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Things you will learn

Key highlights

Tools you can work hands on

Machine Learning with Python

Program Contents

  • Python Basics
  • Python Data Structures
  • Python Programming Fundamentals
  • File handling in PythonWorking with NumPy Arrays
      • Create
      • Insert
      • Delete
      • Update
      • Where
      • Orderby
      • Drop
      • Join
      • App Routing
      • URL Building
      • HTTP Methods
      • Templates
      • Request Object
      • Cookies
      • Session
      • File Uploading
      • Redirect & Errors
      • Flashing
      • SQLite
      • WTF
      • Cleaning Data
      • Cleaning Empty Cells
      • Cleaning Wrong Format
      • Cleaning Wrong Data
      • Removing Duplicates
      • Feature Selection
      • Numpy
      • Pandas
      • SciPy
      • Matplotlib
      • Seaborn
      • Linear Functions
      • Plotting Functions
      • Slope and Intercept
      • Percentiles
      • Standard Deviation
      • Variance
      • Correlation & Correlation Matrix
      • Confusion Matrix
      • Causality
      • Linear Regression
      • Supervised Learning
      • Classification
      • Regression
      • Estimation
      • Unsupervised Learning
      • Clustering
      • Prediction
      • Reinforcement Learning
      • Decision Making
      • Simple Linear Regression
      • Multiple Linear Regression
      • Polynomial Regression
      • Stepwise Regression
      • Ridge Regression
      • Lasso Regression
      • ElasticNet Regression
      • Support Vector Regression(SVR)
      • Decision Tree Regression
      • Random Forest Regression
      • Logistic Regression
      • K-Nearest Neighbors(K-NN)
      • Support Vector Machine(SVM)
      • Kernel SVM
      • Naive Bayes
      • Stochastic Gradient Descent
      • Random Forest Classification
      • Associate Rule Learning
      • Apriori
      • F-P Growth Algorithm
      • Connectivity-based Clustering(Hierarchical
        Clustering)
      • Centroids-based Clustering(Partitioning methods)
      • Density-based Clustering(Model based methods)
      • Fuzzy Clustering
      • Constraint-based(Supervised Clustering)
      • Auto Encoders
      • Ensemble Methods
      • BeautifulSoup
      • Neurons & Perceptron
      • Activation Function
      • Gradient Descent
      • Stochastic Gradient Descent
      • Backpropagation
      • Optimization Algorithms
      • SGD
      • Momentum
      • NAG
      • Adagrad
      • Adadelta
      • RMSprop
      • Adam
      • Batch Normalization
      • Hyperparameter tuning
      • Interpretability
      • Generative Adversarial Networks (GANs)
      • Radial Basis Function Networks (RBFNs)
      • Multilayer Perceptrons (MLPs)
      • Self Organizing Maps (SOMs)
      • Deep Belief Networks (DBNs)
      • Restricted Boltzmann Machines( RBMs)
      • Convolutional Neural Network & its Layers
      • CNN Architecture
      • Deep Learning Frameworks:
        • Tensorflow
        • Keras
        • PyTorch
      • Introduction to RNN 
      • LSTM(Long Short Term Memory)
      • Lemmatization and Stemming
      • Keyword Extraction
      • Topic Modeling
      • Aspect Mining
      • Named Entity Recognition
      • Machine Translation and more
      • Overview
      • Image Conversion
      • Drawing Functions
      • Blur
      • Filtering
      • Color and Face Detection

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