Data Science Online Training in USA

Data Science Online Training in USA

Data Science Online Training in USA

AiMLAnalytics is the best training platform in USA providing Online Data Science Training classes by real-time faculty with course material, industry test cases and 24x7 Lab Facility.

AiML Analytics is the best platform to learn new and emerging technologies with hands-on experience in online mode which is located in USA. Best online courses at best prices compared to other institutions in USA. Best training classes in Machine Learning, Data Science, Python, and Artificial Intelligence.

ABOUT AiML Analytics

  • AiMLAnalytics is well recognized training institute for python, data science, machine learning and deep learning.
  • Our vision is to design and deliver a quality online Industry training in future technologies.
  • Our courses matches the requirements of both freshers as well as working professionals.
  • Our faculties are well experienced and worked as Data Scientists, Machine Learning and Deep Learning Engineers at reputed companies.

Faculty

  • Our trainers supports in technology, practical hands-on sessions which enables students to learn new technologies easily.
  • Trainers have more than 8+ years of experience in Data Science and Machine Learning.
  • These training courses are designed to complete a gap between students and technologies which will be helpful.

Who should attend?

  • It is open for all the working professionals, freshers, students, lecturers etc., apart from their studies which is a best source to learn the new era of emerging technologies through online courses and develop their skills in it with practical knowledge also.

Why choose AiML Analytics?

  • It is the best platform to learn different emerging technologies with our well experienced trainers. Our trainers will not only meet the standards in theoretical way but also practically with one-on-one attention and interacting with freshers.

Course Content

Python Core

  • Introduction of python and comparison with other programming languages
  • Installation of Anaconda Distribution and another python IDE
  • Python Objects, Number & Booleans, Strings, Container objects, Mutability of objects
  • Operators – Arithmetic, Bitwise, comparison and Assignment operators, Operators Precedence and associativity.
  • Conditions (If else, if-elif-else)
  • Loops(While ,for)
  • Break and Continue statements
  • Range functions

String Objects and Collections

  • String object basics
  • String methods
  • Splitting and Joining strings
  • String format functions
  • List object basics
  • List methods
  • List as Stack and Queues
  • List comprehensions

Tuples, Set, Dictionaries and Functions

  • Tuples, Sets, Dictionary object basics, Dictionary.
  • Object Methods, Dictionary View Objects, Functions basics, Parameter passing, Iterators.
  • Generator Functions
  • Lambda functions
  • Map, Reduce, Filter functions

OOPS concepts and working with files

  • Creating classes and Objects
  • Inheritance, Multiple Inheritance
  • Working with files
  • Reading and writing files
  • Buffered read and write.
  • Other file methods

Modules, Exception Handling and Database Programming

  • Using standard module
  • Creating new modules
  • Exceptions Handling with Try-except
  • Creating, inserting and retrieving table
  • Updating and deleting the  data

 

Python Projects

  • Number Guessing
  • Hangman
  • Python Story Generator

 

Database

  • Mongo DB
  • SQL

GitHub

  • Account creating
  • Pushing Projects
  • Pulling Projects
  • ReadMe File

Python pandas

  • Python Pandas – Series
  • Python Pandas – DataFrame
  • Python Pandas – Panel
  • Calculator
  • Tic-Tac-Toe
  • Plagiarism Checker

Visualization

  • Matplotlib
  • Seaborn
  • Plotly

Rest API

  • Flask introduction
  • Flask Application
  • Open link Flask
  • App Route Flask
  • URL Building Flask
  • HTTP Methods Flask
  • Templates Flask
  • Request Object
  • Python Pandas – Basic Functionality
  • Descriptive Statistics
  • Function Application
  • Python Pandas – Reindexing
  • Python Pandas – Iteration
  • Python Pandas – Sorting
  • Working with Text Data
  • Options & Customization
  • Indexing & selecting data
  • Statistical Functions
  • Python Pandas – Window Functions
  • Python Pandas – Date Functionality
  • Python Pandas – Time delta
  • Python Pandas – Categorical Data
  • Python Pandas – Visualization
  • Python Pandas – IO Tools

Python NumPy

  • NumPy – ND array Object
  • NumPy – Data Types
  • NumPy – Array Attributes
  • NumPy – Array Creation Routines
  • NumPy – Array from Existing Data
  • Array from Numerical Ranges
  • NumPy – Indexing & Slicing
  • NumPy – Advanced Indexing
  • NumPy – Broadcasting
  • NumPy – Iterating Over Array
  • NumPy – Array Manipulation
  • Numpy – Binary Operators
  • NumPy – String Functions
  • NumPy – Mathematical Functions
  • NumPy – Arithmetic Operations
  • NumPy – Statistical Functions
  • Sort, Search & Counting Functions
  • NumPy – Byte Swapping
  • NumPy – Copies & Views
  • NumPy – Matrix Library
  • NumPy – Linear Algebra

