Python and Machine Learning

Languages
English
Batch Size
40-50
Duration
45 hours
Investment
$$$$$
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Course Contents

  • Data Science, Machine Learning and its applications
  • Data Visualization
  • Supervised and unsupervised algorithms
  • Analytics solutions and assessment of their effectiveness

  1. Introduction
    • Data Science
    • Data Scientist
    • Artificial Intelligence vs Machine Learning vs Deep Learning
    • Statistical vs Machine Learning Approaches
    • Big Data
    • Data Life Cycle
    • OSEMN - Obtain, Scrub, Explore, Model, and iNterpret
  2. Data
    • Data sources
    • Obtaining the data
    • Data preparation
    • Identification of data requirements
    • Data quality
    • Duplicate, missing, and incomplete data
  3. Exploratory Data Analysis
    • Introduction
    • Data Visualization
    • Data Mining
    • Data Wrangling
    • Data Cleaning
    • Data Preparation (for the model)
    • Feature Engineering - Feature Scaling and Standardization
    • Univariate Analysis + Multivariate Analysis + Correlation
    • Descriptive statistics
    • Inferences from Visualization
    • Seaborn
    • Matplotlib
  4. Supervised Learning
    • Supervised Learning Algorithms
    • Regression
    • Classification
    • Logistic
    • Decision Tree, Training and Visualization
    • Classification And Regression Trees (CART)
    • Random Forest
    • Support Vector Machine
    • K-Nearest Neighbour
    • Case study
  5. Model Improvement
    • Association and dependence
    • Differences between causation and correlation
    • Simpson’s paradox
    • Curse of Dimensionality (COD)
    • Bias-variance trade-off
    • Train-test split
    • K-fold cross-validation
    • Evaluation of models
    • Comparison of model performance
    • Computational Complexity
    • Gini Impurity or Entropy
    • Regularization Hyperparameters
  6. Unsupervised Learning
    • Different Clustering techniques
    • K-Means Clustering
    • K-Means Algorithm
    • Benefits of using K-Means Clustering
    • Problems & Use Cases
  7. Industry Readiness - Project

Basic Python programming knowledge
Software options: Anaconda Navigator: https://www.anaconda.com/products/individual OR Google Colab: https://colab.research.google.com/

Instructor Profile

instructor_image

Dr. Roy is an expert in the field of Statistics, Data Science, Quality and Cyber Security with 20+ years of experience of training and teaching professionals & students in these areas. Many of his students are working in reputed companies.

This course includes:

  • 100% Online Sessions
  • Instructor led
  • Customizable Syllabus
  • Customizable Schedule
  • Certificate of Completion
  • Training Recordings
  • Training Resources
  • Learner Assessment
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