Online Classroom Batches Preferred
(Class 1Hr - 1:30Hrs) / Per Session
(Class 1Hr - 1:30Hrs) / Per Session
(Class 3hr - 3:30Hrs) / Per Session
(Class 4:30Hr - 5:00Hrs) / Per Session
No Interest Financing start at ₹ 5000 / month
Android App Developer Online Training Overview
To become a qualified Machine Learning Programmer, we deliver a rigorous Master's program in Machine Learning. You will learn various aspects of AI such as Machine Learning with Python, Deep Learning with TensorFlow, Artificial Neural Networks, Statistics, Data Science, SAS Advanced Analytics, Tableau Business Intelligence, Python and R programming, Build Chatbots with Watson Assistant, and MS Excel via hands-on projects as part of this AI Engineer course co-created with IBM. Additionally, you will have access to IBM Cloud Platforms such as Cognitive Classes and IBM Watson Cloud Lab.
Android App Developer Training will:
- In addition, you will obtain the following: Access to an IBM Cloud Lite Account and an AI Master's Certificate recognised by the industry
- You'll learn how to use Python libraries such as NumPy, SciPy, and Scikit, as well as important Machine Learning techniques.
- It gives you a thorough understanding of AI principles, including the fundamentals of statistics required for Data Science, Python programming, and Machine Learning.
- Learn how to use a Jupyter notebook to write your own Python scripts and perform simple hands-on data analysis.
- Learn about the data wrangling, data discovery, data visualisation, hypothesis creation, and testing processes of Data Science.
Skills You Will Gain
- Core JSP and Servlets
- J2EE , Struts, Spring
- Hibernate, JDBC, Web Services
- Advanced JSP and Servlets
- EJB, JDO, JSF
- Android Development
Android App Developer Course Key Features 100% Money Back Guarantee
5 Weeks TrainingFor Become a Expert
Certificate of TrainingFrom Industry Android App Developer Experts
Beginner FriendlyNo Prior Knowledge Required
Build 3+ ProjectsFor Hands-on Practices
Lifetime AccessTo Self-placed Learning
Placement AssistanceTo Build Your Career
Top Companies Placement
Annual SalaryHiring Companies
Machine Learning Masters Program Course Curriculam
- 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 Machine Learning Masters Program Online Course Download syllabus
- What can Python do?
- Why Python?
- Good to know
- Python Syntax compared to other programming languages
- Python Install
- The print statement
- Python Data Structures & Data Types
- String Operations in Python
- Simple Input & Output
- Simple Output Formatting
- Operators in python
- The If statement and its’ related statement
- An example with if and it’s related statement
- The while loop
- The for loop
- The range statement
- Break &Continue
- Examples for looping
- Create your own functions
- Functions Parameters
- Variable Arguments
- Scope of a Function
- Function Documentations
- Lambda Functions& map
- n Exercise with functions
- Create a Module
- Standard Modules
- Exception handling with try
- handling Multiple Exceptions
- Writing your own Exception
- File handling Modes
- Reading Files
- Writing& Appending to Files
- Handling File Exceptions
- The with statement
- New Style Classes
- Creating Classes
- Instance Methods
- Exception Classes & Custom Exceptions
- The Functions any and all
- With Statement
- Data Compression
- List Comprehensions
- Nested List Comprehensions
- Dictionary Comprehensions
- Default Parameters
- Variable Arguments
- Specialized Sorts
- Components and Events
- An Example GUI
- The root Component
- Adding a Button
- Entry Widgets
- Text Widgets
- Check buttons
- DB Connection
- Creating DB Table
- INSERT, READ, UPDATE, DELETE operations
- COMMIT & ROLLBACK operation
- handling Errors
- A Daytime Server
- Clients and Servers
- The Client Program
- The Server Program
- Program execution time
- more methods on date/time
- Frozen set
- Working with special charLearnoVitars, date, emails
- Match and find all
- charLearnoVitar sequence and substitute
- Search method
- Class and threads
- Treads Life cycle
- use cases
- Facebook Messenger
- Define Data Science
- Discuss the era of Data Science
- Describe the Role of a Data Scientist
- Illustrate the Life cycle of Data Science
- List the Tools used in Data Science
- State what role Big Data and Hadoop, Python, R and Machine Learning play in Data Science
- Data Analysis Pipeline
- What is Data Extraction
- Types of Data
- Raw and Processed Data
- Data Wrangling
- Exploratory Data Analysis
- Visualization of Data
- 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 descent
- What is 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?
- Introduction to Dimensionality
- Why Dimensionality Reduction
- Factor Analysis
- Scaling dimensional model
- 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
- What is Clustering & its Use Cases?
- What is K-means Clustering?
- How K-means algorithm works?
- How to do optimal clustering
- What is C-means Clustering?
- What is Hierarchical Clustering?
- How Hierarchical Clustering works?
- What are Association Rules?
- Association Rule Parameters
- Calculating Association Rule Parameters
- Recommendation Engines
- How Recommendation Engines work?
- Collaborative Filtering
- Content Based Filtering
- What is Reinforcement Learning
- Why Reinforcement Learning
- Elements of Reinforcement Learning
- Exploration vs Exploitation dilemma
- Epsilon Greedy Algorithm
- Markov Decision Process (MDP)
- Q values and V values
- Q – Learning
- α values >
- What is Time Series Analysis?
- Importance of TSA
- Components of TSA
- White Noise
- AR model
- MA model
- ARMA model
- ARIMA model
- ACF & PACF
- What is Model Selection?
- Need of Model Selection
- Cross – Validation
- What is Boosting?
- How Boosting Algorithms work?
- Types of Boosting Algorithms
- Adaptive Boosting
- Why do we need Graphical Models?
- Introduction to Graphical Model
- How does Graphical Model help you deal with uncertainty and complexity?
- Types of Graphical Models
- Graphical Modes
- Components of Graphical Model
- Representation of Graphical Models
- Inference in Graphical Models
- Learning Graphical Models
- Decision theory
- What is Bayesian Network?
- Advantages of Bayesian Network for data analysis
- Bayesian Network in Python Examples
- Independencies in Bayesian Networks
- Criteria for Model Selection
- Building a Bayesian Network
- Complexity in Inference
- Exact Inference
- Approximate Inference
- Monte Carlo Algorithm
- Gibb’s Sampling
- Inference in Bayesian Networks
- General Ideas in Learning
- Parameter Learning
- Learning with Approximate Inference
- Structure Learning
- Model Learning: Parameter Estimation in Bayesian Networks
- Model Learning: Parameter Estimation in Markov Networks
For the first week of this course, you will learn how to understand the exploration-exploitation trade-off in sequential decision-making, implement incremental algorithms for estimating action-values, and compare the strengths and weaknesses to different algorithms for exploration. For this week’s graded assessment, you will implement and test an epsilon-greedy agent.
When you’re presented with a problem in industry, the first and most important step is to translate that problem into a Markov Decision Process (MDP). The quality of your solution depends heavily on how well you do this translation. This week, you will learn the definition of MDPs, you will understand goal-directed behavior and how this can be obtained from maximizing scalar rewards, and you will also understand the difference between episodic and continuing tasks. For this week’s graded assessment, you will create three example tasks of your own that fit into the MDP framework.
Once the problem is formulated as an MDP, finding the optimal policy is more efficient when using value functions. This week, you will learn the definition of policies and value functions, as well as Bellman equations, which is the key technology that all of our algorithms will use.
This week, you will learn how to compute value functions and optimal policies, assuming you have the MDP model. You will implement dynamic programming to compute value functions and optimal policies and understand the utility of dynamic programming for industrial applications and problems. Further, you will learn about Generalized Policy Iteration as a common template for constructing algorithms that maximize reward. For this week’s graded assessment, you will implement an efficient dynamic programming agent in a simulated industrial control problem.
- Overview of Text Mining
- Need of Text Mining
- Natural Language Processing (NLP) in Text Mining
- Applications of Text Mining
- OS Module
- Reading, Writing to text and word files
- Setting the NLTK Environment
- Accessing the NLTK Corpora
- Frequency Distribution
- Different Types of Tokenizers
- Bigrams, Trigrams & Ngrams
- POS Tagging
- Named Entity Recognition
- Syntax Trees
- Context Free Grammars (CFG)
- Automating Text Paraphrasing
- Machine Learning: Brush Up
- Bag of Words
- Count Vectorizer
- Term Frequency (TF)
- Inverse Document Frequency (IDF)
- Converting text to features and labels
- Multinomial Naive Bayes Classifier
- Leveraging Confusion Matrix
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Machine Learning Masters Program Training Course Objectives
- On the off chance that you are hoping to take your vacation to another level, Machine Learning can do that for you. This is certain shot proof that even a slight improvement in ML calculations is monstrously beneficial for the organizations that utilization them, and hence, so are individuals behind them.
