Artificial Intelligence Training

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    Tutor: Technologics
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  • Course Code: PGAI
  • Affilaited: Govt Of India
  • Duration: 180 Days
  • Projects : Yes
  • Skill Level: All
  • Languages: English
  • Softskill: Mandatory
  • Assessments: Self
  • Regular Batch: Mo - Fr
  • Weekend Batch: Sat & Sun

Description

This Artificial Intelligence course You will master TensorFlow, Machine Learning, and other AI concepts, plus the programming languages needed to design intelligent agents, deep learning algorithms & advanced artificial neural networks that use predictive analytics to solve real-time decision-making problems.

Course Objectives

  • Complete knowledge of AI & ML.
  • Able to Program, Test & Debug of Software.
  • Able to develop algorithm in Deep Learning, Neural Network & Tenser Flow.
  • Real-time Projects Execution.

 

Training Highlights

  • 100% Hands-On Practical Oriented Training
  • Industrial experienced faculties
  • Fully Equipped Updated Software and Hardware facility
  • 100% Placement Assistance Track record Till Date
  • Individual Focus
  • Frequent accessibility to Industrial leadership
  • New generation Hardware
  • Free Soft Skills Training
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    Certification

    The primary and overall objective of this course is to give a hands-on experience to devlop AI & ML Model.

    • Lecture 1.1:

      Introduction to Python, Datatypes ,variables.

    • Lecture 1.2:

      Lists,Tuple,Dictionary,Sets,Strings, Control Statements, Compression, Functions

    • Lecture 1.3:

      Modules ,import,pip in python, Python Date and Math, Python Files

    • Lecture 1.4:

      NumPy Basics

    • Lecture 1.5:

      Creating NumPy Arrays, Structure and Content of Arrays, Subset, Slice, Index and Iterate through Arrays, Multidimensional Arrays, Computation Times in NumPy and Standard Python Lists

    • Lecture 1.6:

      Operations on NumPy Arrays- Intro, Basic Operations, Operations on Arrays, Basic Linear Algebra Operations

    • Lecture 1.7:

      Assignment on Python & Numpy Basics

    • Lecture 1.8:

      Pandas - Introduction, Pandas Basics, Indexing and Selecting Data, Merge and Append

    • Lecture 1.9:

      Grouping and Summarizing Dataframes, Lambda function & Pivot tables, Data Extraction

    • Lecture 2.0:

      Reading Delimited and Relational Databases, Reading Data From Websites, Getting Data From APIs, Reading Data From PDF Files, Cleaning Datasets

    • Lecture 2.1:

      Assignment on Pandas & Data Extraction

    • Lecture 2.2:

      Data Visualization- Intro, Basic Visualization, Different char Types, Data Visualisation Toolkit, Components of a Plot, Sub-Plots, Functionalities of Plots

    • Lecture 2.3:

      Plotting Data Distributions- Introduction, Univariate Distributions, Univariate Distributions - Rug Plots, Bivariate Distributions, Bivariate Distributions - Plotting Pairwise Relationships

    • Lecture 2.4:

      Plotting Categorical and Time-Series Data- Plotting Distributions Across Categories, Plotting Aggregate Values Across Categories, Time Series Data

    • Lecture 2.5:

      Assignment On Data Visualization and Matplotlib

    • Lecture 2.6:

      SQL - Introduction, An introduction to RDBMS and SQL, Data Retrieval with SQL, Compound Functions and Relational Operators, Pattern Matching with Wildcards, Basics of Sorting

    • Lecture 2.7:

      Analytics and Problem Solving

    • Lecture 2.8:

      CRISP-DM Framework- Introduction, Define the Business Problem - Business Understanding, Owning an IPL Team - Business Understanding, Understanding Raw Data, Preparing Data for Analysis, The Heart of Data Analysis: Modelling

    • Lecture 2.9:

      Inferential Statistics- Basics of Probability, Discrete Probability Distributions, Continuous Probability Distributions, Central Limit Theorem, Applications of Sampling, Methods Concepts of Hypothesis Testing

    • Lecture 3.0:

      EDA-Exploratory Data Analysis:Data Sourcing, Data Cleaning, Univariate Analysis, Segmented Univariate, Bivariate Analysis, Derived Metrics.

    • Lecture 3.1:

      Assignment On EDA

    • Lecture 3.2:

      Introduction, definition and history of Supervisory Control and Data Acquisition,

    • Lecture 3.3:

      Machine Learning

    • Lecture 3.4:

      Introduction to Simple Linear Regression: Course Overview, Introduction, Introduction to Machine Learning, Regression Line, Best Fit Line, Strength of Simple Linear Regression,

    • Lecture 3.5:

      Simple Linear Regression in Python: Introduction, Assumptions of Simple Linear Regression, Reading and Understanding the Data, Hypothesis Testing in Linear Regression, Building a Linear Model, Residual Analysis and Predictions, Linear Regression using SKLearn.

    • Lecture 3.6:

      Multiple Linear Regression: Introduction, Motivation: When One Variable isn't Enough, Moving from SLR to MLR: New Considerations, Multicollinearity, Dealing with Categorical Variables, Model Assessment and Comparison, Feature Selection

    • Lecture 3.7:

      Project - Linear Regression_1

    • Lecture 3.8:

      Logistic Regression:

    • Lecture 3.9:

      Univariate Logistic Regression: Introduction: Univariate Logistic Regression, Binary Classification, Sigmoid Curve, Finding the Best Fit Sigmoid Curve, Odds and Log Odds.

