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Machine Learning Course | Edureka

** Machine Learning Masters Program: https://www.edureka.co/masters-program/machine-learning-engineer-training ** <br><br>This Edureka Machine Learning PPT on "Complete Machine Learning Course" will provide you with detailed and comprehensive knowledge of Machine Learning. It will provide you with the in-depth knowledge of the different types of Machine Learning with the different algorithms that lie under each category with a demo for each algorithm and the approach one should take to solve these problems. This PPT will be covering the following topics: <br><br>What is Data Science? <br>Data Science Peripherals <br>What is Machine learning? <br>Features of Machine Learning <br>How it works? <br>Applications of Machine Learning <br>Market Trend of Machine Learning <br>Machine Learning Life Cycle <br>Important Python Libraries <br>Types of Machine Learning <br>Supervised Learning <br>Unsupervised Learning <br>Reinforcement Learning <br>Detailed Supervised Learning <br>Supervised Learning Algorithms <br>Linear Regression <br>Use Case(with Demo) <br>Model Fitting <br>Need for Logistic Regression <br>What is Logistic Regression? <br>What is Decision Tree? <br>What is Random Forest? <br>What is Nau00efve Bayes? <br>Detailed Unsupervised Learning <br>What is Clustering? <br>Types of Clustering <br>Market Basket Analysis <br>Association Rule Mining <br>Example <br>Apriori Algorithm <br>Detailed Reinforcement Learning <br>Reward Maximization <br>The Epsilon Greedy Algorithm <br>Markov Decision Process <br>Q-Learning <br><br><br>Instagram: https://www.instagram.com/edureka_learning/ <br>Facebook: https://www.facebook.com/edurekaIN/ <br>Twitter: https://twitter.com/edurekain <br>LinkedIn: https://www.linkedin.com/company/edureka

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Machine Learning Course | Edureka

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  1. Agenda Agenda ❖ Introduction To Data Science ❖ What is Machine Learning? ❖ Applications of Machine Learning ❖ Various Types Of Machine Learning ❖ Supervised Learning in Depth ❖ Unsupervised Learning in Depth ❖ Reinforcement Learning in Depth MACHINE LEARNING CERTIFICATION TRAINING www.edureka.co/machine-learning-certification-training

  2. MACHINE LEARNING CERTIFICATION TRAINING www.edureka.co/machine-learning-certification-training

  3. MACHINE LEARNING CERTIFICATION TRAINING www.edureka.co/machine-learning-certification-training

  4. What is Data Science? What is Data Science? 2.5 x 1018 Bytes The United States alone faces a shortage of 140,000 to 190,000 people with analytical expertise and 1.5 million managers and analysts with the skills to understand and make decisions based on the analysis of big data MACHINE LEARNING CERTIFICATION TRAINING www.edureka.co/machine-learning-certification-training

  5. What is Data Science? What is Data Science? Data science, also known as data-driven science, is an interdisciplinary field about scientific methods, processes, and systems to extract knowledge or insights from data in various forms, either structured or unstructured. MACHINE LEARNING CERTIFICATION TRAINING www.edureka.co/machine-learning-certification-training

  6. Data Science Peripherals Data Science Peripherals Statistics MACHINE LEARNING CERTIFICATION TRAINING www.edureka.co/machine-learning-certification-training

  7. Data Science Peripherals Data Science Peripherals Statistics Prog Languages MACHINE LEARNING CERTIFICATION TRAINING www.edureka.co/machine-learning-certification-training

  8. Data Science Peripherals Data Science Peripherals Statistics Prog Languages Software MACHINE LEARNING CERTIFICATION TRAINING www.edureka.co/machine-learning-certification-training

  9. Data Science Peripherals Data Science Peripherals Statistics Prog Languages Software Machine Learning MACHINE LEARNING CERTIFICATION TRAINING www.edureka.co/machine-learning-certification-training

  10. Data Science Peripherals Data Science Peripherals Statistics Prog Languages Software Machine Learning Big Data MACHINE LEARNING CERTIFICATION TRAINING www.edureka.co/machine-learning-certification-training

  11. What is Machine Learning? What is Machine Learning? Machine Learning is a class of algorithms which is data-driven, i.e. unlike "normal" algorithms it is the data that "tells" what the "good answer" is Getting computers to program themselves and also teaching them to make decisions using data “Where writing software is the bottleneck, let the data do the work instead.” MACHINE LEARNING CERTIFICATION TRAINING www.edureka.co/machine-learning-certification-training

  12. Features of Machine Learning Features of Machine Learning MACHINE LEARNING CERTIFICATION TRAINING www.edureka.co/machine-learning-certification-training

