Artificial Intelligence Course Online

In this Artificial Intelligence course online using TensorFlow, you will learn the concepts such as CNN, TensorFlow, Data Science, Neural Networks, Perceptron, NLP, etc. Furthermore, gain hands-on learning experience through the expert training by our faculty. Therefore, as this course curriculum is designed by industry experts, you will master your skills in artificial intelligence and machine learning.

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AI Course Online – Overview

Join this comprehensive Artificial Intelligence training course online and master your skills to work on cutting-edge technologies. In this artificial intelligence training course online using TensorFlow, you will master concepts such as binary classification, supervised and unsupervised learning, logistic regression, vectorization, neural networks, python for scripting machine learning applications, etc.

Artificial Intelligence Online Training – Key Features

  • Trusted content.
  • Re-learn for free anytime in a year.
  • Rigorous assignments and assessments.
  • Learn at your own pace.
  • Mandatory feedback sessions.
  • Mock-interviews.
  • Hands-on real-time experience.
  • Free mentorship.
  • Live chat for instant solutions.
  • Job ready employees post-training.
  • End-to-end training.
  • Download the certificate after the course.

AI Course Online using TensorFlow – Benefits

The global artificial intelligence market size is projected to see a huge expansion in the period 2022 to 2030, it is known to be the best career choice. Also, graduates may experience amazing career opportunities due to its increasing adoption across multiple industry domains.

Designation
Annual Salary
Hiring Companies
Job Wise Benefits
Designation
Artificial Intelligence Engineer

UK
Hiring Companies

Artificial Intelligence Online Program – Training Options

Self-Paced Learning

£ 1200

  • 1-year access to the AI course content.
  • 1 capstone project.
  • Multiple assessments.
  • Continuous feedback sessions.
  • Access to the class recordings.
  • Assistance and support.
  • Download certification.
  • Free mentorship.

Online Boot Camp

£ 1000

  • Everything in Self-paced learning +
  • On-spot doubt clarification.
  • Interactive training sessions.
  • Sessions on the capstone project.
  • Live, online classroom training.
  • Mock-interviews.

Corporate Training

Customized to your team’s needs

  • 1-year access to the AI course content.
  • 1 capstone project.
  • Multiple assessments.
  • Continuous feedback sessions.
  • Class recordings.
  • Assistance and support.
  • Certification after the course.

Artificial Intelligence Online Training Course – Curriculum

Eligibility

New graduates aiming to make a career in AI, software professionals who are seeking a career change, data scientists, and data analysts can register for this artificial intelligence course online.

Pre-requisites

Regardless of their previous knowledge or experience, anyone can join this AI course who is willing to build their career in artificial intelligence.

