Foundation Machine Learning Program

We will walk you step-by-step into the World of Machine Learning. With every tutorial you will develop new skills and improve your understanding of this challenging yet lucrative sub-field of Data Science.This course is packed with practical exercises which are based on real-life examples. So not only will you learn the theory, but you will also get some hands-on practice building your own models.We’ll teach you how to program with Python, how to create amazing data visualizations, and how to use Machine Learning!

Methodology– This course is fun and exciting, but at the same time we dive deep into Machine Learning. For each hands-on lecture we provide you with the code, documentation, and exercises.

  • Explanation of the theory and rational behind each algorithm
  • Deep dive with a real-life programming exercise implementation

Course structure

  • Part 0- Introduction to basic statistics
    • Understand statistical measures related functions
    • Visualize data distributions and functions
    • Use covariance and correlation related metrics
    • Apply conditional probability for finding correlated features
    • Understand complex multi-level models
  • Part 1 – Data Pre-processing and Programming with Python
    • NumPy and Pandas
    • Using pandas Data Frames and Files
    • Handling abnormal data pattern using NumPy and Pandas
    • Data visualizations techniques
  • Part 2 – Foundation of ML&AI with few real-life examples implemented
  • Part 3- Key Algorithms intuition &Deep dive along with programming
    • Regression intuition and needs
    • Regression Algorithms & deep drivefor all models along with evaluation techniques
    • Classifications intuition and needs
    • Classifications Algorithms & deep drivefor all major models along with evaluation techniques
    • Clustering intuition and needs:
    • Clustering Algorithms & deep drive K-Means, Hierarchical Clustering
  • Part 4–Overview and quizzes till part 3
  • Part 5–Introduction to Association Rule Learning
    • ARL intuition and needs
    • ARL Algorithms & deep drive
  • Part 6–Introduction to Natural Language Processing
    • NLP intuition and needs
  • Part 7 -Convolutional Neural Networks
    • CNN intuition and needs
  • Part 8 – Neural Nets and Deep Learning
    • NN/DL intuition and needs
  • Part 9 – Dimensionality management and model boosting
    •  Intuition and needs
  • Part 10–Introduction to capstone project
    • Basic introduction and ideation
    • Implementation & deep drive