Machine Learning & Deep Learning

by Pythonsoft Claim Listing

Machine Learning & Deep Learning course is offered by Pythonsoft. Pythonsoft is a leading software training institute in India to provide quality training to students. As a training institute, we try to provide strong programming skills to our students.

₹25000

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img Duration

60 Hours

Course Details

Machine Learning & Deep Learning course is offered by Pythonsoft. Pythonsoft is a leading software training institute in India to provide quality training to students. As a training institute, we try to provide strong programming skills to our students. We provide both conceptual and practical-oriented training from very basic to advanced levels.

 

Content:

  • Chapter 1
  • Introduction
  • What is machine learning?
  • What is Deep learning?
  • What is data science
  • Supervised learning
  • Unsupervised learning
  • Difference between DS and ML and AI and DL
  • A sample programming Example
  • Chapter -2
  • Basics of python
  • Installing python
  • Different IDES
  • Variables
  • Data types
  • Loop
  • function
  • module and package
  • object oriented programming
  • python packages numpy,sklearn,matplotlib,pandas
  • Working with ANACONDA
  • Chapter -3
  • Numpy
  • Introduction to Numpy
  • Creating numpy array
  • Attributes of numpy array
  • Advantage of Numpy array over List
  • Mathematical operation on numpy array
  • Different ways to create numpy array
  • Reshaping numpy array
  • Concatenation and splitting operation
  • Trigonometric functions
  • Random sample generation
  • Chapter -4
  • Data analysis with Pandas
  • Pandas series
  • Pandas data frame
  • Reading CSV files
  • Parameters of read_csv()
  • Read excel files
  • Handling missing values
  • categorical data
  • Data cleaning and pre processing
  • Chapter -5
  • Data Visualization
  • Matplotlib
  • Seaborn
  • Chapter-6
  • Regression
  • Linear Regression
  • Multiple linear regression
  • Polynomial Regression
  • Logistic regression
  • Chapter-7
  • Logistic Regression
  • Introduction
  • Logistic function or sigmoid function
  • Types of logistic regression
  • Implementation
  • Chapter-8
  • K-Nearest Neighbors Algorithm
  • How KNN works
  • KNN classifier
  • Confusion Matrix
  • KNN Regressor
  • How to choose k value
  • Chapter -9
  • Naïve Bayes Algorithm
  • Bayes Theorem
  • Types of naïve bayes classifier
  • Bernoulli naïve bayes
  • Gaussian Naïve
  • Multinomial NB
  • Text Processing
  • Chapter -10
  • Decision Tree Algorithm
  • Why to use decision trees?
  • Decision Tree Terminologies
  • How a decision tree works
  • Advantages and disadvantages
  • Chapter -11
  • Random Forest Algorithm
  • What is random forest
  • How random forest works
  • Ensemble learning
  • Bagging and boosting
  • Advantages and disadvantages
  • Chapter -12
  • Support vector machine
  • What is support vector machine?
  • Types of SVM
  • Hyper plane and support vectors
  • How support vector works?
  • Chapter-13
  • Unsupervised Learning
  • What is unsupervised learning
  • Types of unsupervised learning
  • Applications of unsupervised learning
  • K-means clustering
  • Chapter 14
  • Feature engineering and Dimensional Reduction
  • feature extraction
  • feature selection
  • dummy variable and one hot encoding
  • Label encoding and ordinal encoding
  • Feature scaling
  • Hyper parameter tuning
  • dimension reduction (feature reduction)
  • Chapter-15
  • Model selection
  • What is Model Selection?
  • The need for Model Selection
  • Cross-Validation
  • What is Boosting?
  • How Boosting Algorithms work?
  • Types of Boosting Algorithms
  • Chapter -16
  • Time series prediction
  • What is Time Series Analysis?
  • Importance of TSA
  • Components of TSA
  • White Noise
  • AR model
  • MA model
  • ARMA model
  • ARIMA model
  • Chapter-17
  • Project work
  • Bhubaneshwar Branch

    Plot No-MB-63, GGP colony, Bhubaneshwar

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