Artificial Intelligence Masters Program Training Course
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Ensure career success with this artificial intelligence course, in collaboration with IBM. featuring exclusive IBM hackathons, masterclasses, & ask me something sessions, this AI certification training helps you master key ideas as well as data science with Python, Machine Learning, Deep Learning, & NLP. Moreover, get job-ready AI training with live sessions, sensible labs, and comes. Upon completion of this AI certification training, you may receive certificates from IBM(for IBM-AI courses) for the courses in the learning path.
- 40+ Hrs Hands On Training
- 2 Live Projects For Hands-On Learning
- 50 Hrs Practical Assignments
- 24/7 Students
- Exclusive Hackathons and Live interaction with Certified expertIncludes live Master Classes and Ask me anything sessions
- 230+ hours of live interactive learningLive Online classes by industry experts
- Capstone and 14+ real-life projectsBuilt on Voice-based Virtual, Price Predicting and etc.
- LearnoVita Job Assist™Get noticed by the top hiring companies
- Course 1 Artificial Intelligence
- Course 2 Data Science with Python
- Course 3 Machine Learning
- Course 4 Deep Learning with Keras and TensorFlow
- Course 5 Advanced Deep Learning
- Course 6 Natural Language Processing (NLP)
- Course 7 AI capstone project
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Customized learning paths, 4x outcomes & completion rates; award-winning client support.
Online Classroom Batches Preferred
07-Oct-2024
Monday (Monday - Friday)
Weekdays Regular
08:00 AM (IST)
(Class 1Hr - 1:30Hrs) / Per Session
09-Oct-2024
Wednesday (Monday - Friday)
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05-Oct-2024
Saturday (Saturday - Sunday)
Weekend Regular
11:00 AM (IST)
(Class 3hr - 3:30Hrs) / Per Session
05-Oct-2024
Saturday (Saturday - Sunday)
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- Core JSP and Servlets
- J2EE , Struts, Spring
- Hibernate, JDBC, Web Services
- Advanced JSP and Servlets
- EJB, JDO, JSF
- Android Development
- Servlets
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Artificial Intelligence Masters Program Course Curriculam
Trainers Profile
Pre-requisites
- Working knowledge of Windows, Web sites and browsers & Client/server environment.
- There are no prerequisites to attend this course but knowledge of performance testing will help.
- Any software tester / developer, mobile application testers / developers and IT professionals can learn loadrunner performance testing.
Syllabus of Artificial Intelligence Masters Program Online Course Download syllabus
- What is Data Science?
- What is Machine Learning?
- What is Deep Learning?
- What is AI?
- Data Analytics & it’s types
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- What is Python?
- Why Python?
- Installing Python
- Python IDEs
- Jupyter Notebook Overview
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- Python Basic Data types
- Lists
- Slicing
- IF statements
- Loops
- Dictionaries
- Tuples
- Functions
- Array
- Selection by position & Labels
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- Pandas
- Numpy
- Sci-kit Learn
- Mat-plot library
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- Reading CSV files
- Saving in Python data
- Loading Python data objects
- Writing data to csv file
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- Selecting rows/observations
- Rounding Number
- Selecting columns/fields
- Merging data
- Data aggregation
- Data munging techniques
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- Central Tendency
- Probability Basics
- Standard Deviation
- Bias variance Trade off
- Distance metrics
- Outlier analysis
- Missing Value treatment
- Correlation
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- Classification
- Regression
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- Supervised Learning
- Linear Regression
- Logistic regression
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- K-Means
- K-Means ++
- Hierarchical Clustering
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- Support Vectors
- Hyperplanes
- 2-D Case
- Linear Hyperplane
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- Linear
- Radial
- polynomial
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- K – Nearest Neighbour
- Naïve Bayes Classifier
- Decision Tree – CART
- Decision Tree – C50
- Random Forest
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- Perceptron
- Multi-Layer perceptron
- Markov Decision Process
- Logical Agent & First Order Logic
- AL Applications
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- CNN – Convolutional Neural Network
- RNN – Recurrent Neural Network
- ANN – Artificial Neural Network
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- Text Pre-processing
- Noise Removal
- Lexicon Normalization
- Lemmatization
- Stemming
- Object Standardization
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- Syntactical Parsing
- Dependency Grammar
- Part of Speech Tagging
- Entity Parsing
- Named Entity Recognition
- Topic Modelling
- N-Grams
- TF – IDF
- Frequency / Density Features
- Word Embedding’s
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- Text Classification
- Text Matching
- Levenshtein Distance
- Phonetic Matching
- Flexible String Matching
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- What is Data Science?
- What is Machine Learning?
- What is Deep Learning?
- What is AI?
- Data Analytics & it’s types
Get detailed course syllabus in your inbox
- What is Python?
- Why Python?
- Installing Python
- Python IDEs
- Jupyter Notebook Overview
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- Python Basic Data types
- Lists
- Slicing
- IF statements
- Loops
- Dictionaries
- Tuples
- Functions
- Array
- Selection by position & Labels
Get detailed course syllabus in your inbox
- Pandas
- Numpy
- Sci-kit Learn
- Mat-plot library
Get detailed course syllabus in your inbox
- Reading CSV files
- Saving in Python data
- Loading Python data objects
- Writing data to csv file
Get detailed course syllabus in your inbox
- Selecting rows/observations
- Rounding Number
- Selecting columns/fields
- Merging data
- Data aggregation
- Data munging techniques
Get detailed course syllabus in your inbox
- Central Tendency
- Mean
- Median
- Mode
- Skewness
- Normal Distribution
- Probability Basics
- What does mean by probability?
- Types of Probability
- ODDS Ratio?
- Standard Deviation
- Data deviation & distribution
- Variance
- Bias variance Trade off
- Underfitting
- Overfitting
- Distance metrics
- Euclidean Distance
- Manhattan Distance
- Outlier analysis
- What is an Outlier?
- Inter Quartile Range
- Box & whisker plot
- Upper Whisker
- Lower Whisker
- catter plot
- Cook’s Distance
- Missing Value treatments
- What is a NA?
- Central Imputation
- KNN imputation
- Dummification
- Correlation
- Pearson correlation
- Positive & Negative correlation
- Error Metrics
- Classification
- Confusion Matrix
- Precision
- Recall
- Specificity
- F1 Score
- Regression
- MSE
- RMSE
- MAPE
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- Linear Regression
- Linear Equation
- Slope<
- Intercept
- R square value
- Logistic regression
- ODDS ratio
- Probability of success
- Probability of failure
- ROC curve
- Bias Variance Tradeoff
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- K-Means
- K-Means ++
- Hierarchical Clustering
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- K – Nearest Neighbour
- Naïve Bayes Classifier
- Decision Tree – CART
- Decision Tree – C50
- Random Forest
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- Business Analytics, Data, Information
- Understanding Business Analytics and R
- Compare R with other software in analytics
- Install R
- Perform basic operations in R using command line
- Learn the use of IDE R Studio
- Use the ‘R help’ feature in R
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- Variables in R
- Scalars
- Vectors
- Matrices
- List
- Data frames
- Using c, Cbind, Rbind, attach and detach functions in R
- Factors
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- Data sorting
- Find and remove duplicates record
- Cleaning data
- Recoding data
- Merging data
- Slicing of Data
- Merging Data
- Apply functions
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- Reading Data
- Writing Data
- Basic SQL queries in R
- Web Scraping
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- Box plot
- Histogram
- Pareto charts
- Pie graph
- Line chart
- Scatterplot
- Developing Graphs
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- Basics of Statistics
- Inferencial statistics
- Probability
- Hypothesis
- Standard deviation
- Outliers
- Correlation
- Linear & Logistic Regression
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- Introduction to Data Mining
- Understanding Machine Learning
- Supervised and Unsupervised Machine Learning Algorithms
- K- means clustering
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- Anova
- Sentiment Analysis
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- Decision Tree
- Concepts of Random Forest
- Working of Random Forest
- Features of Random Forest
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- 1. Introduction to Deep Learning
- 2. Introduction to Numpy
- 3. Introduction to Tensorflow and Keras
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- 1. Solution of Equations, row and column Interpretation
- 2. Vector Space Properties
- 3. Partial Derivative of Polynomial and Two conditions for Local Minima
- 4. Physical Interpretation of gradient (Direction of Maximum Change)
- 5. Matrix Vector Multiplication
- 6. EVD and interpretation of Eighen Vectors
- 7. Linear Independence and Rank of Matrix
- 8. Orthonormal Matrices, Projection Matrices, Vandemonde Matrix, Markov Matrix, Symmetric, Block Diagonal
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- 1. Intuition behind Linear Regression, classification
- 2. Grid Search
- 3. Gradient Descent
- 4. Training Pipeline
- 5. Metrics ROC Curve, Precision Recall Curve
- 6. Calculating Entropy
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- 1. Evolution of Perceptrons, Hebbs Principle, Cat Experiment
- 2. Single layer NN
- 3. Tensorflow Code
- 4. Multilayer NN
- 5. Back propagation, Dynamic Programming
- 6. Mathematical Take on NN
- 7. Function Approximator
- 8. Link with Linear Regression
- 9. Dropout and Activation
- 10. Optimizers and Loss Functions
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- 1. 1D and 2D Convolution
- 2. Why CNN for Images and speech?
- 3. Convolution Layer
- 4. Coding Convolution Layer
- 5. Learning Sharpening using single convolution Layer in Tensor-Flow
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- 1. Convolution
- 2. Pooling
- 3. Activation
- 4. Dropout
- 5. Batch Normalization
- 6.Object Classification
- 7. Creating Batch in Tensorflow and Normalize
- 8. Training MNIST and CIFAR datasets
- 9. Understanding a pre-trained Inception Architecture
- 10. Input Augmentation Techniques for Images >
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- 1. Finetuning last layers of CNN Model
- 2. Selecting appropriate Loss
- 3. Adding a new class in the last Layer
- 4. Making a model Fully Convolutional for Deployment
- 5. Finetune Imagenet for Cats vs Dog Classification.
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- 1. Different types of problem in Objects
- 2. Difficulties in Object Detection and Localization
- 3. Fast RCNN
- 4. Faster RCNN
- 5. YOLO v1-v3
- 6. SSD
- 7. MobileNet
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- 1. Image Compression Simple Autoencoder
- 2. Denoising Autoencoder
- 3. Variational Autoencoder and Reparematrization Trick
- 4. Robust Word Embedding using Variational Autoencoder
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- 1. Evolution of Recurrent Structures
- 2. LSTM, RNN, GRU, Bi-RNN, Time-Dense
- 3. Learning a Sine Wave using RNN in Tensorflow
- 4. Creating Autocomplete for Harry Potter in Tensorflow
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- 1. Generative vs Discrimative Models
- 2. Theory of GAN
- 3. Simple Distribution Generator in Tensorflow using MCMC (Markov Chain Monte Carlo)
- 4. DCGAN,WGANs for Images
- 5. InfoGANs, CycleGANs and Progressive GANs
- 6. Creating a GAN for generating Manga Art
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- 1. Model Free Prediction
- 2. Monte Carlo Prediction and TD Learning
- 3. Model Free Control with REINFORCE and SARSA Learning
- 4. Assignment : Implementation of REINFORCE and SARSA Learning in Gridworld
- 5. Off policy vs On Policy Learning
- 6. Importance Sampling for Off Policy Learning
- 7. Q Learning
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- 1. Understanding Deep Learning as Function Approximator
- 2. Theory of Behavioral Cloning and Deep Q Learning
- 3. Revisiting Point Collector Example in Unity and
- 4. Assignment : Training Cartpole Example via Deep Q Learning
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- 1. Face Detection using Yolo-v3
- 2. Building Autocomplete Feature using RNNs
- 3. Real-time Depth Prediction and Pose Estimation
- 4. How is Deep Learning used in Autonomous Driver Assistant systems
- 5. Tips and Tricks for scaling and easy Deployment of Deep Learning Models
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- What is Data Science?
- What is Machine Learning?
- What is Deep Learning?
- What is AI?