Exploratory Data Analysis

  • Feature_Engineering_and_Se lection
  • Building_Tuning_and_Deplo ying_Models
  • Analyzing_Bike_Sharing_Tr ends
  • Analyzing_Movie_Reviews_S entiment
  • Customer_Segmentation_and_Effective_Cross_Selling
  • Analyzing_Wine_Types_and_Quality
  • Analysing_Music_Trends_and_Recommendations
  • Forecasting_Stock_and_Commodity _Prices

 

Statistics

  • Descriptive Statistics
  • Sample vs Population statistics
  • Random Variables
  • Probability distribution function
  • Expected value
  • Binomial Distribution
  • Normal Distributions
  • Z-score
  • Central limit Theorem
  • Hypothesis testing
  • Z-Stats vs T-stats
  • Type 1 type 2 error
  • Confidence interval
  • Chi-Square test
  • ANOVA test
  • F-stats

Machine Learning 1

  • Introduction
  • Supervised, Unsupervised, Semi-supervised, Reinforcement
  • Train, Test, Validation Split
  • Performance
  • Overfitting ,underfitting
  • OLS
  • Linear Regression
  • Assumptions
  • R square adjusted R square
  • Intro to Scikit learn
  • Training methodology
  • Hands on linear regression
  • Ridge Regression
  • Logistics regression
  • Precision Recall
  • ROC curve
  • F-Score

Machine Learning 2

  • Decision Tree
  • Cross Validation
  • Bias vs Variance
  • Ensemble approach
  • Bagging Boosting
  • Random Forest
  • Variable Importance

Machine Learning 3

  • XGBoost
  • Hands on XgBoost
  • K Nearest Neighbour
  • Lazy learners
  • Curse of dimensionality
  • KNN Issues
  • Hierarchical clustering
  • K-Means
  • Performance measurement
  • Principal Component analysis
  • Dimensionality reduction
  • Factor Analysis

Machine Learning 4

  • SVR
  • SVM
  • Polynomial
  • Regression
  • Ada boost
  • Gradient boost
  • Gaussian mixture
  • Anamoly detection
  • Novelty detection algorithm
  • Stacking
  • KNN Regression
  • Decision tree Regression
  • DBSCAN

Machine Learning Projects

  • Stock Price Prediction using  Machine Learning
  • Housing Prices Prediction  Project
  • Wine Quality test project
  • Mall Customers Clustering  Analysis

 

Natural Language Processing

  • Text Analytics
  • Tokenizing, Chunking
  • Document term Matrix
  • TF and IDF
  • Sentiment analysis hands on

 

Deep Learning 1

  • Basic of Neural Network
  • Type of NN
  • Cost Function
  • Gradient descent
  • Linear Algebra basics
  • Tensor flow In depth
  • Hands on Simple NN with Tensor flow
  • Word Embedding
  • CBOW, Skip-gram
  • Word Relations
  • Hands on word2vec

Deep Learning 2

  • Convolutional Neural Network
  • Maxpool, Window padding
  • Hands on
  • Image classification using Convolutional Neural Network
  • Recurrent Neural Network
  • Long Short Term Memory (LSTM) architecture
  • Sentiment Analysis Hands on
  • Hands on embedding + RNN
  • Seq-to-Seq model
  • Hands on translation
  • Encoder Decoder
  • Hands on cleaning images

Deep Learning 3

  • GAN
  • BERT
  • CNN Architectures
  • LeNet-5
  • AlexNet
  • GoogLeNet
  • VGGNet
  • ResNet
  • SSD
  • SSD lite
  • Faster R CNN

Deep Learning 4

  • Implementing a ResNet-34 CNN using Keras
  • Using Pretrained Models From Keras
  • Pretrained Models for Transfer Learning
  • Classification and Localization
  • Tensor flow Object Detection
  • YOLO Object Detection

Deep Learning Projects

  • Road Lane line detection – Computer Vision Project in Python
  • text to speech and creating small chatbots
  • Face Detection
  • YOLO Object Detection
  • Flower detection with Pretrained models

 

 

AiMlAnalytics  Providing The Following Services:

•Soft Copy Material

•Access To Training videos

•Implementation Oriented Training

•Interview / Proxy Support

•Job Support

•Resume Preparation Help

•Experienced & Certified Trainers

•Interview Questions& Answers (or) FAQs

•Mock Interviews

•24x7 Online Technical Support

•Online Training For Week End And Regular Batches

For More Details

https://aimlanalytics.com/