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Artificial Intelligence

Complete guide to artificial intelligence, preparation for deep reinforcement learning with stock trading applications.

What you'll learn
  • Understand reinforcement learning on a technical level
  • Implement various types of reinforcement learning algorithms
  • Understand the relationship between reinforcement learning and psychology
  • Apply gradient-based supervised machine learning methods to reinforcement learning
Artificial Intelligence

Artificial Intelligence

This Artificial Intelligence Program, gives training on the skills required for a successful career in AI. Throughout this exclusive training program, you'll master Deep Learning, Machine Learning, and the programming languages.

Course Content

Section A: Introduction to Artificial Intelligence
  • Introduction to Artificial Intelligence (AI)
  • History of AI
  • Importance and other Philosophies about AI
  • General Approaches and Goals of AI
  • Components of AI
  • Working Domains/Companies/Products in Current Market
  • Programming Languages Used for AI
  • Python Programming
Section B: Python Programming
  • Basic of python and why python for Artificial Intelligence
  • Installation of software on different OS.
  • Understanding basic syntax with data types
  • Number, String, List, Tuple, Dictionary
  • Extracting data from a file
  • Committing your code to GIT
Section C: More about Python Programming
  • Conditional statement and loops
  • Function and modules
  • File handling
  • Creating own modules / library
  • Web scraping with urllib2
  • Grabbing system information from Popen and os library
  • Scanning Network IP & MAC address with loops
Section D: Libraries Used
  • Introduction to Numpy & Matplotlib
  • Managing arrary with numpy
  • Multidimensional array with numpy
  • Unit matrix handling & creating
  • Deleting indexes from matrix
  • Deep dive with Matplotlib
  • Drawing general purpose graphs
  • Graphs with mathematic
Section E: Linear Regression
  • Using House Price Prediction
  • Simple Linear Regression
  • Polynomial Linear Regression
  • Cost Function of Linear Regression
  • Understanding linear regression using mat
Section F: Neural Networks analysis
  • Introduction to Neural Network
  • Understanding neural networks
  • Data learning and machine predictions
  • Neural networks real understanding
  • Neural network implementation with real datasets
  • Natural Language Processing
  • Tokenizing text data
  • Converting words to their base forms using stemming
  • Converting words to their base forms using lemmatization
  • Dividing text data into chunks
  • Extracting the frequency of terms using a Bag of Words model
  • Building a category predictor
Section G: Image Processing & ML
  • Introduction to Image Processing
  • How image search is going to work
  • Taking pictures with python for image processing
  • Loading and registering images
  • Face/Object detection
Section H: Projects
  • Face Detection / Recognition and Automation
  • Intelligent Blind Stick
  • Ecommerce Product Recommendation

Coming Soon...


  1. 1. Basic Knowledge of Python.
  2. 2. Should have laptop.

Course Outcomes!!

  1. 1. Writing tip to showcase skills you have learnt in the course.
  2. 2. Mock interview practice and frequently asked interview questions.
  3. 3. Career guidance regarding hiring companies and open positions.
  4. 4. Certificates
  5. 5. Opportunity of Research Internship