- Data Analytics & it’s types
Get detailed course syllabus in your inbox
- What is Python?
- Why Python?
- Installing Python
- Python IDEs
- Jupyter Notebook Overview
Get detailed course syllabus in your inbox
- Python Basic Data types
- Lists
- Slicing
- IF statements
- Loops
- Dictionaries
- Tuples
- Functions
- Array
- Selection by position & Labels
Get detailed course syllabus in your inbox
- Pandas
- Numpy
- Sci-kit Learn
- Mat-plot library
Get detailed course syllabus in your inbox
- Reading CSV files
- Saving in Python data
- Loading Python data objects
- Writing data to csv file
Get detailed course syllabus in your inbox
- Selecting rows/observations
- Rounding Number
- Selecting columns/fields
- Merging data
- Data aggregation
- Data munging techniques
Get detailed course syllabus in your inbox
- Central Tendency
- Mean
- Median
- Mode
- Skewness
- Normal Distribution
- Probability Basics
- What does mean by probability?
- Types of Probability
- ODDS Ratio?
- Standard Deviation
- Data deviation & distribution
- Variance
- Bias variance Trade off
- Underfitting
- Overfitting
- Distance metrics
- Euclidean Distance
- Manhattan Distance
- Outlier analysis
- What is an Outlier?
- Inter Quartile Range
- Box & whisker plot
- Upper Whisker
- Lower Whisker
- Scatter plot
- Cook’s Distance
- Missing Value treatment
- What is a NA?
- Central Imputation
- KNN imputation
- Dummification
- Correlation
- Pearson correlation
- Positive & Negative correlation
Get detailed course syllabus in your inbox
- Classification
- Confusion Matrix
- Precision
- Recall
- Specificity
- F1 Score
- Regression
- MSE
- RMSE
- MAPE
Get detailed course syllabus in your inbox
- Supervised Learning
- Linear Regression
- Linear Equation
- Slope
- Intercept
- R square value
- Logistic regression
- ODDS ratio
- Probability of success
- Probability of failure Bias Variance Tradeoff
- ROC curve
- Bias Variance Tradeoff
- Unsupervised Learning
- K-Means
- K-Means ++
- Hierarchical Clustering
- SVM
- Support Vectors
- Hyperplanes
- 2-D Case
- Linear Hyperplane
- SVM Kernal
- Linear
- Radial
- polynomial
- Other Machine Learning algorithms
- K – Nearest Neighbour
- Naïve Bayes Classifier
- Decision Tree – CART
- Decision Tree – C50
- Random Forest
Get detailed course syllabus in your inbox
- AI Introduction
- Perceptron
- Multi-Layer perceptron
- Markov Decision Process
- Logical Agent & First Order Logic
- AL Applications
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- CNN – Convolutional Neural Network
- RNN – Recurrent Neural Network
- ANN – Artificial Neural Network
- Introduction to NLP
- Text Pre-processing
- Noise Removal
- Lexicon Normalization
- Lemmatization
- Stemming
- Object Standardization
- Text to Features (Feature Engineering)
- Syntactical Parsing
- Dependency Grammar
- Part of Speech Tagging
- Entity Parsing
- Named Entity Recognition
- Topic Modelling
- N-Grams
- TF – IDF
- Frequency / Density Features
- Word Embedding’s
- Tasks of NLP
- Text Classification
- Text Matching
- Levenshtein Distance
- Phonetic Matching
- Flexible String Matching
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- Enhance report design
- Add report objects to enhance design
- Format data and report objects
- Add a background image to a report
- Add row numbers to a report
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- Create multi-lingual reports
- Highlight exceptional data
- Show and hide data
- Conditionally render objects in reports
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- Analysis Studio Fundamentals
- Nest Data in Crosstabs in Analysis Studio
- Create Analysis with Multiple filter
- Reusable analysis
- Build Advanced Crosstabs in Analysis Studio
- Focus with Filters in Analysis Studio
- Creating reports from cubes
- Drill down and drill up
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- Introduction to Event Studio
- Create an agent
- Add tasks to an agent
- Run an agent through its lifecycle
- Schedule an agent
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- Introdcution to Dashboards
- Create Dashboard
- Types of Filter-Value, Slider and advanced filter
- Overview of RSS Feed and web Page
- Content Pane
- Create Widgets
- Sort, Filter and Calculate data
- Hands on
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- Overview of Business Intelligence Advance level
- Create Different types of Reports
- Reporting Styles and filters
- Create dashboard objects
- Summarize data and Create Calculations
- Dispatcher and Services
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- All Services
- Properties of Services
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- 1. Introduction to Statistical Machine Translation
- 2. Introduction to neural models
- 3. Introduction to neural models for translation and conversation
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- 1. Introduction to Deep Semantic Similarity Model (DSSM) and its applications.
