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    machine-learning-elearning

    Course Overview

    Machine learning (ML) is the study of computer algorithms that improve automatically through experience.It is seen as a subset of artificial intelligence. Machine learning algorithms build a mathematical model based on sample data, known as "training data", in order to make predictions or decisions without being explicitly programmed to do so. Machine learning algorithms are used in a wide variety of applications, such as email filtering and computer vision, where it is difficult or infeasible to develop conventional algorithms to perform the needed tasks.

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    COURSES FEATURES

    Language          : English
    Lectures            : 03
    Certification     : Yes
    Project              : 01
    Duration           : 45 hrs
    Max-Students : 20
    • Introduction to Supervised Learning
    • Introduction to unsupervised learning
    • Introduction to reinforcement learning
    • Machine Learning versus Rule-based programming
    • Understanding What Machine Learning can do using the Tasks Framework
    • Creating Machine-Learning Models with Python and scikit learn.
    • Types of datasets used in Machine Learning.
    • Life Cycle of Machine Learning
    • Dealing with Missing Values – An example
    • Standardization and Normalization to Deal with Variables with Different Scales
    • Types of scaling techniques
    • Eliminating Duplicate Entries
    • Learning Rules to Classify Objects?
    • Understanding Logistic Regression
    • Applying Logistic Regression to The Iris classification Task
    • Closing Our First Machine Learning Pipeline with a Simple Model Evaluator
    • Creating Formulas that predict the Future – A House Price Example
    • Understanding Linear Regression
    • Applying Linear Regression to the Boston House Price Task
    • Evaluating Numerical Predictions with Least Squares
    • Gradient Descent Algorithm
    • Batch Gradient Descent
    • Stochastic Gradient Descent algorithm
    • Exploring Unsupervised Learning and Its Usefulness
    • Finding Groups Automatically with k-means clustering
    • Reducing The Number of variables in your data with PCA
    • Smooth out your Histograms with kernel Density Estimation
    • Decision Trees Classifier
    • Decision Tree Regressor
    • Random Forest Classifier
    • Random Forest Regressor
    • Automatic Feature Engineering with Support Vector Machines
    • Deal with Nonlinear Relationships with Polynomial Regression
    • Reduce the number of Learned Rules with Regularization
    • Using Feature Scaling to Standardize Data
    • Implementing Feature Engineering with Logistic Regression
    • Extracting Data with Feature Selection and Interaction
    • Combining all Together
    • Build Model Based on Real-world Problems
    • Support Vector machines
    • Implementing kNN on the Data set
    • Decision Tree as Predictive Model
    • Dimensionality Reduction techniques
    • Combining all Together
    • Random Forest for Classification
    • Gradient Boosting Trees and Bayes Optimization
    • CatBoost to Handle Categorical Data
    • Implement Blending
    • Implement Stacking
    • Memory-Based Collaborative Filtering
    • Item-to-Item Recommendation with kNN
    • Applying Matrix Factorization on Datasets
    • Word batch for Real-world Problem
    • Validation Dataset Tuning.
    • Regularizing model to avoid over fitting
    • Adversarial Validation
    • Perform metric Selection on real Data.
    • Tune a linear model to predict House prices
    • Tune an SVM to predict a politician’s Party Based on their Voting Record
    • Splitting your datasets into train, test and validate
    • Persist Models by Saving Them to Disk
    • Transform your variable length Features into One-Hot Vectors
    • Finding the most important Features in your classifier
    • Predicting Multiple Targets with the Same Dataset
    • Retrieving the Best Estimators after Grid Search
    • Extracting Decision Tree Rules from Scikit-learning
    • Finding out which features are important in Random Forest Model
    • Classifying with SVMs, when your data has unbalanced classes
    • Computing True/False Positives/Negatives after in scikit-learn
    • Labelling Dimensions with Original Feature Names after PCA
    • Clustering Text Documents with Scikit-learn k-means
    • Listing Word Frequency in a Corpus Using Only scikit-learn
    • Polynomial Kernel Regression Using Pipelines
    • Visualize outputs over two dimensions using Numpy’s Meshgrid
    • Drawing out a Decision Tree Trained in scikit-learn
    • Clarify your Histogram by Labeling each Bin
    • Centralizing Your Color legend when you have multiple subplots
    • Programming with TENSORFLOW
    • Implementation of all above models with TENSORFLOW

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      Feel free to reach us at Phone: +91 9553700070 or mail us at contact@techeclasses.com

      COURSES FEATURES

      Language          : English
      Lectures            : 03
      Certification     : Yes
      Project              : 01
      Duration           : 45 hrs
      Max-Students : 20
      Campus placements, Freshers, Beginners and Working Professionals. For more details enroll now and get a call from us.
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