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Applied AI Course - Full Stack Data Science with AI ML Specialization

Applied AI course to power your own Artificial Intelligence applications using Machine Learning & Deep Learning!

Opt for this Applied AI course if you want skills to build your own Machine Learning & Neural Network powered applied AI applications! Over the last couple of years, the demand for Python professionals with AI and Machine Learning skills is highest among Analytics recruiters.


Theoretical concepts of Artificial Intelligence are not sufficient to use it for practical applications and that is where Applied AI comes into the picture. Through this course you will learn various aspects of Data Science, Machine and Deep Learning which you need to apply, both conceptually and practically, to meet tangible business objectives. 


What makes this the best AI and Machine Learning course is that you start your journey from basics by learning important tools like Python and relevant Data Science libraries. The course curriculum also covers data extraction, wrangling, essential statistical concepts, supervised & unsupervised machine learning, and deep learning techniques to power various actual AI applications. One of the few Artificial Intelligence and Machine Learning courses that are also apt for beginners who are looking to head-start their journey in Data Science. We are proud to share that this course is also ranked among India’s top AI courses by Analytics India Magazine.


This specialization will help you to get a break into Artificial Intelligence and Machine Learning domain, with skills based on the most sought after tools and libraries, like Python, Numpy, Pandas, Scikit-Learn, NLTK, TextBlob, PyTorch, TensorFlow, Keras, etc. You will learn AI and Machine Learning starting from data handling, visualization, statistical modeling, machine learning and move towards advanced use of deep learning for AI-based applications like image processing, text data processing, chat-bots, time series, recommendation systems, machine translation, IoT, etc.


After successful completion of this course, you will master not only the theory but also learn how it is applied in the industry. Considering the practical application-based curriculum, this is the best among various Machine Learning and Artificial Intelligence courses in Bangalore and Delhi NCR for aspirants who are looking for an industry-relevant AI ML certification from an established & eminent Institute.


If you are well versed with Data Science & Machine Learning, you may directly opt for our AI and Deep Learning with Python course


Applied AI and Machine Learning course duration: 400 hours (At least 130 hours Live Classes + 54 hours eLearning + Average 8-10 hours of self-study per week)


Delivery Formats:


1. Classroom and bootcamps for Artificial Intelligence course in Bangalore and Delhi NCR (Gurgaon & Noida). 


2. Fully interactive live online training (Global access)


3. Self-paced e-learning modules (Global access)


Useful Blogs:


1. What is Applied AI?


2. How to learn AI and Machine Learning yourself?


3. Machine Learning vs Artificial Intelligence


 

Who Should do this course?

Candidates from various technical or quantitative backgrounds, like Engineering, Maths, Statistics, Business Management who are looking for advanced analytics, AI and machine learning skills and excel in advanced applications of AI & Machine Learning.

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Course Duration 400 hrs
Classes 58
Tools Python - Pandas, NumPy, SciKit Learn, NLTK, TextBlob, PyTorch, TensorFlow & Keras
Learning Mode Live/Video Based

Course Outline

  • Introduction to Bridge Course & Analytics Software’s Basic Excel
  • Basic Programming Elements
  • Introduction to Basic Statistics
  • RDBMS & SQL (Basics)
  • Introduction to Analytics & Data Science
  • Introduction to Mathematical Foundations

  • Overview of Python- Starting with Python
  • Why Python for data science?
  • Anaconda vs. python
  • Introduction to installation of Python
  • Introduction to Python IDE's(Jupyter,/Ipython)
  • Concept of Packages - Important packages
    • NumPy, SciPy, scikit-learn, Pandas, Matplotlib, etc
  • Installing & loading Packages & Name Spaces
  • Data Types & Data objects/structures (strings, Tuples, Lists, Dictionaries)
  • List and Dictionary Comprehensions
  • Variable & Value Labels – Date & Time Values
  • Basic Operations – Mathematical/string/date
  • Control flow & conditional statements
  • Debugging & Code profiling
  • Python Built-in Functions (Text, numeric, date, utility functions)
  • User defined functions – Lambda functions
  • Concept of apply functions
  • Python – Objects – OOPs concepts
  • How to create & call class and modules?

  • What is NumPy?
  • Overview of functions & methods in NumPy
  • Data structures in NumPy
  • Creating arrays and initializing
  • Reading arrays from files
  • Special initializing functions
  • Slicing and indexing
  • Reshaping arrays
  • Combining arrays
  • NumPy Maths

  • What is pandas, its functions & methods
  • Pandas Data Structures (Series & Data Frames)
  • Creating Data Structures (Data import – reading into pandas)

  • Understand the data
  • Sub Setting / Filtering / Slicing Data
    • Using [] brackets
    • Using indexing or referring with column names/rows
    • Using functions
    • Dropping rows & columns
  • Mutation of table (Adding/deleting columns)
  • Binning data (Binning numerical variables in to categorical variables)
  • Renaming columns or rows
  • Sorting (by data/values, index)  -By one column or multiple columns  - Ascending or Descending
  • Type conversions
  • Setting index
  • Handling duplicates /missing/Outliers
  • Creating dummies from categorical data (using get_dummies())
  • Applying functions to all the variables in a data frame (broadcasting)
  • Data manipulation tools(Operators, Functions, Packages, control structures, Loops, arrays etc.)

