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Best Data Science Training in Bangalore

About AMC Technologies :

We offer the Best Data Science training in bangalore that covers strong basics , intermediate level, advanced concepts. Get the Best Data Science training in bangalore from us will help you work in top-notch IT companies such as Google, IBM, Yahoo, Amazon, Accenture, Capegemini and more. With better understanding about the trending technologies, we are the best among the data science training institutes in bangalore. At AMC Technology, you can have in-depth learning and acquire the skills to apply the most effective tactics at the right time.

About Data Science :

Best Data Science Training in Bangalore

We are the best Data Science training courses provider and motivated to train you in the App Development technology. Our instructors are experienced professionals who possess great experience in MNCs and framework . Our training is 100% practical as we are aware of the industry needs and offer both Classroom and Online sessions.

Data Science Courses Descriptions:

1. Python for Data Science

2. Introduction to Statistics

  • Types of Statistics
  • Analytics Methodology and Problem-Solving Framework
  • Populations and samples
  • Parameter and Statistics
  • Uses of variable: Dependent and Independent variable
  • Types of Variable: Continuous and categorical variable

3. Descriptive Statistics

4. Probability Theory and Distributions

5. Picturing your Data

  • Histogram
  • Normal Distribution
  • Skewness, Kurtosis
  • Outlier detection

6. Inferential Statistics

7. Hypothesis Testing

8. Analysis of variance (ANOVA)

  • Two sample t-Test
  • F-test
  • One-way ANOVA
  • ANOVA hypothesis
  • ANOVA Model
  • Two-way ANOVA

9. Regression

  • Exploratory data analysis
  • Hypothesis testing for correlation
  • Outliers, Types of Relationship, Scatter plot
  • Missing Value Imputation
  • Simple Linear Regression Model
  • Multiple Regression
  • Model Building and Evaluation

10. Model post fitting for Inference

  • Examining Residuals
  • Regression Assumptions
  • Identifying Influential Observations
  • Detecting Collinearity

11. Categorical Data Analysis

  • Describing categorical Data
  • One-way frequency tables
  • Association
  • Cross Tabulation Tables
  • Test of Association
  • Logistic Regression
  • Model Building
  • Multiple Logistic Regression and Interpretation

12. Model Building and scoring for Prediction

  • Introduction to predictive modelling
  • Building predictive model
  • Scoring Predictive Model
  • Introduction to Machine Learning and Analytics

13. Introduction to Machine Learning

  • What is Machine Learning?
  • Fundamental of Machine Learning
  • Key Concepts and an example of ML
  • Supervised Learning
  • Unsupervised Learning

14. Linear Regression with one variable

  • Model Representation
  • Cost Function
  • Parameter Learning
  • Gradient Descent

15. Linear Regression with Multiple Variable

  • Computing parameter analytically
  • Ridge, Lasso, Polynomial Regression

16. Logistic Regression

  • Classification
  • Hypothesis Testing
  • Decision Boundary
  • Cost Function and Optimization

17. Multiclass Classification

18. Regularization

  • Overfitting, Under fitting

19. Model Evaluation and Selection

  • Confusion Matrix
  • Precision-recall and ROC curve
  • Regression Evaluation

20. Support Vector Machine

21. Decision Tree, Random Forest

22. Unsupervised Learning

  • Clustering
  • K-mean Algorithm

23. Dimensionality Reduction

  • Principal Component Analysis and applications

24. Introduction to text analytics

25. Introduction to Neural Network

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