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Data Science AI Deep Machine Learning training in Hyderabad. It is developed with data science tool and which is used to simplify and easily access the data and store the data easily. By R Programming language we can easily manipulate the data, also it can help in the analysis of Data, we can create the wonderful visualization and helps to access the high-quality content. This data science course in Kukatpally with Python Training provides you to learn data manipulation and cleaning of data using python.
Complete basics of Data Science Course in Kukatpally
The concepts of BigData and able to work in Data mining.
Understand the usage and how to use the tools like a tableau, map-reduce…
IT experienced Professional who are interested to build their career in development/ data scientist.
Any B.E/ B.Tech/ BSC/ MCA/ M.Sc Computers/ M.Tech/ BCA/ BCom College Students in any stream.
Fresh Graduates.
The course can learn by any IT professional having basic knowledge of:
Mathematics
Statistics
Any Programming Language
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Duration: 10 weeks
Time : 8am or 7pm
Mode : class room
Course Fee:Contact Us
Trainer :
SNO | TOPIC | SUB TOPIC | duration | |
0 | Machine Learning | introduction | ||
1 | Introduction to Statistics | Mean, Mode
Variance covariance Standard deviation Correlation Coefficient |
1 week | |
2 | Python |
data structures drawing graphs Numpy Pandas sklearn
|
Lists,Tuples,sets,dictionaries
Scatter, plot, Pie and Bar arrays,matrix,statistics series,frames,read csv/excel Data sets Apply Data Frame functions
|
2 week |
Data Analysis | data processing
|
read data from csv/excel sheet
Extract rows/ columns update df with new cols identify NAN values identify invalid data predict the value and replace
Introduction to Tensor with Tensorflow Linear Regression Using Tensorflow
|
1 week | |
3 | Regression | Linear regression
|
1 week | |
4 | Classification | logistic regression
Naïve Bayes Classifier random forest Time Series Analysis – ARIMA, Auto ARIMA |
1 week | |
5 | Clustering | K means clustering | ||
6 | Model Selection | Principal Compenent Analysis (PCA)
Recommendation Systems |
||
7 | Natural language processing NLP | Text Preprocessing
Topic Modeling Sentiment Analysis Vector Space Models: Word2Vec, GloVe
|
1 week | |
8 | Deep Learning | Neural networks
Single Layer & Multi-Layer Perceptron Forward Propagation Types of Activation functions Bias & Weights Optimization Techniques: RMSprop, Adam, Adagrad Architectures: Convolutional Neural Networks, CNN Recurrent Neural Networks, RNN & LSTM |
1 week | |
9 | ||||
Statistics:
Supervised Models:
Unsupervised Models:
Natural Language Processing – NLP:
Deep Learning:
Miscellaneous Concepts:
Tools: