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Machine learning Training with Data Science Expert

👉 35 Hours of Instructor-Led Google DFP Training

 👉 Real World use cases and Scenarios 

 👉 Hands on Practical Experience 

Machine learning is the branch of Artificial Intelligence where computer learns the rules of solving complex problems without explicitly programmed. It enables the development of computer programs that can access data and use it for learn themselves.

Machine learning let computers learns automatically without human intervention based on the observations or data, past experiences to look patterns in the data and make decision in the future.

Will Cover 

  • Data Science (Pandas, NumPy, SciPy, Scikit-learn, matplotlib)
  • Exploratory Data Analysis, Statistics
  • Machine learning Algorithms (Supervised & Unsupervised), Model Tuning, Recommendation Systems
  • Hands on Projects and Assignments

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Machine Learning Training Course Overview

What you’ll learn: By end of the training you will have the deep understanding/knowledge of how Machine learning algorithm works, how to optimize it and how you can apply these algorithms with real world data with practical applications. You will be solving multiple case studies as well as assignments to have more hands-on experience.

You will learn mathematical and heuristic aspects for Machine Learning algorithms.

Introduction to Machine Learning

  • How  What is Machine learning?

  • Machine learning applications

  • Kinds of Machine learning problems

  • Tools for Machine learning

  • When to apply Machine learning.

Data Handling using numpy and pandas

  • Numpy arrays

  • Pandas and Data Frame

  • Data import and export

  • Data transformations

  • Practice Assignment

Exploratory Data Analysis

  • Data visualization using matplotlib

  • Data insights

  • Probability and Central tendency

  • Summary Statistics

  • Data distribution

  • Handling missing values

  • Correlation analysis

  • Outlier detection

  • Practice Assignment

Generalized Liner Model

  • Linear Regression Algorithm

  • Feature engineering

  • Cost function

  • Gradient Descent

  • Model building process

  • Overfitting and Under fitting

  • Bias variance tradeoff

  • Model evaluation metrics(MSE,RMSE)

  • Logistic Regression Algorithm

  • Confusion matrix

  • ROC plot

  • Case study using Linear Regression & Logistic Regression

Unsupervised Learning

  • K-Means Clustering Algorithm

  • K-Mediods Clustering Algorithm

  • Determination of right K

  • Hierarchical clustering Technique

  • Case Study using Clustering Algorithm

Time Series Analysis

  • Understanding trend, seasonality and randomness in time series data

  • Stationary

  • ACF and PACF

  • Time Series forecasting using ARIMA

  • Case study using Time Series analysis

Decision Tree Algorithm

  • Entropy

  • Information gain, Gini index

  • Building Decision Tree

  • Case study using Decision Tree

Support Vector Machine Algorithm

  • Geometric intuition

  • Mathematical derivation

  • Kernel trick

  • Cost complexity

  • Case study using SVM

K-Nearest Neighbors Algorithm

  • Distance measures: Manhattan, Euclidean, Hamming

  • Cosine distance and cosine similarity

  • Decision surface

  • Overfitting and Underfitting

  • Classification and Regression

  • Limitation of KNN

  • Case study using KNN

Ensemble model

  • Weak learner and Strong learner

  • Bagging techniques

  • Boosting techniques

  • Case study using ensemble model

Random Forest Algorithm

  • Geometric intuition

  • Feature selection

  • Feature Importance

  • Case study using Random Forest Algorithm

Building Recommendation Engine

  • Collaborative filtering model

  • Matrix factorization

  • Popularity Model

  • Implement Recommendation Engine

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