    • Lecture 4.0:

      Multivariate Logistic Regression - Model Building: Introduction, Multivariate Logistic Regression - Telecom Churn Example, Data Cleaning and Preparation, Building your First Model, Feature Elimination using RFE, Confusion Matrix and Accuracy, Manual Feature Elimination

    • Lecture 4.1:

      Parameterisation, Checking Load, rated Voltage & current

    • Lecture 4.2:

      Multivariate Logistic Regression - Model Evaluation: Introduction, Metrics Beyond Accuracy: Sensitivity & Specificity, Sensitivity & Specificity in Python, ROC Curve, ROC Curve in Python, Finding the Optimal Threshold, Model Evaluation Metrics - Exercise, Precision & Recall, Making Predictions.

    • Lecture 4.3:

      Project – Logistic Regression

    • Lecture 4.4:

      Bayes’ Theorem and Its Building Blocks: Introduction: Naive Bayes, Conditional Probability and Its Intuition, Bayes' Theorem.

    • Lecture 4.5:

      Naive Bayes For Categorical Data: Introduction, Naive Bayes -With One Feature, Comprehension, Conditional Independence in Naive Bayes, Deciphering Naive Bayes.

    • Lecture 4.6:

      Naive Bayes for Text Classification: Introduction - Naive Bayes for Text Classification, Document Classifier - Pre Processing Steps, Document Classifier - Worked out Example, Laplace Smoothing, Bernoulli Naive Bayes, SMS Spam Ham Classifier : Multinomial and Bernoulli.

    • Lecture 4.7:

      Principles of Model Selection: Introduction to Model Selection, Model and Learning Algorithm, Simplicity, Complexity and Overfitting, Bias-Variance Tradeoff, Regularization

    • Lecture 4.8:

      Regularized Regression: Introduction, Regularized Regression, Ridge and Lasso Regression, Ridge and Lasso Regression in Python, Model Selection Criteria

    • Lecture 4.9:

      Assignment – Advanced Regression

    • Lecture 5.0:

      SVM - Maximal Margin Classifier: Introduction, Concept of a Hyperplane in 2D, Concept of a Hyperplane in 3D, Maximal Margin Classifier

    • Lecture 5.1:

      SVM - Soft Margin Classifier: Introduction, The Soft Margin Classifier, The Slack Variable, Cost of Misclassification, SVM Python Lab

    • Lecture 5.2:

      Kernels: Introduction to Kernels, Mapping Nonlinear Data to Linear Data, Feature Transformation, The Kernel Trick

    • Lecture 5.3:

      Project: SVM

    • Lecture 5.4:

      Introduction to Decision Trees: Introduction to Decision Trees, Interpreting a Decision Tree, Decision Tree Classification in Python, Regression with Decision Trees

    • Lecture 5.5:

      Algorithms for Decision Tree Construction: Introduction, Concept of Homogeneity, Gini Index, Entropy and Information Gain, Splitting by R-squared, Truncation and Pruning, Tree Truncation, Tree Pruning, Building Decision Trees in Python, Hyperparameters in Python

    • Lecture 5.6:

      Random Forests: Introduction, Ensembles, Creating a Random Forest, OOB (Out-of-Bag) Error, Random Forests Python

    • Lecture 5.7:

      Boosting and AdaBoost: Introduction, Introduction to Boosting, Weak Learners

    • Lecture 5.8:

      Gradient Boosting: Introduction, Understanding Gradient Boosting, Gradient in Gradient Boosting, Gradient Boosting Algorithm, XGBoost

    • Lecture 5.9:

      Project : Boosting and Bagging Python Lab

    • Lecture 6.0:

      Introduction to Clustering: Introduction, Understanding Clustering, Practical Example of Clustering - Customer Segmentation K Means Clustering: Introduction, Steps of the Algorithm, K Means Algorithm

    • Lecture 6.1:

      Hierarchical Clustering: Introduction, Hierarchical Clustering Algorithm, Interpreting the Dendrogram, Types of Linkages, Cutting the Dendrogram & Analyzing the Clusters

    • Lecture 6.2:

      Fundamentals of PCA: PCA in Python, Introduction, Applying PCA using Python, Scree Plots, Dimensionality Reduction, Improving Model Performance

    • Lecture 6.3:

      Deep Learning

    • Lecture 6.3:

      Structure of Neural Networks: Introduction, Neural Networks - Inspiration from the Human Brain, Introduction to Perceptron, Binary Classification using Perceptron, Perceptrons - Training, Multiclass Classification using Perceptrons, Working of a Neuron, Activation Functions, Feed Forward in Neural Networks, Backpropagation in Neural Networks, Modifications to Neural Networks, Loss Function, Minibatch Gradient Descent, Gradient Descent, Momentum based methods, Dropouts -The Bayesian Approach, Vanishing and Exploding Gradients, Initializations, Dropouts, Batch Normalization

    • Lecture 6.4:

      Assignment on Neural Networks

    • Lecture 6.5:

      Introduction to Convolutional Neural Networks: Introduction, A Specialised Architecture for Visual Data, Applications of CNNs, Understanding the Visual System of Mammals, Introduction to CNNs, Reading Digital Images, Feature Maps, Pooling

    • Lecture 6.6:

      Building CNNs with Python and Keras: Introduction, Building CNNs in Keras - MNIST

    • Lecture 6.7:

      Project: Facial Expression Recognition with CNN

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