  13. How It Works? How It Works? Learn from Data Find Hidden Insights Train and Grow MACHINE LEARNING CERTIFICATION TRAINING www.edureka.co/machine-learning-certification-training

  14. Applications of Machine Learning Applications of Machine Learning MACHINE LEARNING CERTIFICATION TRAINING www.edureka.co/machine-learning-certification-training

  15. Applications of Machine Learning Applications of Machine Learning MACHINE LEARNING CERTIFICATION TRAINING www.edureka.co/machine-learning-certification-training

  16. Applications of Machine Learning Applications of Machine Learning MACHINE LEARNING CERTIFICATION TRAINING www.edureka.co/machine-learning-certification-training

  17. Market Trend: Machine Learning Market Trend: Machine Learning MACHINE LEARNING CERTIFICATION TRAINING www.edureka.co/machine-learning-certification-training

  18. Machine Learning Life Cycle Machine Learning Life Cycle Collecting Data Analyse Data Test Algorithm Step 2 Step 4 Step 6 Step 1 Step 3 Step 5 Data Wrangling Train Algorithm Deployment MACHINE LEARNING CERTIFICATION TRAINING www.edureka.co/machine-learning-certification-training

  19. Step 1: Collecting Data Step 1: Collecting Data 1 2 3 4 5 6 MACHINE LEARNING CERTIFICATION TRAINING www.edureka.co/machine-learning-certification-training

  20. Data Wrangling Data Wrangling 1 2 3 4 5 6 MACHINE LEARNING CERTIFICATION TRAINING www.edureka.co/machine-learning-certification-training

  21. Analyse Data Analyse Data 1 2 model 3 4 5 6 MACHINE LEARNING CERTIFICATION TRAINING www.edureka.co/machine-learning-certification-training

  22. Train Algorithm Train Algorithm 1 2 model 3 Training set 4 5 6 MACHINE LEARNING CERTIFICATION TRAINING www.edureka.co/machine-learning-certification-training

  23. Test Algorithm Test Algorithm 1 2 model 3 Test set Accurate? 4 5 6 MACHINE LEARNING CERTIFICATION TRAINING www.edureka.co/machine-learning-certification-training

  24. Operation and Optimization Operation and Optimization 1 2 3 4 5 6 MACHINE LEARNING CERTIFICATION TRAINING www.edureka.co/machine-learning-certification-training

  25. Important Python Libraries Important Python Libraries MACHINE LEARNING CERTIFICATION TRAINING www.edureka.co/machine-learning-certification-training

  26. Types of Machine Learning Types of Machine Learning MACHINE LEARNING CERTIFICATION TRAINING www.edureka.co/machine-learning-certification-training

  27. Supervised Learning Supervised Learning It’s a Face Label Face Label Non-Face MACHINE LEARNING CERTIFICATION TRAINING www.edureka.co/machine-learning-certification-training

  28. Unsupervised Learning Unsupervised Learning MACHINE LEARNING CERTIFICATION TRAINING www.edureka.co/machine-learning-certification-training

  29. Reinforcement Learning Reinforcement Learning MACHINE LEARNING CERTIFICATION TRAINING www.edureka.co/machine-learning-certification-training

  30. Supervised Learning Supervised Learning Supervised learning is where you have input variables (x) and an output variable (Y) and you use an algorithm to learn the mapping function from the input to the output It is called Supervised Learning because the process of an algorithm learning from the training dataset can be thought as a teacher supervising the learning process MACHINE LEARNING CERTIFICATION TRAINING www.edureka.co/machine-learning-certification-training

  31. Supervised Learning Supervised Learning Machine Learning Training Dataset Statistical Models Training and Testing Prediction & Testing Historical Data Random Sampling Prediction Testing Dataset Model Validation Outcome MACHINE LEARNING CERTIFICATION TRAINING www.edureka.co/machine-learning-certification-training

  32. Supervised Learning Supervised Learning Training and Testing New Data Model Predicted Outcome Prediction MACHINE LEARNING CERTIFICATION TRAINING www.edureka.co/machine-learning-certification-training

  33. Supervised Learning Algorithms Supervised Learning Algorithms Linear Regression Logistic Regression Decision Tree Random Forest Naïve Bayes Classifier MACHINE LEARNING CERTIFICATION TRAINING www.edureka.co/machine-learning-certification-training

  34. Linear Regression Linear Regression Linear Regression Analysis is a powerful technique used for predicting the unknown value of a variable (Dependent Variable) from the known value of another variables (Independent Variable) • A Dependent Variable(DV) is the variable to be predicted or explained in a regression model • An Independent Variable(IDV) is the variable related to the dependent variable in a regression equation MACHINE LEARNING CERTIFICATION TRAINING www.edureka.co/machine-learning-certification-training

