Course in Brief:

Python 3.x (Any Platform) with Pycharm and Jupyter Notebook

Python Mini Course: Fundamentals with Python and IDEs, Basic tokens, Standard I/O, Control Structures, Strings, Container Types [List, Set, Tuple and Dictionary], Exception Handling, Local File Handling, Working with CSV.

Machine Learning: Working with datasets, Descriptive Statistics, Data Visualization, ML Algorithms, Model selections, Pipelines, One ML Project.

 

Prerequisites for this course:

No prior programming knowledge,

Comfortable with variables (dependent and independent), linear/quadratic equations, graphs of functions, histograms, probability and statistics.

Basic Computer H/W and S/W Knowledge,

Desire to learn.

Learners’ must have a system with any 64-bit OS (preferably Windows 10 OS)

 

Course Details

  1. Python Mini Course for productive machine learning Projects:
  2. Download, Install Python in various platforms and Setting the environment variables
    1. IDEs & Settings
    2. Basic Tokens (Data Types, Operators, Input/Output, Control Structure)
  3. Container types (String, List, Set, Tuple, Dictionary)
  4. Working with Functions / Modules / Libraries
  5. Quick OOPs
  6. Data File & Exception Handling
  7. Working with CSV
    1. Understand Data with Descriptive Statistics.
    2. Understand Data with Visualization.
  1. Step-by-step lessons you will complete:
    1. Get Around in Python, NumPy, SciPy, Matplotlib and Pandas.
      1. Practice assignment, working with lists and flow control in Python.
      2. Practice working with NumPy arrays.
      3. Practice creating simple plots in Matplotlib.
      4. Practice working with Pandas Series and Data Frames.
      5. Load Datasets from CSV.
      6. Understand Data with Descriptive Statistics.
      7. Understand Data with Visualization.
  1. Supervised Machine Learning Algorithms:
    1. Regression (Linear and Polynomial)
    2. Classification (Decision Tree, SVM, K-Nearest Neighbour, & Logistic Regression)
  1. Unsupervised Machine Learning Algorithms:
    1. Clustering (K-means, Hierarchical clustering & Density-Based Clustering)
    2. Association (Apriori, FP-Growth)
  1. Reinforcement
    1. Model Selection.
    2. Ensemble Methods.
    3. Model Finalization.
  1. Projects- Here is an overview of the 3 end-to-end projects you will complete: