Artificial Intelligence Training
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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.
The primary and overall objective of this course is to give a hands-on experience to devlop AI & ML Model.
Introduction to Python, Datatypes ,variables.
Lists,Tuple,Dictionary,Sets,Strings, Control Statements, Compression, Functions
Modules ,import,pip in python, Python Date and Math, Python Files
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
Operations on NumPy Arrays- Intro, Basic Operations, Operations on Arrays, Basic Linear Algebra Operations
Assignment on Python & Numpy Basics
Pandas - Introduction, Pandas Basics, Indexing and Selecting Data, Merge and Append
Grouping and Summarizing Dataframes, Lambda function & Pivot tables, Data Extraction
Reading Delimited and Relational Databases, Reading Data From Websites, Getting Data From APIs, Reading Data From PDF Files, