Intellipaat offers a comprehensive Master’s program in Artificial Intelligence to become a certified Artificial Intelligence Engineer. This AI Engineer course will help you learn various aspects of AI like Machine Learning, Deep Learning with TensorFlow, Artificial Neural Networks, Statistics, Data Science, SAS Advanced Analytics, Tableau Business Intelligence, Python and R programming and MS Excel through hands-on projects. As a part of online classroom training, you will receive 5 additional self-paced courses co-created with IBM namely Machine Learning with Python, Deep Learning with TensorFlow, Build Chatbots with Watson Assistant, R for Data Science, and Python. Moreover, you will also get an exclusive Access to IBM Cloud Platforms which are Cognitive Classes and IBM Watson Cloud Lab.
Online Classroom Training
Self Paced Training
This is an Artificial Intelligence Engineer master’s course that is a comprehensive learning approach for mastering the domains of Artificial Intelligence, Data Science, Business Analytics, Business Intelligence, Python coding and Deep Learning with TensorFlow. Upon the completion of the training, you will be able to take on challenging roles in the Artificial Intelligence domain.
There are no prerequisites for taking this Artificial Intelligence master’s course.
Artificial Intelligence is one of the hottest domains being heralded as the one with the ability to disrupt companies cutting across industry sectors. This Intellipaat Artificial Intelligence Engineer master’s course will equip you with all the necessary skills needed to take on challenging and exciting roles in the Artificial Intelligence, Data Science, Business Analytics, Python and R Statistical computing domains and grab the best jobs in the industry at top-notch salaries.
1.1 What is Data Science?
1.2 Significance of Data Science in today’s data-driven world, applications of Data Science, lifecycle of Data Science, and its components
1.3 Introduction to Big Data Hadoop, Machine Learning, and Deep Learning
1.4 Introduction to R programming and RStudio
1. Installation of RStudio
2. Implementing simple mathematical operations and logic using R operators, loops, if statements, and switch cases
2.1 Introduction to data exploration
2.2 Importing and exporting data to/from external sources
2.3 What are data exploratory analysis and data importing?
2.4 DataFrames, working with them, accessing individual elements, vectors, factors, operators, in-built functions, conditional and looping statements, user-defined functions, and data types
1. Accessing individual elements of customer churn data
2. Modifying and extracting results from the dataset using user-defined functions in R
3.1 Need for data manipulation
3.2 Introduction to the dplyr package
3.3 Selecting one or more columns with select(), filtering records on the basis of a condition with filter(), adding new columns with mutate(), sampling, and counting
3.4 Combining different functions with the pipe operator and implementing SQL-like operations with sqldf
1. Implementing dplyr
2. Performing various operations for manipulating data and storing it
4.1 Introduction to visualization
4.2 Different types of graphs, the grammar of graphics, the ggplot2 package, categorical distribution with geom_bar(), numerical distribution with geom_hist(), building frequency polygons with geom_freqpoly(), and making a scatterplot with geom_pont()
4.3 Multivariate analysis with geom_boxplot
4.4 Univariate analysis with a barplot, a histogram and a density plot, and multivariate distribution
4.5 Creating barplots for categorical variables using geom_bar(), and adding themes with the theme() layer
4.6 Visualization with plotly, frequency plots with geom_freqpoly(), multivariate distribution with scatter plots and smooth lines, continuous distribution vs categorical distribution with box-plots, and sub grouping plots
4.7 Working with co-ordinates and themes to make graphs more presentable, understanding plotly and various plots, and visualization with ggvis
4.8 Geographic visualization with ggmap() and building web applications with shinyR
1. Creating data visualization to understand the customer churn ratio using ggplot2 charts
2. Using plotly for importing and analyzing data
3. Visualizing tenure, monthly charges, total charges, and other individual columns using a scatter plot
5.1 Why do we need statistics?
5.2 Categories of statistics, statistical terminology, types of data, measures of central tendency, and measures of spread
5.3 Correlation and covariance, standardization and normalization, probability and the types, hypothesis testing, chi-square testing, ANOVA, normal distribution, and binary distribution
1. Building a statistical analysis model that uses quantification, representations, and experimental data
2. Reviewing, analyzing, and drawing conclusions from the data
6.1 Introduction to Machine Learning
6.2 Introduction to linear regression, predictive modeling, simple linear regression vs multiple linear regression, concepts, formulas, assumptions, and residuals in Linear Regression, and building a simple linear model
6.3 Predicting results and finding the p-value and an introduction to logistic regression
6.4 Comparing linear regression with logistics regression and bivariate logistic regression with multivariate logistic regression
6.5 Confusion matrix the accuracy of a model, understanding the fit of the model, threshold evaluation with ROCR, and using qqnorm() and qqline()
6.6 Understanding the summary results with null hypothesis, F-statistic, and
building linear models with multiple independent variables
1. Modeling the relationship within data using linear predictor functions
2. Implementing linear and logistics regression in R by building a model with ‘tenure’ as the dependent variable
7.1 Introduction to logistic regression
7.2 Logistic regression concepts, linear vs logistic regression, and math behind logistic regression
7.3 Detailed formulas, logit function and odds, bivariate logistic regression, and Poisson regression
7.4 Building a simple binomial model and predicting the result, making a confusion matrix for evaluating the accuracy, true positive rate, false positive rate, and threshold evaluation with ROCR
7.5 Finding out the right threshold by building the ROC plot, cross validation, multivariate logistic regression, and building logistic models with multiple independent variables
7.6 Real-life applications of logistic regression
1. Implementing predictive analytics by describing data
2. Explaining the relationship between one dependent binary variable and one or more binary variables
3. Using glm() to build a model, with ‘Churn’ as the dependent variable
8.1 What is classification? Different classification techniques
8.2 Introduction to decision trees
8.3 Algorithm for decision tree induction and building a decision tree in R
8.4 Confusion matrix and regression trees vs classification trees
8.5 Introduction to bagging
8.6 Random forest and implementing it in R
8.7 What is Naive Bayes? Computing probabilities
8.8 Understanding the concepts of Impurity function, Entropy, Gini index, and Information gain for the right split of node
8.9 Overfitting, pruning, pre-pruning, post-pruning, and cost-complexity pruning, pruning a decision tree and predicting values, finding out the right number of trees, and evaluating performance metrics
1. Implementing random forest for both regression and classification problems
2. Building a tree, pruning it using ‘churn’ as the dependent variable, and building a random forest with the right number of trees
3. Using ROCR for performance metrics
9.1 What is Clustering? Its use cases
9.2 what is k-means clustering? What is canopy clustering?
9.3 What is hierarchical clustering?
9.4 Introduction to unsupervised learning
9.5 Feature extraction, clustering algorithms, and the k-means clustering algorithm
9.6 Theoretical aspects of k-means, k-means process flow, k-means in R, implementing k-means, and finding out the right number of clusters using a scree plot
9.7 Dendograms, understanding hierarchical clustering, and implementing it in R
9.8 Explanation of Principal Component Analysis (PCA) in detail and implementing PCA in R
1. Deploying unsupervised learning with R to achieve clustering and dimensionality reduction
2. K-means clustering for visualizing and interpreting results for the customer churn data
10.1 Introduction to association rule mining and MBA
10.2 Measures of association rule mining: Support, confidence, lift, and apriori algorithm, and implementing them in R
10.3 Introduction to recommendation engines
10.4 User-based collaborative filtering and item-based collaborative filtering, and implementing a recommendation engine in R
10.5 Recommendation engine use cases
1. Deploying association analysis as a rule-based Machine Learning method
2. Identifying strong rules discovered in databases with measures based on interesting discoveries
11.1 Introducing Artificial Intelligence and Deep Learning
11.2 What is an artificial neural network? TensorFlow: The computational framework for building AI models
11.3 Fundamentals of building ANN using TensorFlow and working with TensorFlow in R
12.1 What is a time series? The techniques, applications, and components of time series
12.2 Moving average, smoothing techniques, and exponential smoothing
12.3 Univariate time series models and multivariate time series analysis
12.4 ARIMA model
12.5 Time series in R, sentiment analysis in R (Twitter sentiment analysis), and text analysis
1. Analyzing time series data
2. Analyzing the sequence of measurements that follow a non-random order to identify the nature of phenomenon and forecast the future values in the series
13.1 Introduction to Support Vector Machine (SVM)
13.2 Data classification using SVM
13.3 SVM algorithms using separable and inseparable cases
13.4 Linear SVM for identifying margin hyperplane
14.1 What is the Bayes theorem?
14.2 What is Naïve Bayes Classifier?
14.3 Classification Workflow
14.4 How Naive Bayes classifier works and classifier building in Scikit-Learn
14.5 Building a probabilistic classification model using Naïve Bayes and the zero probability problem
15.1 Introduction to the concepts of text mining
15.2 Text mining use cases and understanding and manipulating the text with ‘tm’ and ‘stringR’
15.3 Text mining algorithms and the quantification of the text
15.4 TF-IDF and after TF-IDF
Case Study 01: Market Basket Analysis (MBA)
1.1 This case study is associated with the modeling technique of Market Basket Analysis, where you will learn about loading data, plotting items, and running algorithms.
1.2 It includes finding out the items that go hand in hand and can be clubbed together.
1.3 This is used for various real-world scenarios like a supermarket shopping cart and so on.
Case Study 02: Logistic Regression
2.1 In this case study, you will get a detailed understanding of the advertisement spends of a company that will help drive more sales.
2.2 You will deploy logistic regression to forecast future trends.
2.3 You will detect patterns and uncover insight using the power of R programming.
2.4 Due to this, the future advertisement spends can be decided and optimized for higher revenues.
Case Study 03: Multiple Regression
3.1 You will understand how to compare the miles per gallon (MPG) of a car based on various parameters.
3.2 You will deploy multiple regression and note down the MPG for car make, model, speed, load conditions, etc.
3.3 The case study includes model building, model diagnostic, and checking the ROC curve, among other things.
Case Study 04: Receiver Operating Characteristic (ROC)
4.1 In this case study, you will work with various datasets in R.
4.2 You will deploy data exploration methodologies.
4.3 You will also build scalable models.
4.4 Besides, you will predict the outcome with highest precision, diagnose the model that you have created with real-world data, and check the ROC curve.
Market Basket Analysis
This is an inventory management project where you will find the trends in the data that will help the company to increase sales. In this project, you will be implementing association rule mining, data extraction, and data manipulation for the Market Basket Analysis.
Credit Card Fraud Detection
The project consists of data analysis for various parameters of banking dataset. You will be using a V7 predictor, V4 predictor for analysis, and data visualization for finding the probability of occurrence of fraudulent activities.
Loan Approval Prediction
In this project, you will use the banking dataset for data analysis, data cleaning, data preprocessing, and data visualization. You will implement algorithms such as Principal Component Analysis and Naive Bayes after data analysis to predict the approval rate of a loan using various parameters.
Netflix Recommendation System
Implement exploratory data analysis, data manipulation, and visualization to understand and find the trends in the Netflix dataset. You will use various Machine Learning algorithms such as association rule mining, classification algorithms, and many more to create movie recommendation systems for viewers using Netflix dataset.
Case Study 1: Introduction to R Programming
In this project, you need to work with several operators involved in R programming including relational operators, arithmetic operators, and logical operators for various organizational needs.
Case Study 2: Solving Customer Churn Using Data Exploration
Use data exploration in order to understand what needs to be done to make reductions in customer churn. In this project, you will be required to extract individual columns, use loops to work on repetitive operations, and create and implement filters for data manipulation.
Case Study 3: Creating Data Structures in R
Implement numerous data structures for numerous possible scenarios. This project requires you to create and use vectors. Further, you need to build and use metrics, utilize arrays for storing those metrics, and have knowledge of lists.
Case Study 4: Implementing SVD in R
Utilize the dataset of MovieLens to analyze and understand single value decomposition and its use in R programming. Further, in this project, you must build custom recommended movie sets for all users, develop a collaborative filtering model based on the users, and for a movie recommendation, you must create realRatingMatrix.
Case Study 5: Time Series Analysis
This project required you to perform TSA and understand ARIMA and its concepts with respect to a given scenario. Here, you will use the R programming language, ARIMA model, time series analysis, and data visualization. So, you must understand how to build an ARIMA model and fit it, find optimal parameters by plotting PACF charts, and perform various analyses to predict values.
1.1 Introduction to Python Language
1.2 Features, the advantages of Python over other programming languages
1.3 Python installation – Windows, Mac & Linux distribution for Anaconda Python
1.4 Deploying Python IDE
1.5 Basic Python commands, data types, variables, keywords and more
Hands-on Exercise – Installing Python Anaconda for the Windows, Linux and Mac.
2.1 Built-in data types in Python
2.2 Learn classes, modules, Str(String), Ellipsis Object, Null Object, Ellipsis, Debug
2.3 Basic operators, comparison, arithmetic, slicing and slice operator, logical, bitwise
2.4 Loop and control statements while, for, if, break, else, continue.
Hands-on Exercise –
1. Write your first Python program
2. Write a Python Function (with and without parameters)
3. Use Lambda expression
4. Write a class
5. Create a member function and a variable
6. create an object
7. Write a for loop
3.1 How to write OOP concepts program in Python
3.2 Connecting to a database
3.3 Classes and objects in Python
3.4 OOPs paradigm, important concepts in OOP like polymorphism, inheritance, encapsulation
3.5 Python functions, return types and parameters
3.6 Lambda expressions
Hands-on Exercise –
1. Creating an application which helps to check balance, deposit money and withdraw the money using the concepts of OOPS.
4.1 Understanding the Database, need of database
4.2 Installing MySQL on windows
4.3 Understanding Database connection using Python.
Hands-on Exercise – Demo on Database Connection using python and pulling the data.
5.1 Introduction to arrays and matrices
5.2 Broadcasting of array math, indexing of array
5.3 Standard deviation, conditional probability, correlation and covariance.
Hands-on Exercise –
1. How to import NumPy module
2. Creating array using ND-array
3. Calculating standard deviation on array of numbers
4. Calculating correlation between two variables.
6.1 Introduction to SciPy
6.2 Functions building on top of NumPy, cluster, linalg, signal, optimize, integrate, subpackages, SciPy with Bayes Theorem.
Hands-on Exercise –
1. Importing of SciPy
2. Applying the Bayes theorem on the given dataset.
7.1 How to plot graph and chart with Python
7.2 Various aspects of line, scatter, bar, histogram, 3D, the API of MatPlotLib, subplots.
Hands-on Exercise –
1. Deploying MatPlotLib for creating Pie, Scatter, Line, Histogram.
8.1 Introduction to Python dataframes
8.2 Importing data from JSON, CSV, Excel, SQL database, NumPy array to dataframe
8.3 Various data operations like selecting, filtering, sorting, viewing, joining, combining
Hands-on Exercise –
1. Working on importing data from JSON files
2. Selecting record by a group
3. Applying filter on top, viewing records
9.1 Introduction to Exception Handling
9.2 Scenarios in Exception Handling with its execution
9.3 Arithmetic exception
9.4 RAISE of Exception
9.5 What is Random List, running a Random list on Jupyter Notebook
9.6 Value Error in Exception Handling.
Hands-on Exercise –
1. Demo on Exception Handling with an Industry-based Use Case.
10.1 Introduction to Thread, need of threads
10.2 What are thread functions
10.3 Performing various operations on thread like joining a thread, starting a thread, enumeration in a thread
10.4 Creating a Multithread, finishing the multithreads.
10.5 Understanding Race Condition, lock and Synchronization.
Hands-on Exercise –
1. Demo on Starting a Thread and a Multithread and then perform multiple operations on them.
11.1 Intro to modules in Python, need of modules
11.2 How to import modules in python
11.3 Locating a module, namespace and scoping
11.4 Arithmetic operations on Modules using a function
11.5 Intro to Search path, Global and local functions, filter functions
11.6 Python Packages, import in packages, various ways of accessing the packages
11.7 Decorators, Pointer assignments, and Xldr.
Hands-on Exercise –
1. Demo on Importing the modules and performing various operation on them using arithmetic functions
2. Importing various packages and accessing them and then performing different operations on them.
12.1 Introduction to web scraping in Python
12.2 Installing of beautifulsoup
12.3 Installing Python parser lxml
12.4 Various web scraping libraries, beautifulsoup, Scrapy Python packages
12.5 Creating soup object with input HTML
12.6 Searching of tree, full or partial parsing, output print
Hands-on Exercise –
1. Installation of Beautiful soup and lxml Python parser
2. Making a soup object with input HTML file
3. Navigating using Py objects in soup tree.
Analyzing the Naming Pattern Using Python
In this Python project, you will work with the United States Social Security Administration (SSA) which has made data on the frequency of baby names from 1880 to 2016 available. The project requires analyzing the data considering different methods. You will visualize the most frequent names, determine the naming trends and come up with the most popular names for a certain year.
Performing Analysis on Customer Churn Dataset
Using the powers of data science and data visualization you will be performing analysis on the reliability of the employees of a telecom industry. This real time analysis of data will be done through multiple labels and the final outcomes will be reflected through multiple reports.
Python Web Scraping for Data Science
Through this project you will be introduced to the process of web scraping using Python. It involves installation of Beautiful Soup, web scraping libraries, working on common data and page format on the web, learning the important kinds of objects, Navigable String, deploying the searching tree, navigation options, parser, search tree, searching by CSS class, list, function and keyword argument.
1.1 Need of Machine Learning
1.2 Introduction to Machine Learning
1.3 Types of Machine Learning, such as supervised, unsupervised, and reinforcement learning, Machine Learning with Python, and the applications of Machine Learning
2.1 Introduction to supervised learning and the types of supervised learning, such as regression and classification
2.2 Introduction to regression
2.3 Simple linear regression
2.4 Multiple linear regression and assumptions in linear regression
2.5 Math behind linear regression
1. Implementing linear regression from scratch with Python
2. Using Python library Scikit-Learn to perform simple linear regression and multiple linear regression
3. Implementing train–test split and predicting the values on the test set
3.1 Introduction to classification
3.2 Linear regression vs logistic regression
3.3 Math behind logistic regression, detailed formulas, the logit function and odds, confusion matrix and accuracy, true positive rate, false positive rate, and threshold evaluation with ROCR
1. Implementing logistic regression from scratch with Python
2. Using Python library Scikit-Learn to perform simple logistic regression and multiple logistic regression
3. Building a confusion matrix to find out accuracy, true positive rate, and false positive rate
4.1 Introduction to tree-based classification
4.2 Understanding a decision tree, impurity function, entropy, and understanding the concept of information gain for the right split of node
4.3 Understanding the concepts of information gain, impurity function, Gini index, overfitting, pruning, pre-pruning, post-pruning, and cost-complexity pruning
4.4 Introduction to ensemble techniques, bagging, and random forests and finding out the right number of trees required in a random forest
1. Implementing a decision tree from scratch in Python
2. Using Python library Scikit-Learn to build a decision tree and a random forest
3. Visualizing the tree and changing the hyper-parameters in the random forest
5.1 Introduction to probabilistic classifiers
5.2 Understanding Naïve Bayes and math behind the Bayes theorem
5.3 Understanding a support vector machine (SVM)
5.4 Kernel functions in SVM and math behind SVM
1. Using Python library Scikit-Learn to build a Naïve Bayes classifier and a support vector classifier
6.1 Types of unsupervised learning, such as clustering and dimensionality reduction, and the types of clustering
6.2 Introduction to k-means clustering
6.3 Math behind k-means
6.4 Dimensionality reduction with PCA
1. Using Python library Scikit-Learn to implement k-means clustering
2. Implementing PCA (principal component analysis) on top of a dataset
7.1 Introduction to Natural Language Processing (NLP)
7.2 Introduction to text mining
7.3 Importance and applications of text mining
7.4 How NPL works with text mining
7.5 Writing and reading to word files
7.6 Language Toolkit (NLTK) environment
7.7 Text mining: Its cleaning, pre-processing, and text classification
1. Learning Natural Language Toolkit and NLTK Corpora
2. Reading and writing .txt files from/to a local drive
3. Reading and writing .docx files from/to a local drive
8.1 Introduction to Deep Learning with neural networks
8.2 Biological neural networks vs artificial neural networks
8.3 Understanding perception learning algorithm, introduction to Deep Learning frameworks, and TensorFlow constants, variables, and place-holders
9.1 What is time series? Its techniques and applications
9.2 Time series components
9.3 Moving average, smoothing techniques, and exponential smoothing
9.4 Univariate time series models
9.5 Multivariate time series analysis
9.6 ARIMA model and time series in Python
9.7 Sentiment analysis in Python (Twitter sentiment analysis) and text analysis
1. Analyzing time series data
2. The sequence of measurements that follow a non-random order to recognize the nature of the phenomenon
3. Forecasting the future values in the series
Analyzing the Trends of COVID-19 with Python
In this project, you will be using Pandas to accumulate data from multiple data files, Plotly to create interactive visualizations, Facebook’s Prophet library to make time series models, and visualizing the prediction by combining these technologies.
Customer Churn Classification
This project will help you get more familiar with Machine Learning algorithms. You will be manipulating data to gain meaningful insights, visualizing data to figure out trends and patterns among different factors, and implementing algorithms like linear regression, decision tree, and Naïve Bayes.
Creating a Recommendation System for Movies
You will be creating a Recommendation system for movies by working with Rating prediction, item prediction, user-based methods in k-nearest neighbor, matrix factorization, decomposition of singular value, collaboration filtering, business variables overview, etc. Two approaches you will use are memory-based and model-based.
Case Study 1 - Decision Tree
Conducting this case study will help you understand the structure of a dataset (PIMA Indians Diabetes database) and create a decision tree model based on it by making use of Scikit-Learn.
Case Study 2 - Insurance Cost Prediction (Linear Regression)
In this case study, you will understand the structure of a medical insurance dataset, implement both simple and multiple linear regressions, and predict values for the insurance cost.
Case Study 3 - Diabetes Classification (Logistic Regression)
Through this case study, you will come to understand the structure of a dataset (PIMA Indians Diabetes dataset), implement multiple logistic regressions and classify, fit your model on the test and train data for prediction, evaluate your model using confusion matrix, and then visualize it.
Case Study 4 - Random Forest
You will be creating a model that would help in classifications of patients in the following ways: ‘is normal,’ ‘is suspected to have a disease,’ or in actuality ‘has the disease’ with the help of the ‘Cardiotocography’ dataset.
Case Study 5 - Principal Component Analysis (PCA)
As part of the case study, you will read the sample Iris dataset. You will use PCA to figure out the number of most important principal features and reduce the number of features using PCA. You will have to train and test the random forest classifier algorithm to check the model performance. Find the optimal number of dimensions that will give good quality results and predict accurately.
Case Study 6 - K-means Clustering
This case study involves data analysis, column extraction from the dataset, data visualization, using the elbow method to find out the appropriate number of groups or clusters for the data to be segmented, using k-means clustering, segmenting the data into k groups, visualizing a scatter plot of clusters, and many more.
1.1 Field of machine learning, its impact on the field of artificial intelligence
1.2 The benefits of machine learning w.r.t. Traditional methodologies
1.3 Deep learning introduction and how it is different from all other machine learning methods
1.4 Classification and regression in supervised learning
1.5 Clustering and association in unsupervised learning, algorithms that are used in these categories
1.6 Introduction to ai and neural networks
1.7 Machine learning concepts
1.8 Supervised learning with neural networks
1.9 Fundamentals of statistics, hypothesis testing, probability distributions, and hidden markov models.
2.1 Multi-layer network introduction, regularization, deep neural networks
2.2 Multi-layer perceptron
2.3 Overfitting and capacity
2.4 Neural network hyperparameters, logic gates
2.5 Different activation functions used in neural networks, including relu, softmax, sigmoid and hyperbolic functions
2.6 Back propagation, forward propagation, convergence, hyperparameters, and overfitting.
3.1 Various methods that are used to train artificial neural networks
3.2 Perceptron learning rule, gradient descent rule, tuning the learning rate, regularization techniques, optimization techniques
3.3 Stochastic process, vanishing gradients, transfer learning, regression techniques,
3.4 Lasso l1 and ridge l2, unsupervised pre-training, xavier initialization.
4.1 Understanding how deep learning works
4.2 Activation functions, illustrating perceptron, perceptron training
4.3 multi-layer perceptron, key parameters of perceptron;
4.4 Tensorflow introduction and its open-source software library that is used to design, create and train
4.5 Deep learning models followed by google’s tensor processing unit (tpu) programmable ai
4.6 Python libraries in tensorflow, code basics, variables, constants, placeholders
4.7 Graph visualization, use-case implementation, keras, and more.
5.1 Keras high-level neural network for working on top of tensorflow
5.2 Defining complex multi-output models
5.3 Composing models using keras
5.3 Sequential and functional composition, batch normalization
5.4 Deploying keras with tensorboard, and neural network training process customization.
6.1 Using tflearn api to implement neural networks
6.2 Defining and composing models, and deploying tensorboard
7.1 Mapping the human mind with deep neural networks (dnns)
7.2 Several building blocks of artificial neural networks (anns)
7.3 The architecture of dnn and its building blocks
7.4 Reinforcement learning in dnn concepts, various parameters, layers, and optimization algorithms in dnn, and activation functions.
8.1 What is a convolutional neural network?
8.2 Understanding the architecture and use-cases of cnn
8.3‘What is a pooling layer?’ how to visualize using cnn
8.4 How to fine-tune a convolutional neural network
8.5 What is transfer learning?
8.6 Understanding recurrent neural networks, kernel filter, feature maps, and pooling, and deploying convolutional neural networks in tensorflow.
9.1 Introduction to the rnn model
9.2 Use cases of rnn, modeling sequences
9.3 Rnns with back propagation
9.4 Long short-term memory (lstm)
9.5 Recursive neural tensor network theory, the basic rnn cell, unfolded rnn, dynamic rnn
9.6 Time-series predictions.
10.1 Gpu’s introduction, ‘how are they different from cpus?,’ the significance of gpus
10.2 Deep learning networks, forward pass and backward pass training techniques
10.3 Gpu constituent with simpler core and concurrent hardware.
11.1 Introduction rbm and autoencoders
11.2 Deploying rbm for deep neural networks, using rbm for collaborative filtering
11.3 Autoencoders features and applications of autoencoders.
12.1 Image processing
12.2 Natural language processing (nlp) – Speech recognition, and video analytics.
13.1 Automated conversation bots leveraging any of the following descriptive techniques: Ibm watson, Microsoft’s luis, Open–closed domain bots,
13.2 Generative model, and the sequence to sequence model (lstm).
As part of this assignment, you have to implement an LSTM encoder. Create an input sequence of numbers. Build an LSTM RNN model on top of this data. Compile the model with ‘adam’ to be the optimizer and loss to be ‘mse’. Fit the model on data and set the number of epochs to be 300. Predict the values and verify it with the input data.
In this assignment, you have to build your convolutional Neural Network using MNIST dataset. For this, you will have to download the MNIST dataset through Keras. You will be asked to fit the dataset to a model and evaluate the loss and accuracy of the model. You will be working with pooling layers, dense layers, dropout layers, flatten layers, and NumPy.
Binary Classification on ‘Customer_Churn’ Using Keras
In this project, you will have to analyze the data of a telecom company to find insights and stop customers from churning out to other telecom companies. You will be working on data manipulation and visualization, and create 3 different models with the help of Keras.
Face Detection Project
For the project, you will be using Python 3.5(64-bit) with OpenCV for face detection. The system will have to be able to detect multiple faces in a single image. You will be working with essential libraries like cv2 and glob (glob helps in finding all the pathnames matching a specified pattern).
Build a sequential model using Keras on top of this Diabetes dataset to find out if a patient has diabetes or not. You will use Stochastic Gradient as the optimization algorithm. You will be required to build another sequential model where ‘Outcome’ is the dependent variable and all other columns are predictors.
You will be detecting wine fraud using Neural Networks as a part of this assignment. You will use the latest version of SciKit Learn (>0.18). Use the wine data set from the UCI Machine Learning Repository. Import the dataset, split the data, and use the predict () method to get predictions. You will have to train your model using Scikit Learn’s estimator objects.
AI and Deep Learning Intro Assignment
For this assignment, you will need to install Anaconda on your system with Python version 3.6 or above. Create a TensorFlow environment, download TensorFlow, and download Pandas, Numpy, SciKit-learn, SciPy, Matplotlib in both Anaconda and TensorFlow environment. You will also need to install Keras and TFLearn in the TensorFlow environment.
As part of the assignment, you will be using an airline-passenger dataset to predict the number of passengers for a particular month. Write a simple function to convert a single column of data into a two-column dataset. You will divide the data into train and test set.
Through this assignment, you will learn to create a session in TensorFlow. You will define constants and perform computations using the session, print ‘Hello World’ using the same, and create a simple Linear Equation, y=mx+c in Tensorflow, where m and c are variables and x is a placeholder.
In this assignment, you will be required to find out the factors that lead up to a patient having cancer. You will need to load the dataset and print the number of samples and features in the data. Then, you will divide the data into train & and create a network.
Introduction to Natural Language Processing (NLP), Introduction to Text Mining, importance and applications of Text Mining, how NPL works with Text Mining, writing and reading to word files, OS Module, Natural Language Toolkit (NLTK) Environment.
Hands-on Exercise: Learning Natural Language Toolkit and NLTK Corpora, reading and writing .txt files to/from local drive, reading and writing .docx Files to/from local drive.
Various Tokenizers, Tokenization, Frequency Distribution, Stemming, POS Tagging, Lemmatization, Bigrams, Trigrams & Ngrams, Lemmatization, Entity Recognition.
Hands-on Exercise: Learning Word Tokenization with Python regular expressions, Sentence Tokenizers, Stopword Removal, Bigrams, Trigrams, and Ngrams, Named Entity Recognition, and POS Tagging.
Overview of Machine Learning, Words, Term Frequency, Count Vectorizer, Inverse Document Frequency, Text conversion, Confusion Matrix, Naiive Bayes Classifier.
Hands-on Exercise: Demonstration of Count Vectorizer, Words, Term Frequency, Inverse Document Frequency, Text conversion, text classification, and Confusion Matrix.
Language Modeling, Sequence Tagging, Sequence Tasks, Predicting Sequence of Tags, Syntax Trees, Context-Free Grammars, Chunking, Automatic Paraphrasing of Texts, Chinking.
Hands-on Exercise: Demonstration of Syntax Trees, Chunking, Automatic Paraphrasing of Texts, and Chinking.
Distributional Semantics, Traditional Models, Tools for sentence and word embeddings, an overview of Topic Models.
Hands-on Exercise: Embedding word and sentence.
Introduction to task-oriented Dialog Systems, Natural Language Understanding, Dialog Manager.
Hands-on Exercise: Design your own Dialog System.
Project: Analyze Movie Review Data with NLP
Problem Statement: Perform sentiment analysis on a given dataset to analyze movie reviews
Project Description: In this project, as an NLP engineer, your job is to pre-process the data using tokenization and lemmatization and then develop an understanding of the different components of the data by identifying different parts of the speech and named entities in the text. After having a sufficient understanding of the attributes and syntactic structure of the text, perform a sentimental analysis task on the data by classifying whether movie reviews are positive or negative.
In this project, you will edit the dataset “Movie Reviews“, which is included in NLTK Corpus. The dataset contains multiple positive and negative reviews retrieved from the imdb.com.
1.1 Introduction rbm and autoencoders
1.2 Deploying rbm for deep neural networks, using rbm for collaborative filtering
1.3 Autoencoders features and applications of autoencoders.
2.1 Constructing a convolutional neural network using TensorFlow
2.2 Convolutional, dense, and pooling layers of CNNs
2.3 Filtering images based on user queries
3.1 Automated conversation bots leveraging
3.2 Generative model, and the sequence to sequence model (lstm).
4.1 Parallel Training
4.2 Distributed vs Parallel Computing
4.3 Distributed computing in Tensorflow
4.4 Introduction to tf.distribute
4.5 Distributed training across multiple CPUs
4.6 Distributed Training
4.7 Distributed training across multiple GPUs
4.8 Federated Learning
4.9 Parallel computing in Tensorflow
5.1 Mapping the human mind with deep neural networks (dnns)
5.2 Several building blocks of artificial neural networks (anns)
5.3 The architecture of dnn and its building blocks
5.4 Reinforcement learning in dnn concepts, various parameters, layers, and optimization algorithms in dnn, and activation functions.
6.1 Understanding model Persistence
6.2 Saving and Serializing Models in Keras
6.3 Restoring and loading saved models
6.4 Introduction to Tensorflow Serving
6.5 Tensorflow Serving Rest
6.6 Deploying deep learning models with Docker & Kubernetes
6.7 Tensorflow Serving Docker
6.8 Tensorflow Deployment Flask
6.9 Deploying deep learning models in Serverless Environments
6.10 Deploying Model to Sage Maker
6.11 Explain Tensorflow Lite Train and deploy a CNN model with TensorFlow
Installation and introduction to SAS, how to get started with SAS, understanding different SAS windows, how to work with data sets, various SAS windows like output, search, editor, log and explorer and understanding the SAS functions, which are various library types and programming files
How to import and export raw data files, how to read and subset the data sets, different statements like SET, MERGE and WHERE
Hands-on Exercise: How to import the Excel file in the workspace and how to read data and export the workspace to save data
Different SAS operators like logical, comparison and arithmetic, deploying different SAS functions like Character, Numeric, Is Null, Contains, Like and Input/Output, along with the conditional statements like If/Else, Do While, Do Until and so on
Hands-on Exercise: Performing operations using the SAS functions and logical and arithmetic operations
Understanding about input buffer, PDV (backend) and learning what is Missover
Defining and using KEEP and DROP statements, apply these statements and formats and labels in SAS
Hands-on Exercise: Use KEEP and DROP statements
Understanding the delimiter, dataline rules, DLM, delimiter DSD, raw data files and execution and list input for standard data
Hands-on Exercise: Use delimiter rules on raw data files
Various SAS standard procedures built-in for popular programs: PROC SORT, PROC FREQ, PROC SUMMARY, PROC RANK, PROC EXPORT, PROC DATASET, PROC TRANSPOSE, PROC CORR, etc.
Hands-on Exercise: Use SORT, FREQ, SUMMARY, EXPORT and other procedures
Reading standard and non-standard numeric inputs with formatted inputs, column pointer controls, controlling while a record loads, line pointer control/absolute line pointer control, single trailing, multiple IN and OUT statements, dataline statement and rules, list input method and comparing single trailing and double trailing
Hands-on Exercise: Read standard and non-standard numeric inputs with formatted inputs, control while a record loads, control a line pointer and write multiple IN and OUT statements
SAS Format statements: standard and user-written, associating a format with a variable, working with SAS Format, deploying it on PROC data sets and comparing ATTRIB and Format statements
Hands-on Exercise: Format a variable, deploy format rule on PROC data set and use ATTRIB statement
Understanding PROC GCHART, various graphs, bar charts: pie, bar and 3D and plotting variables with PROC GPLOT
Hands-on Exercise: Plot graphs using PROC GPLOT and display charts using PROC GCHART
SAS advanced data discovery and visualization, point-and-click analytics capabilities and powerful reporting tools
Character functions, numeric functions and converting variable type
Hands-on Exercise: Use functions in data transformation
Introduction to ODS, data optimization and how to generate files (rtf, pdf, html and doc) using SAS
Hands-on Exercise: Optimize data and generate rtf, pdf, html and doc files
Macro Syntax, macro variables, positional parameters in a macro and macro step
Hands-on Exercise: Write a macro and use positional parameters
SQL statements in SAS, SELECT, CASE, JOIN and UNION and sorting data
Hands-on Exercise: Create SQL query to select and add a condition and use a CASE in select query
Base SAS web-based interface and ready-to-use programs, advanced data manipulation, storage and retrieval and descriptive statistics
Hands-on Exercise: Use web UI to do statistical operations
Report enhancement, global statements, user-defined formats, PROC SORT, ODS destinations, ODS listing, PROC FREQ, PROC Means, PROC UNIVARIATE, PROC REPORT and PROC PRINT
Hands-on Exercise: Use PROC SORT to sort the results, list ODS, find mean using PROC Means and print using PROC PRINT
Categorization of Patients Based on the Count of Drugs for Their Therapy
This project aims to find out descriptive statistics and subset for specific clinical data problems. It will give them brief insight about Base SAS procedures and data steps.
Build Revenue Projections Reports
You will be working with the SAS data analytics and business intelligence tool. You will get to work on the data entered in a business enterprise setup and will aggregate, retrieve, and manage that data. Create insightful reports and graphs and come up with statistical and mathematical analysis to predict revenue projection.
Impact of Pre-paid Plans on the Preferences of Investors
This project aims to find the most impacting factors in the preferences of the pre-paid model. The project also identifies which variables are highly correlated with impacting factors. In addition to this, the project also looks to identify various insights that would help a newly established brand to foray deeper into the market on a large scale.
k-means cluster Analysis on Iris Dataset
In this project, you will be required to do k-means cluster analysis on an Iris dataset to predict the class of a flower using the dimensions of its petals.
Introduction to Excel spreadsheet, learning to enter data, filling of series and custom fill list, editing and deleting fields.
Learning about relative and absolute referencing, the concept of relative formulae, the issues in relative formulae, creating of absolute and mixed references and various other formulae.
Creating names range, using names in new formulae, working with the name box, selecting range, names from a selection, pasting names in formulae, selecting names and working with Name Manager.
the various logical functions in Excel, the If function for calculating values and displaying text, nested If functions, VLookUp and IFError functions.
Learning about conditional formatting, the options for formatting cells, various operations with icon sets, data bars and color scales, creating and modifying sparklines.
multi-level drop down validation, restricting value from list only, learning about error messages and cell drop down.
Introduction to the various formulae in Excel like Sum, SumIF & SumIFs, Count, CountA, CountIF and CountBlank, Networkdays, Networkdays International, Today & Now function, Trim (Eliminating undesirable spaces), Concatenate (Consolidating columns)
Introduction to dynamic table in Excel, data conversion, table conversion, tables for charts and VLOOKUP.
Sorting in Excel, various types of sorting including, alphabetical, numerical, row, multiple column, working with paste special, hyperlinking and using subtotal.
The concept of data filtering, understanding compound filter and its creation, removing of filter, using custom filter and multiple value filters, working with wildcards.
Creation of Charts in Excel, performing operations in embedded chart, modifying, resizing, and dragging of chart.
Introduction to the various types of charting techniques, creating titles for charts, axes, learning about data labels, displaying data tables, modifying axes, displaying gridlines and inserting trendlines, textbox insertion in a chart, creating a 2-axis chart, creating combination chart.
The concept of Pivot tables in Excel, report filtering, shell creation, working with Pivot for calculations, formatting of reports, dynamic range assigning, the slicers and creating of slicers.
