Best Data Science Training in Hyderabad

1. INTRODUCTION TO PYTHON
2. DIFFERENT MODES IN PYTHON
3. VARIABLES IN THE PYTHON
4. PYTHON OPERATORS AND OPERANDS
5. PYTHON CONDITIONAL STATEMENTS
6. PYTHON LOOPS
7. LEARNING PYTHON STRINGS
8. SEQUENCE IN PYTHON
9. PYTHON LISTS
10. PYTHON TUPLE
11. PYTHON SETS
12. PYTHON DICTIONSRY
13. PYTHON FUNCTIONS
14. PYTHON MODULES
15. PYTHON DATE AND TIME
16. READING AND WRITING FILES
17. PYTHON OS MODULES
18. PYTHON EXCEPTION HANDLING
19. PYTHON ITERATORS
20. PYTHON GENERATORS
21. PYTHON DECORATORS
22. PYTHON CLASS AND OBJET(OOP)
23. OOP PRINCIPLES
24. GARBAGE COLLECTION
25. INHERITANCE
26. MULTIPLE INHERITANCE
27. OPERATOR OVERLOADING
28. POLYMORPHISM
29. ABSTRACTION
30. ENCAPSULATION
31. PYTHON REGULAR EXPRESSIONS

1. UNDERSTANDING THE DATA
2. PROBABILITY DISTRIBUTIONS
3. SAMPLING DISTRIBUTIONS
4. HYPOTHESIS TESTING
5. ASSOCIATION BETWEEN CATEGORICAL VARIABLES
6. ANOVA ANALYSIS

