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DeepNeuron online Full Stack Data Science program lets you gain proficiency in Data Science. You will work on real-world projects in Data Science with R, Python, Machine Learning, Dev, Admin, Test and Analysis, Apache Spark, Scala, Deep Learning, Data Science with SAS, SQL, MongoDB and more.
Probability & Statistics
Machine Learning
Programming
Creating a data model
Data Manipulation
Visualizing data
OOPS in Python
Pandas, NumPy, & Scikit-Learn
Time Series Forecasting
Python Integration with Spark
Python is an interpreted, object-oriented, high-level programming language with dynamic semantics. Python's simple, easy to learn syntax emphasizes readability and therefore reduces the cost of program maintenance. Python supports modules and packages, which encourages program modularity and code reuse.
Topics:
Overview of Python The Companies using Python/div>
Different Applications where Python is used Windows Discuss Python Scripts on UNIX/Windows Values, Types, Variables Operands and Expressions Lists, Ranges & Tuples in Python Conditional Statements Python Dictionaries and Sets Loops Command Line Arguments Writing to the screenHands On/Demo:
Creating “Hello World" code Variables Demonstrating Conditional Statements Demonstrating LoopsSkills:
Fundamentals of Python programmingLearning Objectives: In this Module, you will learn how to create generic python scripts, how to address errors/exceptions in code and finally how to extract/filter content using regex.
Topics:
Functions Function Parameters Global Variables Variable Scope and Returning Values Lambda Functions Object-Oriented Concepts/div>
Standard Libraries Modules Used in Python The Import Statements Slicing with Negative Numbers Module Search Path Package Installation Ways Errors and Exception Handling Handling Multiple ExceptionsHands On/Demo:
Functions - Syntax, Arguments, Keyword Arguments, Return Values Lambda - Features, Syntax, Options, compared with the Functions Sorting - Sequences, Dictionaries, Limitations of Sorting Errors and Exceptions - Types of Issues, Remediation Packages and Module - Modules, Import Options, sys PathSkills:
Error and Exception management in Python Working with functions in PythonLearning Objective: Through this Module, you will understand in detail about Data Manipulation
Topics:
Basic Functionalities of a data object Merging of Data objects Concatenation of data objects Types of Joins on data objects Exploring a Dataset Analysing a datasetHands On/Demo:
Pandas Function- Ndim(), axes(), values(), head(), tail(), sum(), std(), iteritems(), iterrows(),itertuples() GroupBy operations Aggregation Concatenation Merging Using and, or, in Conditions JoiningSkills:
Python in Data ManipulationLearning Objectives: In this module, you will learn the concept of Machine Learning and its types.
Topics:
Python Revision (numpy, Pandas, scikit learn, matplotlib) What is Machine Learning? Machine Learning Use-Cases Machine Learning Process Flow Machine Learning Categories Linear regression Gradient descentHands On/Demo:
Linear Regression - Boston DatasetSkills:
Machine Learning concepts Machine Learning types Linear Regression ImplementationLearning Objectives: In this module, you will learn Supervised Learning Techniques and their implementation, for example, Decision Trees, Random Forest Classifier etc.
Topics:
What are Classification and its use cases? What is Decision Tree? Algorithm for Decision Tree Induction Creating a Perfect Decision Tree
Confusion Matrix
What is Random Forest?Hands On/Demo:
Implementation of Logistic regression
Decision tree
Random forest
Skills:
Supervised Learning concepts
Implementing different types of Supervised Learning algorithms
Evaluating model output
Learning Objectives: In this module, you will learn about the impact of dimensions within data. You will be taught to perform factor analysis using PCA and compress dimensions. Also, you will be developing LDA model.
Topics:
Introduction to Dimensionality Why Dimensionality Reduction PCA Factor Analysis Scaling dimensional model LDA Hands-On/Demo:
PCA Scaling Skills:
Implementing Dimensionality Reduction Technique Learning Objectives: In this module, you will learn Supervised Learning Techniques and their implementation, for example, Decision Trees, Random Forest Classifier etc.
Topics:
What is Naïve Bayes? How Naïve Bayes works? Implementing Naïve Bayes Classifier What is Support Vector Machine? Illustrate how Support Vector Machine works? Hyperparameter Optimization Grid Search vs Random Search Implementation of Support Vector Machine for Classification Hands-On/Demo:
Implementation of Naïve Bayes, SVM
Skills:
Supervised Learning concepts Implementing different types of Supervised Learning algorithms Evaluating model output
Learning Objectives: In this module, you will learn about Unsupervised Learning and the various types of clustering that can be used to analyze the data.
Topics:
What is Clustering & its Use Cases? What is K-means Clustering? How does K-means algorithm work?How to do optimal clustering What is C-means Clustering? What is Hierarchical Clustering? How Hierarchical Clustering works?Hands-On/Demo:
Implementing K-means Clustering Implementing Hierarchical Clustering
Skills:
Unsupervised Learning Implementation of Clustering - various types Learning Objectives: In this module, you will learn Association rules and their extension towards recommendation engines with Apriori algorithm.
Topics:
What are Association Rules? Association Rule Parameters Calculating Association Rule Parameters Recommendation Engines How does Recommendation Engines work? Collaborative Filtering Content-Based Filtering Hands-On/Demo:
Apriori Algorithm Market Basket Analysis Skills:
Data Mining using python Recommender Systems using python
Introduction to end to end learning Abstractions of features using deep layers Hyper parameter tuning Regularization for Deep Learning Dropout TensorFlow Installation Tensorflow Installation 2.0 Tensorflow Installation 1.6 with virtual environment TensorFlow 2.0 function Tensorflow 2.0 neural network creation Tensorflow 1.6 functions Tensorflow 1.6 neural network and its functions Keras Introduction Keras in-depth with neural network creation Mini project in Tensorflow Introduction to NLP Stop Words o Tokenization Stemming and lemmatization Bag of Words Model Word Vectorizer TF-IDF POS Tagging Named Entity Recognition Introduction to Sequential data RNNs and its mechanisms Vanishing & Exploding gradients
in RNNS LSTMs - Long short-term memory GRUS - Gated recurrent unit LSTMs Applications o Time series analysis LSTMs with attention mechanismLearning Objectives: In this module, you will learn about Time Series Analysis to forecast dependent variables based on time. You will be taught different models for time series modeling such that you analyze a real time-dependent data for forecasting.
Topics:
What is Time Series Analysis? Importance of TSA Components of TSA White Noise AR model MA model ARMA model ARIMA model Stationarity ACF & PACF Hands on/Demo:
Checking Stationarity Converting a non-stationary data to stationary Implementing Dickey-Fuller Test Plot ACF and PACE Generating the ARIMA plot TSA Forecasting Skills:
TSA in Python Learning Objectives: In this module, you will learn about selecting one model over another. Also, you will learn about Boosting and its importance in Machine Learning. You will learn on how to convert weaker algorithms into stronger ones.
Topics:
What is Model Selection? The need for Model Selection Cross-Validation What is Boosting? How Boosting Algorithms work? Types of Boosting Algorithms Adaptive Boosting Hands on/Demo:
Cross-Validation AdaBoost Skills:
Model Selection Boosting algorithm using python
Learning Objectives: Learn different types of sequence structures, related operations and their usage. Also learn diverse ways of opening, reading, and writing to files.
Topics:
Python files I/O Functions Numbers Strings and related operations Tuples and related operations Lists and related operations Dictionaries and related operations Sets and related operations Hands On/Demo:
Tuple - properties, related operations, compared with a list List - properties, related operations
Dictionary - properties, related operations Set - properties, related operations
Skills:
File Operations using Python Working with data types of Python
Learning Objectives: This Module helps you get familiar with basics of statistics, different types of measures and probability distributions, and the supporting libraries in Python that assist in these operations. Also, you will learn in detail about data visualization.
Topics:
Numpy - arrays Operations on arrays Indexing slicing and iterating Reading and writing arrays on files Pandas - data structures & index operations Reading and Writing data from Excel/CSV formats into Pandas matplotlib library Grids, axes, plots Markers, colours, fonts and styling Types of plots - bar graphs, pie charts, histograms Contour plots Hands On/Demo:
Numpy library-Creating Numpy array, operations performed on Numpy array Pandas library-Creating series and dataframes, Importing and exporting data Matplotlib - Using Scatterplot, histogram, bar graph, pie chart to show information, Styling of PlotSkills:
Probability Distributions in Python Python for Data Visualization Our Python program is exhaustive and this certificate is proof that you have taken a big leap in mastering the domain.
The knowledge and Python skills you've gained working on projects, simulations, case studies will set you ahead of the competition.
Talk about it on Linkedin, Twitter, Facebook, boost your resume, or frame it - tell your friends and colleagues about it.
-Anand Shyam,Data Scientist
-Y.Naidu,Project Manager
-Bhuvanesh,Senior Data Scientist
-Boobathy,Project Manager
-Shiva Project Manager, CTS
-ThulasiDass,CSC ,Chennai
-Babu Lakshman -Cognizant
-Vijay , HCLThe objective of the CKA training is to provide extensive coverage on the design and deployment of containers and orchestral tools for applications with special references to Python container orchestration tools. With Deepneuron Certified Python Administrator course, we ensure hands-on practical experience on creating containers files for Python which will help you to master Python with confidence.
Deepneuron Python certification is for freshers, experienced individuals who are ready to dive for a real-time case study based industry-specific carriculum specialized curated from professional subject matter experts (SMEs). At Deepneuron, you will explore live projects based on conceptual learnings with details explanations for each and every module for upscaling your current career statistics with advancement.
After the completion of the Python online course, you will gain expert knowledge in Kubernetes and will be a proficient player to tap Pythontools at a depth level. Moreover, you will be part of the Deepneuron community to leverage the knowledge-base shared by the members around the globe. You will also earn an industry-recognized Python certification from Deepneuron.
Deepneuron actively provides placement assistance to all learners who have successfully completed the training. For this, we are exclusively tied-up with over 80 top MNCs from around the world. This way, you can be placed in outstanding organizations such as Sony, Ericsson, TCS, Mu Sigma, Standard Chartered, Cognizant, and Cisco, among other equally great enterprises. We also help you with the job interview and résumé preparation as well.
You can definitely make the switch from self-paced training to online instructor-led training by simply paying the extra amount. You can join the very next batch, which will be duly notified to you.
Apparently, no. Our job assistance program is aimed at helping you land in your dream job. It offers a potential opportunity for you to explore various competitive openings in the corporate world and find a well-paid job, matching your profile. The final decision on hiring will always be based on your performance in the interview and the requirements of the recruiter.
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