Data and file security in Excel, protecting row, column, and cell, the different safeguarding techniques.
Learning about VBA macros in Excel, executing macros in Excel, the macro shortcuts, applications, the concept of relative reference in macros.
In-depth understanding of Visual Basic for Applications, the VBA Editor, module insertion and deletion, performing action with Sub and ending Sub if condition not met.
Learning about the concepts of workbooks and worksheets in Excel, protection of macro codes, range coding, declaring a variable, the concept of Pivot Table in VBA, introduction to arrays, user forms, getting to know how to work with databases within Excel.
Learning how the If condition works and knowing how to apply it in various scenarios, working with multiple Ifs in Macro.
Understanding the concept of looping, deploying looping in VBA Macros.
Studying about debugging in VBA, the various steps of debugging like running, breaking, resetting, understanding breakpoints and way to mark it, the code for debugging and code commenting.
The concept of message box in VBA, learning to create the message box, various types of message boxes, the IF condition as related to message boxes.
Mastering the various tasks and functions using VBA, understanding data separation, auto filtering, formatting of report, combining multiple sheets into one, merging multiple files together.
Introduction to powerful data visualization with Excel Dashboard, important points to consider while designing the dashboards like loading the data, managing data and linking the data to tables and charts, creating Reports using dashboard features.
Learning to create charts in Excel, the various charts available, the steps to successfully build a chart, personalization of charts, formatting and updating features, various special charts for Excel dashboards, understanding how to choose the right chart for the right data.
Creation of Pivot Tables in Excel, learning to change the Pivot Table layout, generating Reports, the methodology of grouping and ungrouping of data.
Learning to create Dashboards, the various rules to follow while creating Dashboards, creation of dynamic dashboards, knowing what is data layout, introduction to thermometer chart and its creation, how to use alerts in the Dashboard setup.
How to insert a Scroll bar to a data window?, Concept of Option buttons in a chart, Use of combo box drop-down, List box control Usage, How to use Checkbox Control?
Understanding data quality issues in Excel, linking of data, consolidating and merging data, working with dashboards for Excel Pivot Tables.
Project – if Function
Data – Employee
Problem Statement – It describes about if function and how to implement this if function. It includes following actions:
Calculates Bonus for all employee at 10% of their salary using if Function, Rate the salesman based on the sales and the rating scale., Find the number of times “3” is repeated in the table and find the number of values greater than 5 using Count Function, Uses of Operators and nested if function
1.1 What is data visualization?
1.2 Comparison and benefits against reading raw numbers
1.3 Real use cases from various business domains
1.4 Some quick and powerful examples using Tableau without going into the technical details of Tableau
1.5 Installing Tableau
1.6 Tableau interface
1.7 Connecting to DataSource
1.8 Tableau data types
1.9 Data preparation
2.1 Installation of Tableau Desktop
2.2 Architecture of Tableau
2.3 Interface of Tableau (Layout, Toolbars, Data Pane, Analytics Pane, etc.)
2.4 How to start with Tableau
2.5 The ways to share and export the work done in Tableau
1. Play with Tableau desktop
2. Learn about the interface
3. Share and export existing works
3.1 Connection to Excel
3.2 Cubes and PDFs
3.3 Management of metadata and extracts
3.4 Data preparation
3.5 Joins (Left, Right, Inner, and Outer) and Union
3.6 Dealing with NULL values, cross-database joining, data extraction, data blending, refresh extraction, incremental extraction, how to build extract, etc.
