Sinossi
Chapter 1: Statistics and Probability
Chapter Goal: Introduction and hands on approach to central limit theorem, distributions, confidence intervals, statistical tests, ROC curves, plots, probabilities, permutations and combinations
No of pages: 70-80
Sub -Topics
1. Exploratory Data analysis
2. Probability Distributions
3. Concept of Permutations and Combinations
4. Statistical tests
5. Applications in the industry
6. Case study
Chapter 2: Regression
Chapter Goal: Introduction and hands on approach to the concept of regression, linear regression models, non linear regression models.
No of pages: 50-60
Sub - Topics
1. Concept of Regression
2. Linear regression
3. Polynomial order regression
4. Statistical tests
5. Applications in the industry
6. Case study
&
amp;amp;amp;amp;amp;amp;amp;amp;amp;lt;Chapter 3: Time series models
Chapter Goal: 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 pages: 60-70
Sub - Topics:
1. Concept of trends, cycles, and seasonal variations
2. Time series decomposition
3. ARIMA, and ARMA models
4. Concept of over fitting
5. Statistical tests
6. Applications in the industry
7. Case study
Chapter 4: Classification and Clustering
Chapter Goal: Introduction and hands on approach to supervised, semi supervised and unsupervised models. Emphasis on Logistic regression, k-means, Support Vector Machines, Neural networks
No of pages: 80-90
Sub - Topics:
1. Concept of Classification and clustering
2. Deep
neur3. Support Vector Machines4. Concept of Gradient descent
5. Statistical tests
6. Applications in the industry
7. Case study
Chapter 5: Ensemble methods
Chapter Goal: Introduction and hands on approach to Bagging, and Gradient Boosting
No of pages: 50-60
Sub - Topics:
1. Concept of ensemble methods
2. Concept of Bagging
3. Concept of Gradient Boosting
4. Statistical tests
5. Applications in the industry
6. Case study
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