Course Module

  • Introduction
  • Execution steps
  • Memory management and Garbage collections
  • Data Types and Operations
  • Statements and Syntax
  • File Operations
  • Functions
  • Modules and Packages
  • Classes
  • Exception Handling
  • Advanced Concepts
  • Django
    Objectives of this course
  • Learning Advance python packages Numpy and Pandas
  • This course provides hands on training in Machine Learning applications and algorithms
  • Preprocessing of Machine learning
  • Numpy
  • Creating Arrays-Numpy Data types-Array Mathematics-Array Manipulation-Subsetting, slicing, Indexing-boolean Indexing-Array Manipulation-Reshaping,Transposing,spliting and combining arrays-Inspecting Array and aggregating array.
  • Pandas
  • Series-Dataframe-Importing pandas- Dictionary into data frames, read and write to CSV and Excel, selecting, Boolean Indexing, setting, dropping, soring, filling missing values, getting dataframe information and retrieving series, summary and applying lambda function
  • Data Wrangling
  • Combining and Merging Data Sets-Reshaping and Pivoting-Data Transformation-Regular Expression
  • Plotting and Visualization
  • Figures, Subplots, Line plot, bar plot, histograms. Colors, ticks, labels and legends
  • Introduction to Machine Learning
  • Introduction to Machine Learning – types of learning – supervised – unsupervised- classification problem based on the K-nearest neighbors method, Feature Selection - Feature Extraction - Principal Component Analysis - Colloborative Filtering - Unsupervised Learning (Clustering) - Kmeans Clustering - Hierarchical Clustering
    Descriptive Statistics
  • Introduction to the course
  • Descriptive Statistics
  • Probability Distributions
  • Inferential Statistics
  • Inferential Statistics through hypothesis tests
  • Permutation & Randomization Test
  • Regression & ANOVA
  • Regression
  • ANOVA(Analysis of Variance)
  • Machine Learning: Introduction and Concepts
  • Differentiating algorithmic and model based frameworks
  • Regression : Ordinary Least Squares, Ridge Regression, Lasso Regression, K Nearest Neighbours Regression & Classification
  • Supervised Learning with Regression and Classification techniques -1
  • Bias-Variance Dichotomy
  • Model Validation Approaches
  • Logistic Regression
  • Linear Discriminant Analysis
  • Quadratic Discriminant Analysis
  • Regression and Classification Trees
  • Support Vector Machines
  • Supervised Learning with Regression and Classification techniques -2
  • Ensemble Methods: Random Forest
  • Neural Networks
  • Deep learning
  • Unsupervised Learning and Challenges for Big Data Analytics
  • Clustering
  • Associative Rule Mining
  • Challenges for big data anlalytics
  • Prescriptive analytics
  • Creating data for analytics through designed experiments
  • Creating data for analytics through Active learning
  • Creating data for analytics through Reinforcement learning
Course Duration : 54 Hrs | 12 Weeks
Course Price : INR 32,400 16,200
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