1. Connect to Excel sheet to import data
2. Use metadata and extracts
3. Manage NULL values
4. Clean up data before using
5. Perform the join techniques
6. Execute data blending from multiple sources
4.1 Mark, highlight, sort, group, and use sets (creating and editing sets, IN/OUT, sets in hierarchies)
4.2 Constant sets
4.3 Computed sets, bins, etc.
1. Use marks to create and edit sets
2. Highlight the desired items
3. Make groups
4. Apply sorting on results
5. Make hierarchies among the created sets
5.1 Filters (addition and removal)
5.2 Filtering continuous dates, dimensions, and measures
5.3 Interactive filters, marks card, and hierarchies
5.4 How to create folders in Tableau
5.5 Sorting in Tableau
5.6 Types of sorting
5.7 Filtering in Tableau
5.8 Types of filters
5.9 Filtering the order of operations
1. Use the data set by date/dimensions/measures to add a filter
2. Use interactive filter to view the data
3. Customize/remove filters to view the result
6.1 Using Formatting Pane to work with menu, fonts, alignments, settings, and copy-paste
6.2 Formatting data using labels and tooltips
6.3 Edit axes and annotations
6.4 K-means cluster analysis
6.5 Trend and reference lines
6.6 Visual analytics in Tableau
6.7 Forecasting, confidence interval, reference lines, and bands
1. Apply labels and tooltips to graphs, annotations, edit axes’ attributes
2. Set the reference line
3. Perform k-means cluster analysis on the given dataset
7.1 Working on coordinate points
7.2 Plotting longitude and latitude
7.3 Editing unrecognized locations
7.4 Customizing geocoding, polygon maps, WMS: web mapping services
7.5 Working on the background image, including add image
7.6 Plotting points on images and generating coordinates from them
7.7 Map visualization, custom territories, map box, WMS map
7.8 How to create map projects in Tableau
7.9 Creating dual axes maps, and editing locations
1. Plot longitude and latitude on a geo map
2. Edit locations on the geo map
3. Custom geocoding
4. Use images of the map and plot points
5. Find coordinates
6. Create a polygon map
7. Use WMS
8.1 Calculation syntax and functions in Tableau
8.2 Various types of calculations, including Table, String, Date, Aggregate, Logic, and Number
8.3 LOD expressions, including concept and syntax
8.4 Aggregation and replication with LOD expressions
8.5 Nested LOD expressions
8.6 Levels of details: fixed level, lower level, and higher level
8.7 Quick table calculations
8.8 The creation of calculated fields
8.9 Predefined calculations
8.10 How to validate
9.1 Creating parameters
9.2 Parameters in calculations
9.3 Using parameters with filters
9.4 Column selection parameters
9.5 Chart selection parameters
9.6 How to use parameters in the filter session
9.7 How to use parameters in calculated fields
9.8 How to use parameters in the reference line
1. Creating new parameters to apply on a filter
2. Passing parameters to filters to select columns
3. Passing parameters to filters to select charts
10.1 Dual axes graphs
10.3 Single and dual axes
10.4 Box plot
10.5 Charts: motion, Pareto, funnel, pie, bar, line, bubble, bullet, scatter, and waterfall charts
10.6 Maps: tree and heat maps
10.7 Market basket analysis (MBA)
10.8 Using Show me
10.9 Text table and highlighted table
1. Plot a histogram, tree map, heat map, funnel chart, and more using the given dataset
2. Perform market basket analysis (MBA) on the same dataset
11.1 Building and formatting a dashboard using size, objects, views, filters, and legends
11.2 Best practices for making creative as well as interactive dashboards using the actions
11.3 Creating stories, including the intro of story points
11.4 Creating as well as updating the story points
11.5 Adding catchy visuals in stories
11.6 Adding annotations with descriptions; dashboards and stories
11.7 What is dashboard?
11.8 Highlight actions, URL actions, and filter actions
11.9 Selecting and clearing values
11.10 Best practices to create dashboards
11.11 Dashboard examples; using Tableau workspace and Tableau interface
11.12 Learning about Tableau joins
11.13 Types of joins
11.14 Tableau field types
11.15 Saving as well as publishing data source
11.16 Live vs extract connection
11.17 Various file types
1. Create a Tableau dashboard view, include legends, objects, and filters
2. Make the dashboard interactive
3. Use visual effects, annotations, and descriptions to create and edit a story
12.1 Introduction to Tableau Prep
12.2 How Tableau Prep helps quickly combine join, shape, and clean data for analysis
12.3 Creation of smart examples with Tableau Prep
12.4 Getting deeper insights into the data with great visual experience
12.5 Making data preparation simpler and accessible
12.6 Integrating Tableau Prep with Tableau analytical workflow
12.7 Understanding the seamless process from data preparation to analysis with Tableau Prep
13.1 Introduction to R language
13.2 Applications and use cases of R
13.3 Deploying R on the Tableau platform
13.4 Learning R functions in Tableau
13.5 The integration of Tableau with Hadoop
1. Deploy R on Tableau
2. Create a line graph using R interface
3. Connect Tableau with Hadoop to extract data
Understanding the global covid-19 mortality rates
Analyze and develop a dashboard to understand the covid-19 global cases.Compare the global confirmed vs. death cases in a world map. Compare the country wise cases using logarithmic axes. Dashboard should display both a log axis chart and a default axis chart in an alternate interactive way. Create a parameter to dynamically view Top N WHO regions based on cumulative new cases and death cases ratio. Dashboard should have a drop down menu to view the WHO region wise data using a bar chart, line chart or a map as per user’s requirement.
Understand the UK bank customer data
Analyze and develop a dashboard to understand the customer data of a UK bank. Create an asymmetric drop down of Region with their respective customer names and their Balances with a gender wise color code. Region wise bar chart which displays the count of customers based on High and low balance. Create a parameter to let the users’ dynamically decide the limit value of balance which categorizes it into high and low. Include interactive filters for Job classifications and Highlighters for Region in the final dashboard.
Understand Financial Data
Create an interactive map to analyze the worldwide sales and profit. Include map layers and map styles to enhance the visualization. Interactive analysis to display the average gross sales of a product under each segment, allowing only one segment data to be displayed at once. Create a motion chart to compare the sales and profit through the years. Annotate the day wise profit line chart to indicate the peaks and also enable drop lines. Add go to URL actions in the final dashboard which directs the user to the respective countries Wikipedia page.
Understand Agriculture Data
Create interactive tree map to display district wise data. Tree maps should have state labels. On hovering on a particular state, the corresponding districts data are to be displayed. Add URL actions, which direct users’ to a Google search page of the selected crop. Web page is to be displayed on the final dashboard. Create a hierarchy of seasons, crop categories and the list of crops under each. Add highlighters for season. One major sheet in the final dashboard should be unaffected by any action applied. Use the view in this major sheet to filter data in the other. Using parameters color code the seasons with high yield and low yield based on its crop categories. Rank the crops based on their yield
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This course is designed for clearing the following industry certifications.
Furthermore, you will also be rewarded as an Artificial Intelligence Professional for completing the following learning path that are co-created with IBM:
The complete course is created and delivered in association with IBM to get top jobs in the world’s best organizations. The entire training includes real-world project(s) and case studies that are highly valuable.
Upon the completion of your master’s in AI online, you will have quiz(zes) that will help you prepare for the above-mentioned certification exams and score top marks.
Intellipaat Certification is awarded upon successfully completing the project work(s) and after they are reviewed by experts. Intellipaat certification is recognized in some of the biggest companies like Cisco, Cognizant, Mu Sigma, TCS, Genpact, Hexaware, Sony and Ericsson, among others.
Our Alumni works at top 3000+ companies
Intellipaat provides the best Artificial Intelligence Engineer training course that gives you all the skills needed to work in the domains of AI, Machine Learning, Deep Learning, Data Science with R Statistical computing and Python to give the professionals an added advantage. Upon the completion of the training, you will be awarded the Intellipaat Artificial Intelligence Engineer certification.
You will be working on real-time projects and step-by-step assignments that have high relevance in the corporate world, and the curriculum is designed by industry experts. Upon the completion of the training course, you can apply for some of the best jobs in top MNCs around the world at top salaries. Intellipaat offers lifetime access to videos, course materials, 24/7 support and course material upgrading to the latest version at no extra fee. Hence, it is clearly a one-time investment.
Intellipaat has been serving AI and ML enthusiasts from every corner of the world. You can be living in any country, be it India, Canada, USA, Australia, Japan, or European nations like Germany, UK, or anywhere. You can have full access to the best Artificial Intelligence Master’s programs in the world sitting at home or office 24/7.
At Intellipaat, you can enroll in either the instructor-led online training or self-paced training. Apart from this, Intellipaat also offers corporate training for organizations to upskill their workforce. All trainers at Intellipaat have 12+ years of relevant industry experience, and they have been actively working as consultants in the same domain, which has made them subject matter experts. Go through the sample videos to check the quality of our trainers.
Intellipaat is offering the 24/7 query resolution, and you can raise a ticket with the dedicated support team at anytime. You can avail of the email support for all your queries. If your query does not get resolved through email, we can also arrange one-on-one sessions with our trainers.
You would be glad to know that you can contact Intellipaat support even after the completion of the training. We also do not put a limit on the number of tickets you can raise for query resolution and doubt clearance.
Intellipaat is offering you the most updated, relevant, and high-value real-world projects as part of the training program. This way, you can implement the learning that you have acquired in real-world industry setup. 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.
Intellipaat 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.
Once you complete Intellipaat’s training program, working on real-world projects, quizzes, and assignments and scoring at least 60 percent marks in the qualifying exam, you will be awarded Intellipaat’s course completion certificate. This certificate is very well recognized in Intellipaat-affiliated organizations, including over 80 top MNCs from around the world and some of the Fortune 500companies.
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.