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Python Machine Learning Case Studies: Five Case Studies for the Data Scientist
Chapter 1: Statistics and ProbabilityChapter Introduction and hands on approach to central limit theorem, distributions, confidence intervals, statistical tests, ROC curves, plots, probabilities, permutations and combinationsNo of 70-80Sub -Topics1. Exploratory Data analysis2. Probability Distributions3. Concept of Permutations and Combinations4. Statistical tests5. Applications in the industry6. Case study
Chapter 2: RegressionChapter Introduction and hands on approach to the concept of regression, linear regression models, non linear regression models.No of 50-60Sub - Topics1. Concept of Regression2. Linear regression3. Polynomial order regression4. Statistical tests5. Applications in the industry6. Case study<Chapter 3: Time series modelsChapter Introduction and hands on approach to concepts of trends, cycles, seasonal variations, anomaly detection, exponential smoothing, rolling moving averages, ARIMA, ARMA, over fitting.No of 60-70Sub - Concept of trends, cycles, and seasonal variations2. Time series decomposition3. ARIMA, and ARMA models4. Concept of over fitting5. Statistical tests6. Applications in the industry7. Case study
Chapter 4: Classification and ClusteringChapter Introduction and hands on approach to supervised, semi supervised and unsupervised models. Emphasis on Logistic regression, k-means, Support Vector Machines, Neural networksNo of 80-90Sub - Concept of Classification and clustering2. Deep neur3. Support Vector Machines4. Concept of Gradient descent5. Statistical tests6. Applications in the industry7. Case study
Chapter 5: Ensemble methodsChapter Introduction and hands on approach to Bagging, and Gradient BoostingNo of 50-60Sub - Concept of ensemble methods2. Concept of Bagging 3. Concept of Gradient Boosting4. Statistical tests5. Applications in the industry6. Case study
Chapter 2: RegressionChapter Introduction and hands on approach to the concept of regression, linear regression models, non linear regression models.No of 50-60Sub - Topics1. Concept of Regression2. Linear regression3. Polynomial order regression4. Statistical tests5. Applications in the industry6. Case study<Chapter 3: Time series modelsChapter Introduction and hands on approach to concepts of trends, cycles, seasonal variations, anomaly detection, exponential smoothing, rolling moving averages, ARIMA, ARMA, over fitting.No of 60-70Sub - Concept of trends, cycles, and seasonal variations2. Time series decomposition3. ARIMA, and ARMA models4. Concept of over fitting5. Statistical tests6. Applications in the industry7. Case study
Chapter 4: Classification and ClusteringChapter Introduction and hands on approach to supervised, semi supervised and unsupervised models. Emphasis on Logistic regression, k-means, Support Vector Machines, Neural networksNo of 80-90Sub - Concept of Classification and clustering2. Deep neur3. Support Vector Machines4. Concept of Gradient descent5. Statistical tests6. Applications in the industry7. Case study
Chapter 5: Ensemble methodsChapter Introduction and hands on approach to Bagging, and Gradient BoostingNo of 50-60Sub - Concept of ensemble methods2. Concept of Bagging 3. Concept of Gradient Boosting4. Statistical tests5. Applications in the industry6. Case study
224 pages, Paperback
Published October 29, 2017
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