◆ PANDAS

◆ NUMPY

◆ SKLEARN

◆ SCIPY

◆ PLOTLY

◆ MATPLOTLIB AND SEABORN

◆ KERAS

◆ TENSORFLOW

◆ PYTORCH

◆ NLTK

◆ SPACY

◆ MACHINE LEARNING FUNDAMENTALS

◆ UNDERSTANDING SUPERVISED AND UNSUPERVISED LEARNING TECHNIQUES

◆ CLUSTERING

◆ IMPLEMENTATION OF ASSOCIATION RULE

◆ UNDERSTANDING THE PROCESS FLOW OF SUPERVISED LEARNING TECHNIQUE

◆ LINEAR REGRESSION

◆ MULTI LINEAR REGRESSION

◆ POLYNOMIAL LINEAR REGRESSION

◆ LOGISTIC REGRESSION

◆ DECISION TREE

◆ RANDOM FOREST

◆ SUPPORT VECTOR MACHINES

◆ K NEAREST NEIGHBOUR

◆ XG BOOST

◆ ADA BOOST

◆ BAGGING CLASSIFIER

◆ VOTING CLASSIFIER

◆ NAIVE BAYS CLASSIFIER

◆ FEATURE ENGINEERING

◆ TEXT MINING

◆ SENTIMENT ANALYSIS

◆ TIME SERIES ANALYSIS

◆ STUDYING VARIOUS ALGORITHMS THEORITICALLY AND PROGRAMATICALLY

◆ APPLYING DIFFERENT ALGORITHMS TO DIFFERENT DATASETS

◆ FEATURE SELECTION AND PROCESSING

◆ HOW TO SELECT THE RIGHT DATA

◆ FEATURE SELECTION TECHNIQUES

◆ PREPROCESSING INTRODUCTION

◆ NORMALIZATION TECHNIQUES

◆ SCALING TECHNIQUES

◆ REGULARISATION TECHNIQUES

◆ STANDARDIZATION TECHNIQUES

◆ PRINCIPLE COMPONENT ANALYSIS

◆ SINGULAR VALUE DECOMPOSITION

◆ LINEAR DISCRIMINATE ANALYSIS

◆ GRADIENT DESCENT CONCEPTS

◆ MODEL SELECTION CROSS VALIDATION

◆ INTRODUCTION TO MODEL TUNING

◆ PARAMETER TUNING GRID SEARCHCV

◆ SELECTING THE BEST ALGORITHM

◆ MACHINE LEARNING VS DEEP LEARNING

◆ BASICS OF BIOLOGICAL NEURON

◆ BASICS OF ARTIFICIAL NEURON

◆ PERCEPTRON

◆ WHAT IS NEURON

◆ WHAT IS INPUT LAYER

◆ WHAT IS HIDDEN LAYER

◆ WHAT IS OUTPUT LAYER

◆ WHAT IS FULLY CONNECTED NETWORK

◆ LINERA FUNCTIONS

◆ NON LINEAR FUNCTIONS

◆ ACTIVATION FUNCTIONS

◆ LOSS FUNCTIONS

◆ OPTIMIZERS

◆ GRADIENT

◆ GRADIENT DESCENT

◆ STOCHASTIC GRADIENT DESCENT

◆ COST FUNCTION

◆ PROBLEMS OF GRADIENT DESCENT

◆ FORWARD PROPAGATION

◆ BACKWORD PROPAGATION

◆ HOW TO TRAIN NEURAL NETWORK

◆ HOW TO VALIDATE A NEURAL NETWORK

◆ CONCEPTS OF OVERFITTING AND UNDERFITTING

◆ ARTIFICIALNEURAL NETWORK

◆ CONVOLUTION NEUAL NETWORK

◆ RECORRUNT NEURAL NETWORK

◆ LSTM

◆ TRANSFER LEARNING INTRODUCTION

◆ DATA AUGMENTATION TECHNIQUES

◆ TIME SERIES ANALYSIS

◆ DESCRIBE TIME SERIES DATA

◆ DIFFERENT CONCEPTS OF TIME SERIES DATA

◆ IMPLEMENT MODEL FOR FORECASTING

◆ SEASONALITY TREND RESIDUAL

◆ STATIONARITY AND NON STATINARITY

◆ AUGMENTED DICKY FULLER TEST

◆ P-VALUE ANALYSIS

◆ DIFFERENCING AND INTEGRATING

◆ ARIMA MODEL

◆ SARIMA MODEL

◆ S P D Q VALUES

◆ AUTO CORRELATION PARTIAL AUTO CORRELATION PLOTS

◆ RECOMMENDATION SYSTEMS

◆ COLLABORATIVE FILTERING

1. MODEL BASED

B.MEMORY BASED

◆ CONTENT BASED FILTERING

◆ SIMILARITY BASED FILTERING

1. USER-USER FILTERING
2. ITEM-ITEM BASED FILTERING

◆ MATRIX FACTORIZATION

◆ HYBRID FILTERING

◆ COSINE SIMILARITY

◆ PERSONS CORRELATION

◆ INTRODUCTION

◆ TEXT NORMALIZATION,

◆ EDIT DISTANT

◆ LANGUAGE MODELLING WITH N GRAMS

◆ NAIVE BAYES CLASSIFICATION AND SENTIMENT(NLP + ML)

◆ LOGISTIC REGRESSION(NLP + ML)

◆ VECTOR SEMANTICS AND EMBEDDINGS

◆ NEURAL NETS AND NEURAL LANGUAGE MODELS(NLP + DL)

◆ PART-OF-SPEECH TAGGING

◆ SEQUENCE PROCESSING WITH RECURRENT NETWORKS

◆ ENCODER-DECODER MODELS, ATTENTION, AND CONTEXTUAL EMBEDDINGS

◆ CONSTITUENCY GRAMMARS

◆ CONSTITUENCY PARSING

◆ STATISTICAL CONSTITUENCY PARSING

◆ DEPENDENCY PARSING

◆ LOGICAL REPRESENTATIONS OF SENTENCE MEANING

◆ COMPUTATIONAL SEMANTICS AND SEMANTIC PARSING

◆ INFORMATION EXTRACTION

◆ WORD SENSES AND WORDNET

◆ SEMANTIC ROLE LABELING AND ARGUMENT STRUCTURE

◆ LEXICONS FOR SENTIMENT, AFFECT, AND CONNOTATION

◆ COREFERENCE RESOLUTION

◆ DISCOURSE COHERENCE

◆ SUMMARIZATION

◆ QUESTION ANSWERING

◆ DIALOG SYSTEMS AND CHATBOTS

◆ PHONETICS

◆ SPEECH PROCESSING

◆ HIDDEN MARKOV MODELS

◆ LATENT DIRICHLET ALLOCATION

◆ IMAGE ENHANCEMENT

◆ IMAGE DENOISING

◆ TRANSFORMATIONS

◆ FILTERING, FOURIER AND WAVELET TRANSFORMS AND IMAGE COMPRESSION

◆ COLOR VISION

◆ FEATURE EXTRACTION

◆ POSE ESTIMATION

◆ REGISTRATION