Advanced Machine Learning and Artificial Intelligence

Artificial Intelligence (AI) is a field that has a long history but is still constantly 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. 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.

 

Course Duration

440 Hours

FEE AND OTHER DETAILS

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Description

The course duration is 440 hours at 20 hours per week.

Foundation Classes

  • Python for Data Analysis: Data Structures, Object Oriented Programming, Data Manipulation and Data Visualization in Python
  • Introduction to SQL:SQL for querying information from databases
  • Math for Data Analysis: Linear Algebra, Matrices, Eigen Vectors and their application for Data Analysis

Statistics Essentials

  • Inferential Statistics:Probability Distribution Functions, Random Variables, Sampling Methods, Central Limit Theorem and more to draw inferences
  • Hypothesis Testing:Formulating and testing hypotheses to solve business problems
  • Exploratory Data Analysis:How to summarize data sets and derive initial insights

 

Machine Learning

  • Linear Regression:Implementing linear regression and predicting continuous data values
  • Supervised Learning:Understanding and implementing algorithms like Naive Bayes and Logistic Regression
  • Unsupervised Learning:Creating segments based on similarities using K-Means and Hierarchical clustering
  • Support Vector Machines:Classifying data points using support vectors
  • Decision Trees:Tree-based model that is simple and easy to use. Fundamentals on how to implement them

Natural Language Processing

  • Basics of text processing:Getting started with the Natural language toolkit ; the basics of text processing in python
  • Lexical processing: How to extract features from unstructured text and build machine learning models on text data
  • Syntax and Semantics:Conducting sentiment analysis, learning to parse English sentences and extracting meaning from them
  • Other problems in text analytics:Exploring the applications of text analytics in new areas and various business domains

Neural Networks & Deep Learning

  • Information flow in a neural network:Understanding the components and structure of artificial neural networks
  • Training a neural network:Learning the cutting-edge techniques used to train highly complex neural networks
  • Convolutional Neural Networks:Using CNN’s to solve complex image classification problems
  • Recurrent Neural Networks:Studying LSTMs and RNN’s applications in text analytic
  • Creating and deploying networks using Tensorflow and keras:Building and deploying deep neural networks on a website, learning to use the Tensorflow API and Keras

Graphical Models

  • Directed and Undirected Models:Basics of directed and undirected graphs
  • Inference:How graphical models are used to draw inferences using datasets
  • Learning:How to estimate parameters and structure of graphical models

Reinforcement Learning

  • Introduction to RL:Understanding the basics of RL and its applications in AI
  • Markov Decision Processes:Model processes as Markov chains, learn algorithms for solving optimization problems
  • Q-learning:Writing Q-learning algorithms to solve complex RL problems

Additional information

Course Duration

440 Hours

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