  • Exploratory data analysis
  • Descriptive statistics, Frequency Tables and summarization
  • Uni-variate Analysis (Distribution of data & Graphical Analysis)
  • Bi-Variate Analysis(Cross Tabs, Distributions & Relationships, Graphical Analysis)

  • Introduction to Data Visualization
  • Introduction to Matplotlib
  • Basic Plotting with Matplotlib
  • Line Plots

  • Basic Visualization Tools
    • Area Plots
    • Histograms
    • Bar Charts
    • Pie Charts
    • Box Plots
    • Scatter Plots
    • Bubble Plots
  • Advanced Visualization Tools
    • Waffle Charts
    • Word Clouds
    • Seaborn and Regression Plots

  • Introduction to Folium
  • Maps with Markers
  • Choropleth Maps

  • Descriptive vs. Inferential Statistics
  • What is probability distribution?
  • Important distributions (discrete & continuous distributions)
  • Deep dive of normal distributions and properties
  • Concept of sampling & types of sampling
  • Concept of standard error and central limit theorem
  • Concept of Hypothesis Testing
  • Statistical Methods - Z/t-tests (One sample, independent, paired), ANOVA, Correlation and Chi- square

  • Introduction to Predictive Modeling
    • Concept of model in analytics and how it is used
    • Common terminology used in modeling process
    • Types of Business problems - Mapping of Algorithms
    • Different Phases of Predictive Modeling
    • Data Exploration for modeling
    • Exploring the data and identifying any problems with the data (Data Audit Report)
    • Identify missing/Outliers in the data
    • Visualize the data trends and patterns
  • Supervised Learning: Regression problems
    • Linear Regression
    • Non-linear Regression
    • K-Nearest Neighbor
    • Decision Trees
    • Ensemble Learning - Bagging, Random Forest, Adaboost, Gradient Boost, XGBoost
    • Support Vector Regressor
  • Supervised Learning: Classification problems
    • Logistic Regression
    • K-Nearest Neighbor
    • Naïve Bayes Classifier
    • Decision Trees
    • Ensemble Learning - Bagging, Random Forest, Adaboost, Gradient Boost, XGBoost
    • Support Vector Classifie

  • Introduction to Machine Learning
    • Applications of Machine Learning
    • Supervised vs Unsupervised Learning
    • Overall process of executing the ML project
    • Stages of ML Project
    • Concept of Over fitting and Under fitting (Bias-Variance Trade off) & Performance Metrics
    • Concept of feature engineering
    • Regularization (LASSO, Elastic net and Ridge)
    • Types of Cross validation(Train & Test, K-Fold validation etc.)
    • Concept of optimization - Gradient descent algorithm
    • Cost & optimization functions
    • Python libraries suitable for Machine Learning
  • Unsupervised Learning
    • Principle Component Analysis 
    • K-Means Clustering
    • Hierarchical Clustering
    • Density-Based Clustering
  • Recommender Systems
    • Content-based recommender systems
    • Collaborative Filtering
  • Time Series Forecasting
    • What is forecasting?
    • Applications of forecasting
    • Time Series Components and Decomposition
    • Types of Seasonality
    • Important terminology: lag, lead, Stationary, stationary tests, auto correlation & white noise, ACF & PACF plots, auto regression, differencing
    • Classification of Time Series Techniques (Uni-variate & Multivariate)
    • Time Series Modeling & Forecasting Techniques
      • Averages (Moving average, Weighted Moving Average)
      • ETS models (Holt Winter Methods)
      • Seasonal Decomposition
      • ARIMA/ARIMAX/SARIMA/SARIMAX
      • Regression
      • Evaluation of Forecasting Models

  • Text Mining - characteristics, trends
  • Text Processing using Base Python & Pandas, Regular Expressions
    • Text processing using string functions & methods
    • Understanding regular expressions
    • Identifying patterns in the text using regular expressions

  • Getting Started with NLTK
  • Introduction to NLP & NLTK
  • Introduction to NLTK Modules (corpus, tokenize, Stem, collocations, tag, classify, cluster, tbl, chunk, Parse, ccg, sem, inference, metrics, app, chat, toolbox etc)

  • Reading data from file folder/from text file, from the Internet & Web scrapping, Data Parsing
  • Cleaning and normalization of data
  • Sentence Tokenize and Word Tokenize, Removing insignificant words(“stop words”), Removing special symbols, removing bullet points and digits, changing letters to lowercase, stemming /lemmatization /chunking
  • Creating Term-Document matrix
  • Tagging text with parts of speech
  • Word Sense Disambiguation
  • Finding associations
  • Measurement of similarity between documents and terms
  • Visualization of term significance in the form of word clouds

