Machine Learning

  • Master important concepts of the Machine Learning with Python programming language. Helps you learn important libraries of Python.
  • Important concepts of Machine Learning like supervised learning, unsupervised learning, linear regression, classification & gradient descent.

What this course covers

  • Includes basic and advanced concepts of Python programming language
  • Important concepts of Python like data types, collection, operators, loops, decorators, generators and lambda
  • Important Python libraries for Machine Learning, Data Science and Data Analytics
  • Import libraries of Python like SciPy, NumPy, Pandas, MatPlotLib, SciKit
  • Basic & advanced concepts of data processing and covers concepts like Regression, Classification, Gradient Descent in detail
  • Important concepts of linear regression, unsupervised learning & cluster analysis

Requirements for this course

  • Machine with Windows, Linux or Mac OS X Operating System
  • Basic Knowledge of Programming is an advantage

Who can benefit from this course

  • Software Developers and Testing professionals working towards automation
  • Data Scientists, Data Analysts, Network & Automation Engineers
  • Whoever wants to work in the field of Data Science, Machine Learning, Artificial Intelligence

Course Syllabus

  • Python Libraries – NumPy
    • NumPy Introduction
    • NumPy create arrays
    • NumPy array indexing
    • NumPy array slicing
    • NumPy copy
    • NumPy shape, reshape
    • NumPy join, split
    • NumPy search
    • NumPy sort
  • Data libraries in Python – Pandas
    • Data analysis in Python
    • Pandas library
    • Pandas Series
    • Pandas DataFrames
    • Pandas Read Json, CSV
    • Pandas operations
    • Python for Statistics
  • Python Library – SciPy
    • Installation of SciPy library
    • NumPy vs. SciPy
    • SciPy Matrices Operations
    • SciPy special & integration functions
    • SciPy Interpolation functions
    • SciPy Transformation functions
    • SciPy Algebraic functions
    • SciPy Eigen Values & vectors
  • Python Libraries – Matplotlib
    • Matplotlib Introduction
    • Matplotlib pyplot
    • Matplotlib plotting
    • Matplotlib markers
    • Matplotlib line
    • Matplotlib labels
    • Matplotlib grid
    • Matplotlib scatter
    • Matplotlib bars
    • Matplotlib histograms
    • Matplotlib pie charts
  • Python Library – SciKit
    • Basics of SciKit
    • SciKit Data Loading
    • Training/Testing Data Generation
    • Preprocessing
    • Generate Model
    • Evaluate Models
  • Introduction to Machine Learning
    • What is Machine Learning
    • Data in Machine Learning
    • Demystifying Machine Learning
    • Machine Learning Applications
    • Python libraries for Machine Learning
  • Data Processing
    • Understanding Data Processing
    • Data Exploration
    • Data Preprocessing in Python
    • Data Cleansing
    • Handling Imbalanced Data
    • Generate test datasets for Machine learning
    • Feature Scaling
    • Feature selection techniques
  • Supervised Learning – Regression Techniques
    • Introduction to Regression
    • Linear regression
    • Logistic regression
    • Polynomial regression
    • Stepwise regression
    • Ridge regression
    • Lasso regression
    • Elastic Net regression
    • Ensemble Methods – Bagging, Boosting & Aggregating
    • Gradient Boosted Trees
    • Model Evaluation and performance
  • Supervised Learning – Classification
    • Introduction to Classification
    • Basic Concept of Classification
    • Classification Vs. Regression
    • K-Nearest Neighbor
    • Guassian Naive Bayes
    • Decision Trees
    • Support Vector Machine (SVM)
    • Random Forest
  • Gradient Descent
    • Introduction to Gradient Descent
    • Gradient Descent Algorithms
    • Stochastic Gradient Descent (SGD)
    • Mini-Batch Gradient Descent
    • Optimization for Gradient Descent
    • Momentum-based Gradient Optimizer
  • Linear Regression
    • Introduction to Linear Regression
    • Gradient Descent in Linear Regression
    • Normal Equation in Linear Regression
    • Linear Regression (Python Implementation)
    • Univariate Linear Regression
    • Multiple Linear Regression
    • Weighted Linear Regression
  • Unsupervised Learning
    • Introduction to Unsupervised Learning
    • Similarity Measures
    • Cluster Analysis and Similarity Measures
    • KA-chemical Clustering
    • Principal means Clustering
    • Hierarchy Components Analysis
    • Association Rules Mining & Market Basket Analysis
Machine Learning Course Introduction

INR 20,000 INR 16,000 GST Inclusive

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Machine Learning

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