About Artificial Intelligence Training
Artificial Intelligence (AI) has a long history but is still properly and actively growing and changing. In this course, you’ll learn the basics of modern AI as well as some of the representative applications of AI such as Data Science, Machine Learning, Deep Learning, Statistics, Artificial Neural Networks, Restricted Boltzmann Machine (RBM) and Tensorflow with Python. Along the way, we also hope to excite you about the numerous applications and huge possibilities in the field of AI, which continues to expand human capability beyond our imagination. This Artificial Intelligence course will provide a broad understanding of the basic techniques for building intelligent computer systems and an understanding of how AI is going to apply.
Introduction to Data Science Deep Learning & Artificial Intelligence
- Introduction to Deep Learning & AI
- Deep Learning: A revolution in Artificial Intelligence
- Limitations of Machine Learning
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Java
What is Deep Learning?
Types of Analytics
Functions Function Parameters Global Variables Variable Scope and Returning Values. Sorting Alternate Keys Lambda Functions Sorting Collections of Collections Classes & OOPs Statistics What is Statistics Descriptive Statistics Central Tendency Measures The Story of Average Dispersion Measures Data Distributions Central Limit Theorem What is Sampling Why Sampling Sampling Methods Inferential Statistics What is Hypothesis testing Confidence Level Degrees of freedom what is pValue Chi-Square test What is ANOVA Correlation vs Regression Uses of Correlation & Regression Machine Learning, Deep Learning & AI using Python Introduction ML Fundamentals ML Common Use Cases Understanding Supervised and Unsupervised Learning Techniques Clustering Similarity Metrics Distance Measure Types: Euclidean, Cosine Measures Creating predictive models Understanding K-Means Clustering Understanding TF-IDF, Cosine Similarity and their application to Vector Space Model Case study Implementing Association rule mining What is Association Rules & its use cases? What is Recommendation Engine & it’s working? Recommendation Use-case Case study Understanding Process flow of Supervised Learning Techniques
What is Random Forests Features of Random Forest Out of Box Error Estimate and Variable Importance Case study Naive Bayes Classifier. Case study Project Discussion Problem Statement and Analysis Various approaches to solve a Data Science Problem Pros and Cons of different approaches and algorithms. Linear Regression Case study Introduction to Predictive Modeling Linear Regression Overview Simple Linear Regression Multiple Linear Regression Logistic Regression Case study Logistic Regression Overview Data Partitioning Univariate Analysis Bivariate Analysis Multicollinearity Analysis Model Building Model Validation Model Performance Assessment AUC & ROC curves Scorecard Support Vector Machines Case Study Introduction to SVMs SVM History Vectors Overview Decision Surfaces Linear SVMs The Kernel Trick Non-Linear SVMs The Kernel SVM Time Series Analysis
