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About AimAxis:

AimAxis is a leading e-learning platform providing live instructor-led interactive online training. We cater to professionals and students across the globe in categories like Data Science, Artificial Intelligence (AI), R Language, Python, SEO, Digital Marketing, Photoshop, Google Cloud Platform (GCP), Amazon Web Services (AWS), DevOps, SAP-BO-XIR 4.1/R4.2, Data Warehousing, IBM Cognos BI, Informatica Power Center 9.6.1/10.1, OBIEE, MSBI, Power BI, Tableau, Big Data, Hadoop, Business Analytics, Mobile Technologies, System Engineering, Project Management, and Programming. We have an easy and affordable learning solution that is accessible to all of the learners. With our students spread across countries like the US, India, UK, Canada, Singapore, Australia, Middle East, Brazil, and many others, we have built a community of overall learners across the globe.

Data Scientist Masters Program

Introduction to Python
  • Python Market Trends
  • Python Applications
  • Python Installation
  • Python IDE & Interpreter
  • Indentation, Comments and Literals
  • Python Code Execution
  • Hello World Program Using Python
  • Identifiers & Variables
  • Data Types
  • Strings
  • Tuples
  • Lists
  • Dictionaries
  • Sets
Python Operators, Sequences, File Operations
  • Conditional Statements
  • Introduction to Loops in Python
  • For Loop
  • Nested Loops
  • While Loop
  • Control Statements
  • Command Line Parameters
  • Sequences and File Operations
Functions & Object-Oriented Concepts
  • Use Case
  • Classes and Objects
  • Constructor and Destructor
  • Key Concepts of Object-Oriented Programming
  • Abstraction,
  • Encapsulation
  • Inheritance
  • Polymorphism
  • Types of Inheritance
  • Single Inheritance
  • Multiple Inheritance
  • Working with Modules and Handling Exceptions
Numpy, Pandas & Matplotlib Data Visualization
  • NumpyIntroduction and various methods
  • What is Pandas?
  • Installing Pandas
  • Pandas Data Structure Types
  • What is dataframe?
  • cleaning up messy data in csv/excel files
  • fillna, interpolate, dropna, replace methods
  • Group By (Split Apply Combine)
  • Concat Dataframes, Merge Dataframes
  • Pivot table
  • melt function, Stack & Unstack functions
  • Crosstab Tab
  • Time Series Analysis: DatetimeIndex and Resample, date_range, Holidays, to_datetime, Period and PeriodIndex, Timezone Handling, Shifting and Lagging,
  • Import & Export to excel file
  • Import From SQL & Export to SQL
  • Matplotlib data Visualization
  • Seaborn data Visualization
R Programming
  • Data Science concepts of R and functioning of R Calculator
  • Various functions like Stack, Merge and Strsplit
  • Creating pie charts, plots and vectors
  • Assigning value to variables and generating repeat and factor levels
  • Performing sorting, analyze variance and the cluster
  • ODBC tables reading and linear and logistic regression
  • Database connectivity
  • Deploying R programming for Hadoop applications
Statistics
  • What is Statistics
  • Descriptive Statistics
  • Central Tendency Measures
  • The Story of Average
  • Dispersion Measures
  • Data Distributions
  • Central Limit Theorem
  • What is Sampling
  • Why Sampling
  • Sampling Methods
  • Inferential Statistics
  • What is Hypothesis testing
  • Confidence Level
  • Degrees of freedom
  • what is pValue
  • Chi-Square test
  • What is ANOVA
  • Correlation vs Regression
  • Uses of Correlation & Regression 
Machine Learning, Deep Learning & AI using Python
Machine Learning Introduction
  • ML Fundamentals
  • ML Common Use Cases
  • Understanding Supervised and Unsupervised Learning Techniques
Implementing Association rule mining
  • What is Association Rules & its use cases?
  • What is Recommendation Engine & it’s working?
  • Recommendation Use-case
  • Case study
Supervised Learning Techniques
Linear Regression
  • Case study
  • Introduction to Predictive Modeling
  • Linear Regression Overview
  • Simple Linear Regression
  • Multiple Linear Regression
Logistic Regression
  • Case study
  • Logistic Regression Overview
  • Data Partitioning
  • Univariate Analysis
  • Bivariate Analysis
  • Multicollinearity Analysis
  • Model Building
  • Model Validation
  • Model Performance Assessment AUC & ROC curves