Course Content

  • Introduction to Python and IDEs – The basics of the python programming language, how you can use various IDEs for python development like Jupyter, Pycharm, etc.
  • Python Basics – Variables, Data Types, Loops, Conditional Statements, functions, decorators, lambda functions, file handling, exception handling ,etc.
  • Object Oriented Programming – Introduction to OOPs concepts like classes, objects, inheritance, abstraction, polymorphism, encapsulation, etc.
  • Hands-on Sessions And Assignments for Practice – The culmination of all the above concepts with real-world problem statements for better understanding.
  • Introduction to Linux – Establishing the fundamental knowledge of how linux works and how you can begin with Linux OS.
  • Linux Basics – File Handling, data extraction, etc.
  • Hands-on Sessions And Assignments for Practice – Strategically curated problem statements for you to start with Linux.
  • Reading the Data, Referencing in formulas , Name Range, Logical Functions, Conditional Formatting, Advanced Validation, Dynamic Tables in Excel, Sorting and Filtering
  • Working with Charts in Excel, Pivot Table, Dashboards, Data And File Security
  • VBA Macros, Ranges and Worksheet in VBA
  • IF conditions, loops, Debugging, etc.
  • Handling Text Data, Splitting, combining, data imputation on text data, Working with Dates in Excel, Data Conversion, Handling Missing Values, Data Cleaning, Working with Tables in Excel, etc.
  • Charts, Pie charts, Scatter and bubble charts
  • Bar charts, Column charts, Line charts, Maps
  • Multiples: A set of charts with the same axes, Matrices, Cards, Tiles
  • Power Pivot, Power Query and Power View
  • Binary Classification Problems, Confusion Matrix, AUC and ROC curve
  • Multiple Classification Problems
  • Probability, Entropy, Dependence
  • Mutual Information
  • Standardization, Normalization, Probability Distributions
  • Inferential Statistics, Hypothesis Testing, ANOVA, Covariance, Correlation
  • Linear Regression, Logistic Regression, Error in regression, Information Gain using Regression
  • Fundamentals of Structured Query Language
  • SQL Tables, Joins, Variables
  • SQL Functions, Subqueries, Rules, Views
  • Nested Queries, string functions, pattern matching
  • Mathematical functions, Date-time functions, etc.
  • Types of UDFs, Inline table value, multi-statement table.
  • Stored procedures, rank function, triggers, etc.
  • Record grouping, searching, sorting, etc.
  • Clustered indexes, common table expressions.
  • What is version control, types, SVN.
  • Common Git commands, Working with branches in Git
  • Github collaboration (pull request), Github Authentication (ssh and Http)
  • Merging branches, Resolving merge conflicts, Git workflow
  • Measure of central tendency, measure of spread, five points summary, etc.
  • Probability Distributions, bayes theorem, central limit theorem.
  • Correlation, covariance, confidence intervals, hypothesis testing, F-test, Z-test, t-test, ANOVA, chi-square test, etc.
  • Web Scraping, Interacting with APIs
  • NumPy Arrays, CRUD Operations,etc.
  • Linear Algebra – Matrix multiplication, CRUD operations, Inverse, Transpose, Rank, Determinant of a matrix, Scalars, Vectors, Matrices.
  • Loading the data, dataframes, series, CRUD operations, splitting the data, etc.
  • Exploratory Data Analysis, Feature engineering, Feature scaling, Normalization, standardization, etc.
  • Null Value Imputations, Outliers Analysis and Handling, VIF, Bias-variance trade-off, cross validation techniques, train-test split, etc.
  • Bar charts, scatter plots, count plots, line plots, pie charts, donut charts, etc, with Python matplotlib.
  • Regression plots, categorical plots, area plots, etc, with Python seaborn.
  • Supervised, Unsupervised learning.
  • Introduction to scikit-learn, Keras, etc.
  • Introduction classification problems, Identification of a regression problem, dependent and independent variables.
  • How to train the model in a regression problem.
  • How to evaluate the model for a regression problem.
  • How to optimize the efficiency of the regression model.
  • Introduction to classification problems, Identification of a classification problem, dependent and independent variables.
  • How to train the model in a classification problem.
  • How to evaluate the model for a classification problem.
  • How to optimize the efficiency of the classification model.
  • Introduction to clustering problems, Identification of a clustering problem, dependent and independent variables.
  • How to train the model in a clustering problem.
  • How to evaluate the model for a clustering problem.
  • How to optimize the efficiency of the clustering model.
  • Linear Regression – Creating linear regression models for linear data using statistical tests, data preprocessing, standardization, normalization, etc.
  • Logistic Regression – Creating logistic regression models for classification problems – such as if a person is diabetic or not, if there will be rain or not, etc.
  • Decision Tree – Creating decision tree models on classification problems in a tree like format with optimal solutions.
  • Random Forest – Creating random forest models for classification problems in a supervised learning approach.
  • Support Vector Machine – SVM or support vector machines for regression and classification problems.
  • Gradient Descent – Gradient descent algorithm that is an iterative optimization approach to finding local minimum and maximum of a given function.
  • K-Nearest Neighbors – A simple algorithm that can be used for classification problems.
  • Time Series Forecasting – Making use of time series data, gathering insights and useful forecasting solutions using time series forecasting.
  • K-means – The k-means algorithm that can be used for clustering problems in an unsupervised learning approach.
  • Dimensionality reduction – Handling multi dimensional data and standardizing the features for easier computation.
  • Linear Discriminant Analysis – LDA or linear discriminant analysis to reduce or optimize the dimensions in the multidimensional data.
  • Principal Component Analysis – PCA follows the same approach in handling the multidimensional data.
  • Classification reports – To evaluate the model on various metrics like recall, precision, f-support, etc.
  • Confusion matrix – To evaluate the true positive/negative, false positive/negative outcomes in the model.
  • r2, adjusted r2, mean squared error, etc.
  • Introduction to keras API and tensorflow
  • Neural networks
  • Multi-layered Neural Networks
  • Artificial Neural Networks
  • Deep neural networks
  • Convolutional Neural Networks
  • Recurrent Neural Networks
  • GPU in deep learning
  • Autoencoders, restricted boltzmann machine
  • Various Tokenizers, Tokenization, Frequency Distribution, Stemming, POS Tagging, Lemmatization, Bigrams, Trigrams & Ngrams, Lemmatization, Entity Recognition