  35. Simple Linear Regression Simple Linear Regression Dependent Variable Independent Variable Y = a + bX Y - Intercept Slope of the Line MACHINE LEARNING CERTIFICATION TRAINING www.edureka.co/machine-learning-certification-training

  36. Regression Line Regression Line Linear Regression Analysis is a powerful technique used for predicting the unknown value of a variable (Dependent Variable) from The regression line is simply a single line that best fits the data (In terms of having the smallest overall distance from the line to the points) Fitted Points Regression Line MACHINE LEARNING CERTIFICATION TRAINING www.edureka.co/machine-learning-certification-training

  37. Demo Copyright © 2019, edureka and/or its affiliates. All rights reserved.

  38. Real Estate Company Use Case Real Estate Company Use Case Hi I am john, I need some baseline for pricing my Villas and Independent Houses MACHINE LEARNING CERTIFICATION TRAINING www.edureka.co/machine-learning-certification-training

  39. Dataset Description Dataset Description Column CRIM ZN INDUS CHAS NOX RM AGE DIS RAD TAX PTRATIO B LSTAT MEDV Description per capita crime rate by town proportion of residential land zoned for lots over 25,000 sq.ft. proportion of non-retail business acres per town. Charles River dummy variable (1 if tract bounds river; 0 otherwise) nitric oxides concentration (parts per 10 million) average number of rooms per dwelling proportion of owner-occupied units built prior to 1940 weighted distances to five Boston employment centres index of accessibility to radial highways full-value property-tax rate per $10,000 pupil-teacher ratio by town 1000(Bk - 0.63)^2 where Bk is the proportion of blacks by town % lower status of the population Median value of owner-occupied homes in $1000's MACHINE LEARNING CERTIFICATION TRAINING www.edureka.co/machine-learning-certification-training

  40. Steps Steps Collecting Data Analyse Data Test Algorithm Step 2 Step 4 Step 6 Step 1 Step 3 Step 5 Data Wrangling Train Algorithm Deployment MACHINE LEARNING CERTIFICATION TRAINING www.edureka.co/machine-learning-certification-training

  41. Model Fitting Model Fitting Fitting a model means that you're making your algorithm learn the relationship between predictors and outcome so that you can predict the future values of the outcome . So the best fitted model has a specific set of parameters which best defines the problem at hand MACHINE LEARNING CERTIFICATION TRAINING www.edureka.co/machine-learning-certification-training

  42. Types of Fitting Types of Fitting Machine Learning algorithms first attempt to solve the problem of under-fitting; that is, of taking a line that does not approximate the data well, and making it to approximate the data better. MACHINE LEARNING CERTIFICATION TRAINING www.edureka.co/machine-learning-certification-training

  43. Need For Logistic Regression Need For Logistic Regression Here, the best fit line in linear regression is going below 0 and above 1 WHO WILL WIN ? MACHINE LEARNING CERTIFICATION TRAINING www.edureka.co/machine-learning-certification-training

  44. What is Logistic Regression? What is Logistic Regression? Logistic Regression is a statistical method for analysing a dataset in which there are one or more independent variables that determine an outcome. Outcome is a binary class type. The outcome(result) will be binary(0/1) 0- If malignant 1- If benign MACHINE LEARNING CERTIFICATION TRAINING www.edureka.co/machine-learning-certification-training

  45. What is Logistic Regression? What is Logistic Regression? The Logistic Regression Curve is called as “Sigmoid Curve”, also known as S-Curve Based on the threshold value set, we decide the output from the function MACHINE LEARNING CERTIFICATION TRAINING www.edureka.co/machine-learning-certification-training

  46. What is Polynomial Regression? What is Polynomial Regression? When we have non linear data, which can’t be predicted with a linear model. We switch to Polynomial Regression. Such a scenario is shown in the below graph MACHINE LEARNING CERTIFICATION TRAINING www.edureka.co/machine-learning-certification-training

  47. What is a Decision Tree? What is a Decision Tree? A decision tree is a tree-like structure in which internal node represents test on an attribute • Each branch represents outcome of test and each leaf node represents class label (decision taken after computing all attributes) True False • A path from root to leaf represents classification rules. MACHINE LEARNING CERTIFICATION TRAINING www.edureka.co/machine-learning-certification-training

  48. Building a Decision Tree Building a Decision Tree MACHINE LEARNING CERTIFICATION TRAINING www.edureka.co/machine-learning-certification-training

  49. Building a Decision Tree Building a Decision Tree MACHINE LEARNING CERTIFICATION TRAINING www.edureka.co/machine-learning-certification-training

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