Best Data Science Training in Hyderabad | Online Course | Machine Learning with Python

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DATA SCIENCE (AI, ML, DL) Course Content

1. Introduction to Data Science
   a. What is data science?
   How is data science different from BI and Reporting?
   b. What is the difference between AI, Data Science, Machine Learning, Deep Learning
   c. Job Landscape and Preparation Time
   d. Who are data scientists?
    What skill sets are required?
   e. What is the day to day job of Data Scientist
    What kind of projects did they work on?
   f. End to End Data Science Project Life Cycle
2. Business Statistics
   a. Data types
    Continuous variables
    Ordinal Variables
    Categorical variables
    Time Series
    Miscellaneous
    Common Data Science Terminology
   b. Descriptive statistics
    Basics concepts of probability
    Frequentist versus Bayesian Probability
    Axioms of probability theory,
    Permutations and combinations
    Conditional and marginal probability
    Joint Probability
    Bayes Theorem
    Probability Mass Function and Probability Density Function
    Cumulative Mass Function and Cumulative Density Function
   c. Central Tendencies
    Mean
    Median
    Mode
    Spread
    Variance
    Standard Deviation
    Effects on central tendencies after transformations
    Quartile Analysis
    Implementation of central tendencies using python
    Box Plots for outlier identification
    Drawing Box plots using python
   d. Sampling

DATA SCIENCE (AI, ML, DL)
 Need for Sampling?
 Different types of Sampling
 Simple random sampling
 Systematic sampling
 Stratified Sampling
 Implementation of sampling techniques using python
e. Data distributions
 Normal Distribution
 Binomial Distribution
 Binomial Approximated to Normal
 Implementation of distributions using python
f. Inferential statistics
 Why inferential statistics?
 Z score calculation
 Defining p value and implementations using python
 Inferring from sample to population
 Sampling distribution of sample means
g. Hypothesis testing
 Confidence Interval
 Testing the hypothesis
 Type I error
 Type II error
 Null and alternate hypothesis
 Reject or acceptance criterion

3. Python for Data Science
   a. Understanding the reason of Python’s popularity
   b. Basics of Python: Operations, loops, functions, dictionaries
   c. Numpy – creating arrays, reading, writing, manipulation techniques
   d. Ground-up for Deep-Learning
4. Exploratory Data Analysis with Python

DATA SCIENCE (AI, ML, DL)
a. Getting to understand structure of Matplotlib
b. Configuring grid, ticks.
c. text, color map, markers, widths with Matplotlib
d. configuring axes, grid,
e. hist, scatterplots
f. bar charts
g. multiple plots
h. 3D plots
i. Correlation matrix plotting

5. Data Munging with Python
   a. Introduction to pandas
   b. Data loading with Pandas
   c. Data types with python
   d. Descriptive Statistics with Pandas
   e. Quartile analysis with Pandas
   f. Sort, Merge, join with Pandas
   g. Indexing and Slicing with pandas
   h. Pivot table, Aggregate and cross tab with pandas
   i. Apply function for parallel processing with Python
   j. Cleaning Data with python
   k. Determining correlation
   l. Handling missing values
   m. Plotting with Pandas
   n. Time series with Pandas
6. Introduction to Artificial Intelligence
   a. Dealing Prediction problem
   b. Forecasting for industry
   c. Optimization in logistics
   d. Segmentation in customer analytics
   e. Supervised learning
   f. Unsupervised Learning
   g. Optimization
   h. Types of AI : Statistical Modelling, Machine Learning, Deep Learning,
   Optimization, Natural Language Processing, Computer vision, Speech
   Processing, Robotics
7. Artificial Intelligence I – Statistical Modelling
   a. Linear Regression
    Assumptions
    Model development and interpretation
    Sum of least squares

DATA SCIENCE (AI, ML, DL)
 Model validation – tests to validate assumptions
 Multiple linear regression
 Disadvantages of linear models
b. Logistic Regression
 Need for logistic regression
 Logit link function
 Maximum likelihood estimation
 Model development and interpretation
 Confusion Matrix – error measurement
 ROC curve
 Measuring sensitivity and specificity
 Advantages and disadvantages of logistic regression models
c. Time series analysis – Forecasting

1. Simple moving averages
2. Exponential smoothing
3. Time series decomposition
4. ARIMA

d. Model validation and deployment
 RMSE – Root Mean squared error
 MAPE – Mean Average Percentage Error
 Confusion matrix and Misclassification rate
 Area under the curve (AUC) , ROC curve

8. Artificial Intelligence II – Machine Learning
   a. Supervised Learning
   
   1.
   Decision trees and Random Forest
   C5.0
9. Classification and Regression trees(CART)
10. Process of tree building
11. Entropy and Gini Index
12. Problem of over fitting
13. Pruning a tree back
14. Trees for Prediction (Linear) – example
15. Tress for classification models – example
    
    9.
    1.
    Advantages of tree-based models?
    Association Rule Mining
    Rules generation from decision trees,
16. Apriori algorithm
    
    3.
    1.
    Support, confidence and lift measures
    Support Vector Machines
    Linear learning machines
17. SVM case for linearly separable data
18. Kernel space
     Neural Networks