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Batch
Date: Oct
19th @ 8:30PM
Faculty: Mrs. Sasmitha
Duration: 45 Days
Venue
:
DURGA SOFTWARE SOLUTIONS at Maitrivanam
Plot No : 202,
IInd Floor ,
HUDA Maitrivanam,
Ameerpet, Hyderabad-500038.
Ph.No: +91 - 9246212143, 80 96 96 96 96
Syllabus:
DATA SCIENCE
Module - 1:
> Installation
> Jupyter Notebook
> Introduction to Python
> Variable
> Type conversion
> Comments, Indentation
> Taking input from keyboard
> If, else
> for loop, while loop.
> Operators
> Datatypes
> List
> Tuple
> Set
> Dictionary
Module - 2:
> Numpy
> Introduction to numpy
> Different methods to create an Array
> Numpy datatypes
> type conversion
> Numpy-Basic operation
> Numpy- universal functions
> Numpy- linear Algebra
> hstack, vstack
Module - 3:
> Pandas
> Introduction to Pandas
> creating series
> creating Data Frames
> selection and Indexing
> conditional Selection
> group by
> pivot table
> joining, merging, concatenation
> Missing value treatment
> sortby
> time series data handling using pandas.
Module - 4:
> Data Visualisation
> Introduction to Seaborn
> Numerical Plots
> Distribution plot
> joint plot
> pain plot
> mug plot
> line blot. Area plot.
> scatter plot
> Categorical Plots
> bar Plot
> count plot
> cat plot
> box plot
> Violin blot
> strip plot
> swarm Plot
> Styling of Plot using Matplotlib
> Styling of Plot using Seaborn
> Multiple plots, hybrid Plot
Module - 5:
> Introduction to Statistics.
> Mean, Median, Mode
> Measure of central tendency
> Measure of Dispersion
> Range, Interquartile range
> Variance
> Standard deviation
> ZScore
> Correlation
> Pearson coefficient correlation constant- 'r’
> Hypothesis Testing
> Annova
> Normal Distribution
Module - 6:
> Introduction to Machine learning
> Regression
> Linear Regression
> Assumptions of LR
> cost function
> Theory about gradient
> Gradient Descent
> optimising cost function
> Multi co linearity
> Over fitting, under fitting
> Ridge Regression
> what is Ridge Regression
> where to use Ridge Regression
> Lasso Regression
> what is lasso Regression
> where to use Lasso Regression
> Polynomial Regression
> Classification
> Decision Tree
> Understanding ID3 Algorithm
> Entropy
> Information Gain
> Bias variance tradeoff
> Random Forest
> Bagging, Bootstrap
> Difference between Random forest and Decision Tree
> Python Implementation of DT
> Python Implementation of Rf
> Visualising DT and Rf
> Hyper parameters selection in Rf
Module - 7:
> Model Validation
> Regression
> MAE, MAPE, MSE, RMSE
> r, r-square, adjusted r square
> Classification
> Recall
> Precision
> classification Report
> confusion matrix.
> Auc, ROC
> Cross Validation
Module - 8:
> Difference between supervised and unsupervised
> K means Clustering
> How to select k in k means
> Python Implementation of K means.
Module - 9:
> Introduction to Deep learning
> Difference between ML and DL
> Installation
> what is a perceptron
> Neural Network Architecture
> Activation functions
> step function
> sigmoid function
> Re LU
> Leaky ReLU
> Cross entropy loss function
> Gradient Descent
> Batch, mini Batch
> epoch
> dropout
> Build first DL model using keras
Module - 10:
> Introduction to Natural Language Processing
> Installation
> Text preprocessing
> Stop word Removal
> Tokenization
> n grams
> Pos tagging
> count vectorized
> tf-idf vectorizer
> Creating Model Pipeline.
> Building a Sentiment Classifier
Module - 11:
> Resume prepation tips.
> Explaining some Real Time Projects from different domain
Module - 12:
> Dimensionality Reduction (Recordings)
> PCA detail explanation
> Math Behind PCA
> Python Implementation of PCA
> Cheetsheet, Summary notes
> Anomaly Detection (Recordings)
> what is an outlier
> LOCI-fast outlier Detection using the local correlation Integral
> Python Implementation of LOCI
> Time Series (Recordings)
> Introduction to Time series
> Time Series Data Preprocessing using Pandas
> Trend and seasonality
> Exponential smoothing
> Holt winter Algorithm.
> Python Implementation