- AI is a strategy for information investigation that computerizes scientific model structure. It is a piece of artificial consciousness dependent on the possibility that frameworks can gain from information, distinguish examples and settle on choices with negligible human intercession.
- Notwithstanding, AI stays a moderately 'difficult' issue. There is no uncertainty the study of propelling AI calculations through research is troublesome. It requires innovativeness, experimentation, and perseverance. The trouble is that AI is on a very basic level of hard troubleshooting issues.
- Do I have to know to program for Machine learning? More or less, Yes. On the off chance that you need a vocation in Machine picking up, having some type of programming information truly makes a difference. As referenced before in this article, learning a programming language can truly assist you with carrying out ML calculations.
- The AI business is expected to expand from USD 1.03 Billion every 2016 to USD 8.81 Billion by 2022, at a Compound Annual Growth Rate (CAGR) of 44.1% during the conjecture time frame.
- AI courses differ in a period from a half year to a year and a half. Nonetheless, the educational plan differs with the kind of degree or accreditation you select. You remain to acquire adequate information on AI through half-year courses which could give you admittance to passage level situations at top firms.
- In case you're hoping to get into fields, for example, regular language handling, PC vision, or AI-related mechanical technology then it would be best for you to learn AI-first. AI is the place where you get PCs to gain from information and to have the option to make forecasts from that information without being expressly advised how to do as such.
- The base qualification that is required is a Bachelor's certificate with at least 1 year of work insight. Or then repeat a level in Mathematics or Statistics. To see more data, click here to look at the Machine learning program.
- Preparing is the main piece of Machine Learning. Pick your highlights and hyper boundaries cautiously. Machines don't make choices, individuals do. Information cleaning is the main piece of Machine Learning.
- It is viewed as a lower-level language than most normal AI dialects, in this manner it is simpler to peruse for the machine. That makes it reasonable to convey equipment level highlights like OS or comparable.
Exam & Certification
- Participate and Complete One batch of Android App Developer Training Course
- Successful completion and evaluation of any one of the given projects
- Complete 85% of the Android App Developer Certification course
- Successful completion and evaluation of any one of the given projects
- Oracle Certified Associate (OCA)
- Oracle Certified Professional (OCP)
- Oracle Certified Expert (OCE)
- Oracle Certified Master (OCM)
- Learn About the Certification Paths.
- Write Code Daily This will help you develop Coding Reading and Writing ability.
- Refer and Read Recommended Books Depending on Which Exam you are Going to Take up.
- Join LearnoVita Online Training Course That Gives you a High Chance to interact with your Subject Expert Instructors and fellow Aspirants Preparing for Certifications.
- Solve Sample Tests that would help you to Increase the Speed needed for attempting the exam and also helps for Agile Thinking.
Pranav SrinivasSoftware Testing, Capgemini
Android App Developer Course FAQ's
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- On Successfully Completing a Career Course with LearnoVita, you Could be Eligible for Job Placement Assistance.
- 100% Placement Assistance* - We have strong relationship with over 650+ Top MNCs, When a student completes his/ her course successfully, LearnoVita Placement Cell helps him/ her interview with Major Companies like Oracle, HP, Wipro, Accenture, Google, IBM, Tech Mahindra, Amazon, CTS, TCS, HCL, Infosys, MindTree and MPhasis etc...
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- More than 5400+ students placed in last year in India & Globally.
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- 85% percent placement record
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- Please Visit Your Student's Portal | Here FREE Lifetime Online Student Portal help you to access the Job Openings, Study Materials, Videos, Recorded Section & Top MNC interview Questions
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- LearnoVita is the unique Authorized Oracle Partner, Authorized Microsoft Partner, Authorized Pearson Vue Exam Center, Authorized PSI Exam Center, Authorized Partner Of AWS and National Institute of Education (nie) Singapore
- Also, LearnoVita Technical Experts Help's People Who Want to Clear the National Authorized Certificate in Specialized IT Domain.
- LearnoVita is offering you the most updated, relevant, and high-value real-world projects as part of the training program.
- All training comes with multiple projects that thoroughly test your skills, learning, and practical knowledge, making you completely industry-ready.
- You will work on highly exciting projects in the domains of high technology, ecommerce, marketing, sales, networking, banking, insurance, etc.
- After completing the projects successfully, your skills will be equal to 6 months of rigorous industry experience.
- We will reschedule the classes as per your convenience within the stipulated course duration with all such possibilities.
- View the class presentation and recordings that are available for online viewing.
- You can attend the missed session, in any other live batch.