  • Vectorization (Count, TF-IDF, Word Embedding's)
  • Sentiment analysis (vocabulary approach, based on Bayesian probability methods)
  • Name entity recognition (NER)
  • Methods of data visualization
    • word length counts plot
    • word frequency plots
    • word clouds
    • correlation plots
    • letter frequency plot
    • Heat map
  • Grouping texts using different methods
  • Language Models and n-grams -- Statistical Models of Unseen Data (Smoothing)

  • Semantic similarity between texts
  • Text Segmentation
  • Topic Mining (LDA)
  • Text Classification (spam detection, sentiment analysis, Intent Analysis)

  • Introduction to Artificial Intelligence (AI)
    • Modern era of AI
    • Role of Machine learning & Deep Learning in AI
    • Hardware for AI (CPU vs. GPU vs. FPGA)
    • Software Frameworks for AI & Deep Learning
    • Key Industry applications of AI
  • Introduction to Deep Learning
    • What are the Limitations of Machine Learning?
    • What is Deep Learning?
    • Advantage of Deep Learning over Machine learning
    • Reasons to go for Deep Learning
    • Real-Life use cases of Deep Learning
    • Overview of important python packages for Deep Learning
  • Introduction to Cloud Computing
    • Introduction to Google Colab
    • What is Cloud Computing? Why it matters?
    • Traditional IT Infrastructure vs. Cloud Infrastructure
    • Cloud Companies (IBM, Microsoft Azure, GCP, AWS ) & their Cloud Services
    • Use Cases of Cloud computing
    • Overview of Cloud Segments: IaaS, PaaS, SaaS
    • Overview of Cloud Deployment Models
    • Implementation of ML/DL model in Cloud

  • Artificial Neural Network
    • Overview of Neural Networks
    • Hidden layers, hidden units
    • Illustrate & Training a Perceptron
    • Important Parameters of Perceptron
    • Limitations of A Single Layer Perceptron
    • Illustrate Multi-Layer Perceptron
    • Activation function, Optimizers, Loss Functions
    • Understand Backpropagation – Using Example
  • Deep Learning with Keras
    • Define Keras
    • How to compose Models in Keras
    • Functional Composition
    • Predefined Neural Network Layers
    • What is Batch Normalization
    • Saving and Loading a model with Keras
    • Using Tensor Board with Keras
    • Use-Case Implementation with Keras
    • Intuitively building networks with Keras
  • Deep Learning with Tensorflow
    • Hello World with TensorFlow
    • Key concepts of Tensorflow
    • Implementing various types of models
    • Linear/Non-linear models
  • Convolutional Neural Networks (CNN)
    • CNN History
    • Understanding CNNs
    • CNN Application
  • Recurrent Neural Networks (RNN)
    • Intro to RNN Model
    • Long Short-Term memory (LSTM)
    • Recursive Neural Tensor Network Theory
    • Recurrent Neural Network Model

  • Unsupervised Learning
    • Restricted Boltzmann Machine
    • Collaborative Filtering with RBM
  • Auto Encoders
    • Auto Encoders
    • Deep Belief Network
  • Accelerating Deep Learning with GPU
    • Hardware Accelerated Deep Learning
    • Distributed Deep Learning
    • Deep Learning in the Cloud

  • Introduction to Computer Vision
  • OpenCV
    • Introduction to OpenCV
    • Core Functionalities
    • Image processing using OpenCV
    • Video processing using OpenCV
    • Feature Detection
    • Video Analysis
  • Computer Vision Applications
    • Concept of Transfer Learning
    • Popular Image net models
    • Object Classification
    • Object Detection
    • Object Tracking
    • Object Localization
    • Object Segmentation
  • Generative Adversarial Networks

  • Text Mining
    • NLP vs. NLU vs. NLG
    • Vectorization using Word Embedding's
    • Word2vec and Glove
  • Language Models
    • Transfer Learning in the Text Mining
    • Introduction to Popular Language Models
      • ULMFiT
      • Transformer
      • Google’s BERT
      • Transformer-XL
      • OpenAI’s GPT-2
      • ELMo
      • Flair
      • StanfordNLP
  • Language Models Application
    • Machine Translation
    • Text Classification
    • Text Segmentation
    • Sentiment Analysis
  • Build Your Own Chatbot
    • Introduction to Chatbots
    • What are chatbots?
    • Chatbots are trending
    • How chatbots work
    • Working with Intents
    • Understanding Intents
    • Working with Entities
    • Understanding Entities
    • Create Entities
    • Import and Export Entities
    • Defining the Dialog
    • Putting it all together
    • Building user-friendly chatbots
    • Implement the Dialog
    • Define Domain-Specific Intents
    • Deploying to a WordPress site
    • Add a preview and retrieve your credentials
    • Deploy your Chatbot
    • Watson Assistant in the Private Cloud

AnalytixLabs stands out from the crowd of vast analytics institutes mainly because of its practical and conceptual approach.The faculty is highly skilled with phenomenal industry experience which is displayed in every learning aspect.They would try their best to resolve every doubt you have. The course content and assignments are very well crafted which would help a lot securing a good job. AnalytixLabs is the right place if someone is looking to learn Analytics from scratch.


Prachi Khurana
(Senior Analyst, Fraud Management at CIBC)

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