  • Overview of Machine Learning, Words, Term Frequency, Countvectorizer, Inverse Document Frequency, Text conversion, Confusion Matrix, Naive Bayes Classifier.
  • Language Modeling, Sequence Tagging, Sequence Tasks, Predicting Sequence of Tags, Syntax Trees, Context-Free Grammars, Chunking, Automatic Paraphrasing of Texts, Chinking.
  • Using the NLP concepts, build a recommendation engine and an AI chatbot assistant using AI.
  • Introduction rbm and autoencoders
  • Deploying rbm for deep neural networks, using rbm for collaborative filtering
  • Autoencoders features and applications of autoencoders.
  • Constructing a convolutional neural network using TensorFlow
  • Convolutional, dense, and pooling layers of CNNs
  • Filtering images based on user queries
  • Automated conversation bots leveraging
  • Generative model, and the sequence to sequence model (lstm).
  • Parallel Training, Distributed vs Parallel Computing
  • Distributed computing in Tensorflow, Introduction to tf.distribute
  • Distributed training across multiple CPUs, Distributed Training
  • Distributed training across multiple GPUs, Federated Learning
  • Parallel computing in Tensorflow
  • Mapping the human mind with deep neural networks (dnns)
  • Several building blocks of artificial neural networks (anns)
  • The architecture of dnn and its building blocks
  • Reinforcement learning in dnn concepts, various parameters, layers, and optimization algorithms in dnn, and activation functions.Parallel computing in Tensorflow
  • Understanding model Persistence, Saving and Serializing Models in Keras, Restoring and loading saved models
  • Introduction to Tensorflow Serving, Tensorflow Serving Rest, Deploying deep learning models with Docker & Kubernetes, Tensorflow Serving Docker, Tensorflow Deployment Flask.
  • Deploying deep learning models in Serverless Environments
  • Deploying Model to Sage Maker
  • Explain Tensorflow Lite Train and deploy a CNN model with TensorFlow
  • MLOps lifecycle
  • MLOps pipeline
  • MLOps Components, Processes, etc
  • Introduction to Azure Machine Learning
  • Deploying Machine Learning Models using Azure
  • Introduction to PowerBI, Use cases and BI Tools , Data Warehousing, Power BI components, Power BI Desktop, workflows and reports , Data Extraction with Power BI.
  • SaaS Connectors, Working with Azure SQL database, Python and R with Power BI
  • Power Query Editor, Advance Editor, Query Dependency Editor, Data Transformations, Shaping and Combining Data ,M Query and Hierarchies in Power BI.
  • Data Modeling and DAX, Time Intelligence Functions, DAX Advanced Features
  • Slicers, filters, Drill Down Reports
  • Power BI Query, Q & A and Data Insights
  • Power BI Settings, Administration and Direct Connectivity
  • Embedded Power BI API and Power BI Mobile
  • Power BI Advance and Power BI Premium
  • The Data Science capstone project focuses on establishing a strong hold of analyzing a problem and coming up with solutions based on insights from the data analysis perspective.
  • The capstone project will help you master the following verticals:
    • Extracting, loading and transforming data into usable format to gather insights.
    • Data manipulation and handling to pre-process the data.
    • Feature engineering and scaling the data for various problem statements.
    • Model selection and model building on various classification, regression problems using supervised/unsupervised machine learning algorithms.
    • Assessment and monitoring of the model created using the machine learning models.
  • Apache spark framework, RDDs, Stopgaps in existing computing methodologies
  • RDD persistence, caching, General operations: Transformation, Actions, and Functions.
  • Concept of Key-Value pair in RDDs, Other pair, two pair RDDs
  • RDD Lineage, RDD Persistence, WordCount Program Using RDD Concepts
  • RDD Partitioning & How it Helps Achieve Parallelization
  • Passing Functions to Spark, Spark SQL Architecture, SQLContext in Spark SQL
  • User-Defined Functions, Data Frames, Interoperating with RDDs
  • Loading Data through Different Sources, Performance Tuning
  • Spark-Hive Integration
  • Recommendation Engine - The case study will guide you through various processes and techniques in machine learning to build a recommendation engine that can be used for movie recommendations, restaurant recommendations, book recommendations, etc.
  • Rating Predictions - This text classification and sentiment analysis case study will guide you towards working with text data and building efficient machine learning models that can predict ratings, sentiments, etc.
  • Census - Using predictive modeling techniques on the census data, you will be able to create actionable insights for a given population and create machine learning models that will predict or classify various features like total population, user income, etc.
  • Housing - This real estate case study will guide you towards real world problems, where a culmination of multiple features will guide you towards creating a predictive model to predict housing prices.
  • Object Detection – A much more advanced yet simple case study that will guide you towards making a machine learning model that can detect objects in real time.
  • Stock Market Analysis – Using historical stock market data, you will learn about how feature engineering and feature selection can provide you some really helpful and actionable insights for specific stocks.
  • Banking Problem – A classification problem that predicts consumer behavior based on various features using machine learning models.
  • AI Chatbot – Using the NLTK python library, you will be able to apply machine learning algorithms and create an AI chatbot.

Artificial Intelligence Online Training Program – FAQs

If your main goal is to become an artificial intelligence engineer, then it is essential to possess all the AI skills that you will be dealing with as an AI engineer. However, Hatigen’s artificial intelligence training course online allows you to master AI and ML skills and gain practical knowledge with real-time projects.

The increase in demand for artificial intelligence and deep learning has led to the use of TensorFlow. Furthermore, as TensorFlow is the most demanded open-source AI library, it is used for data flow graphs and to build models. Therefore, as TensorFlow plays a crucial role in the field of artificial intelligence, join this AI course online using TensorFlow.

In order to offer in-depth knowledge and practical exposure across various industry verticals, we have designed multiple courses in the artificial intelligence domain. They are:

  • AI for Healthcare Course
  • AI Product Manager Course
  • AI Programming with Python Course
  • Artificial Intelligence for Trading Course
  • Artificial Intelligence Master’s Course
  • Artificial Engineer Architect Course

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