  • Scorecard
Decision Tree Classifier
  • How to build Decision trees
  • What is Classification and its use cases?
  • What is Decision Tree?
  • Algorithm for Decision Tree Induction
  • Creating a Decision Tree
  • Confusion Matrix
  • Case study
Random Forest Classifier
  • What is Random Forests
  • Features of Random Forest
  • Out of Box Error Estimate and Variable Importance
  • Case study
Naive Bayes Classifier
  • Case study
Support Vector Machines
  • Case Study
  • Introduction to SVMs
  • SVM History
  • Vectors Overview
  • Decision Surfaces
  • Linear SVMs
  • The Kernel Trick
  • Non-Linear SVMs
  • The Kernel SVM
Time Series Analysis
  • Describe Time Series data
  • Format your Time Series data
  • List the different components of Time Series data
  • Discuss different kind of Time Series scenarios
  • Choose the model according to the Time series scenario
  • Implement the model for forecasting
  • Explain working and implementation of ARIMA model
  • Illustrate the working and implementation of different ETS models
  • Forecast the data using the respective model
  • What is Time Series data?
  • Time Series variables
  • Different components of Time Series data
  • Visualize the data to identify Time Series Components
  • Implement ARIMA model for forecasting
  • Exponential smoothing models
  • Identifying different time series scenario based on which different Exponential Smoothing model can be applied
  • Implement respective model for forecasting
  • Visualizing and formatting Time Series data
  • Plotting decomposed Time Series data plot
  • Applying ARIMA and ETS model for Time Series forecasting
  • Forecasting for given Time period
  • Case Study 
Feature Selection and Pre-processing
  • How to select the right data
  • Which are the best features to use
  • Additional feature selection techniques
  • A feature selection case study
  • Preprocessing
  • Preprocessing Scaling Techniques
  • How to preprocess your data
  • How to scale your data
  • Feature Scaling Final Project
Which Algorithms perform best
  • Highly efficient machine learning algorithms
  • Bagging Decision Trees
  • The power of ensembles
  • Random Forest Ensemble technique
  • Boosting – Adaboost
  • Boosting ensemble stochastic gradient boosting
  • A final ensemble technique
Model selection cross validation score
  • Introduction Model Tuning
  • Parameter Tuning GridSearchCV
  • A second method to tune your algorithm
  • How to automate machine learning
  • Which ML algo should you choose
  • How to compare machine learning algorithms in practice
Clustering
  • Similarity Metrics
  • Distance Measure Types: Euclidean, Cosine Measures
  • Creating predictive models
  • Understanding K-Means Clustering
  • Understanding TF-IDF, Cosine Similarity and their application to Vector Space Model
  • Case study 
Deep Learning
  • Deep Learning Introduction
  • Forward propagation, Backpropagation
  • Activation function,  Need for optimization
  • Gradient descent
  • Deeper networks
  •  Creating Keras Regression Model
  •  Creating Keras Classification Models
  •  Using models, Understanding Model Optimization
  • Model Validation, Model Capacity
  • Project on Deep Learning
  • Project using Keras and tensorflow
  • Convolutional Neural Networks(CNN)
  • Recurrent Neural Networks (RNN)
Natural Language Processing
  • NLU/NLP/Text Analytics/Text Mining
  • Natural Language Understanding/Processing(NLU/P)
  • Regular Expressions
  • Tokenization, Advanced tokenization with regex
  • Charting word length with nltk
  • Word Counts with bag of Words
  • Text pre-processing
  • Gensim
  • Tf-idf with gensim
  • Named Entity Recognition
  • Introduction to spacy
  • Multilingual NER with polyglot
  • Building a “fake news” classifier
  • Dialog Flow
  • RASA NLU
Projects on NLU/NLP
  • Introduction
  • EchoBot, Chitchat Bot
  • Text Munging with regular expressions
  • Understanding intents and entities
  • Word Vectors, Intents and classification, Entity Extraction
  • Robust NLU with Rasa
  •  Building a Virtual assistant
  • Access data from sqlite with parameters
  • Exploring a DB with natural language
  • Incremental slot filling and negation
  • Dialogue
  •           Stateful bots
Machine Learning Project

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