Artificial Intelligence Course Videos

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Download one of the best Artificial Intelligence / Machine Learning courses on the web.

Why you should get this course?

  • No prerequisites - Learn from the basics, from scratch, aka "fundamentals"
  • Practical examples and projects at every stage
  • No Computer Science background necessary
  • Practical approach - not just learn but implement
  • 13+ end-to-end case studies based on real-world business problems

What content is covered in the course?

(A very high-level overview)

  • 150+ hours industry-focused content
  • Python
  • Maths
  • Data Analysis
  • Machine Learning
  • Deep Learning

Course Topics

  • Fundamentals of Programming
    • Python for Data Science Introduction
      • Python, Anaconda and relevant packages installations
      • Why learn Python?
      • Keywords and identifiers
      • Comments, indentation and statements
      • Variables and data types in Python
      • Standard Input and Output
      • Operators
      • Control flow: if else
      • Control flow: while loop
      • Control flow: for loop
      • Control flow: break and continue
    • Python for Data Science: Data Structures
      • Lists
      • Tuples
      • Dictionary
      • Strings
      • Sets
    • Python for Data Science: Functions
      • Types of functions
      • Function arguments
      • Recursive functions
      • Lambda functions
      • Modules
      • Packages
      • File Handling
      • Exception Handling
      • Debugging Python
    • Python for Data Science: Numpy
      • Numpy Introduction
      • Numerical operations on Numpy
    • Python for Data Science: Matplotlib
    • Python for Data Science: Pandas
    • Python for Data Science: Computational Complexity
    • SQL
  • Data Science: Exploratory Data Analysis and Data Visualization
    • Plotting for exploratory data analysis (EDA)
    • Linear Algebra
    • Probability and Statistics
    • Dimensionality reduction and Visualization
    • PCA (principal component analysis)
    • (t-SNE) T-distributed Stochastic Neighbourhood Embedding
  • Foundations of Natural Language Processing and Machine Learning
  • Machine Learning - II( Supervised Learning Models)
  • Feature Engineering, Productionization and deployment of ML Models
  • Machine Learning Real-World Case Studies
  • Data Mining(Unsupervised Learning) and Recommender Systems + Real-World Case Studies
  • Neural Networks, Computer Vision and Deep Learning
  • Deep Learning Real-World Case Studies
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