to demonstrate their domain knowledge or skills needed for a specific profile. So a professional certification offers tangible benefits to both the individual and the employer.
Tests are conducted in a secure and unbiased manner, and certificates are awarded based on merit of the candidates who qualify tests.
Vskills certifications are for relevant qualifications that help students/employees quantify and prove those skills that are valued by the employer and are in great demand. See all Vskills courses
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