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- 1. Introduction to methods applied in Natural Language Understanding
- 2. Continuous word representations method
- 3. Neural knowledge base embedding method
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- 1. Introduction to deep reinforcement learning techniques applied in NLP
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- 1. Neural models applied in Image captioning
- 2. Neural models applied in visual question answering
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- Exploratory Data Analysis
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- Model Building and fitting
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- Unsupervised learning
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- Representing results
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Artificial Intelligence Masters Program Training Course Objectives
- Human Error Reduction.
- Takes threats rather than people.
- Recurrent Jobs Helping.
- Digital support.
- Faster decisions.
- Daily applications.
- New innovations
- Basic technology.
- A major interdisciplinary.
- Advanced Frontline Study.
- Collaboration abundant.
- Graduates with Great Things accomplish.
- More employment or career paths such as machine learning, data collection and analytical, software creation, program administration and testing can be created.
- It is anticipated that the effect of AI on sectors like hospitals, transportation and logistics and home maintenance will be important by 2025.
- As an AI candidate, you have plenty of jobs in this sector.
- Some AI job includes machine learning engineer, data scientist, business intelligence developer, research scientists, and AI engineer.
- Artificial intelligence engineer is one of the most prominent job roles in the AI industry today.
- Python also offers many data extraction applications that help manage the data better.
- For data scientists Python is relevant because it offers a wide range of uses in data science.
- It also offers greater versatility in computer learning and in-depth learning.
- Studying and transforming prototypes in computer technology.
- Machine learning frameworks and systems are designed and developed.
- Perform mathematical analyses of research data and fine-tune models.
- For training purposes to search available data sets online.
- Keras helps you to plan, match, test, and use profound learning models in just a few lines of code to anticipate.
- It allows deep learning tasks commonly available to ordinary developers who seek to do stuff, such as classification and regression predictive modelling
- The fundamental requirement of profound learning is programming.
- Without a programming language, you cannot think deeply.
- To become a deeply trained learner, it is essential to understand how and how these programming languages function.
- Computer Programming Languages like Python and R, Java.
- Strong Problem-Solving skills.
- Good Communication Skills.
- Text Clustering Skills.
- Statistical Analysis Skills.
- Machine Learning Concepts and Methods.
- Text Representation Techniques.
- Algorithm Analysis Skills.
- The capstone of the project was really successful, and it allowed me to clearly grasp the profound learning principles for the answer.
- Just since its final task is much more planned than PyTorch, Keras part of the course is more appealing.
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Just give us a CALL at +91 9383399991 OR email at contact@learnovita.com
Yes We Provide Lifetime Access for Student’s Portal Study Materials, Videos & Top MNC Interview Question After Once You Have Enrolled.
We at LearnoVita believe in giving individual attention to students so that they will be in a position to clarify all the doubts that arise in complex and difficult topics and Can Access more information and Richer Understanding through teacher and other students' body language and voice. Therefore, we restrict the size of each Android App Developer batch to 5 or 6 members
Learning Android App Developer can help open up many opportunities for your career. It is a GREAT SKILL-SET to have as many developer roles in the job market requires proficiency in Android App Developer. Mastering Android App Developer can help you get started with your career in IT. Companies like Oracle, IBM, Wipro, HP, HCL, DELL, Bosch, Capgemini, Accenture, Mphasis, Paypal, and MindLabs.
The Average Android App Developer Developer salary in India is ₹4,43,568 per annum.
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