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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    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



      We have limited participants or only one participant in a live Online Training session to maintain the quality standards. Se we provide demonstration session before enrollment. without attending demonstration learner couldn’t able to clear his technical doubts about his desired technology. So we always recommend a learner to attend a demonstration with the Instructor who will deliver Online Training

      All the instructors at TechEClasses are experts from the industry with minimum 9-12 years of relevant IT experience. They are subject matter experts for providing an awesome learning experience for Online Training.

      Yes you will get  Training videos during Online Training and it is compulsory to work at home to enhance real time scenarios. And yes instructor will provide Online Training study material during training tenure.

      Yes you will get a Course completion training certificate from TechEClasses ,and Instructor will assist you to prepare workday certification.

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      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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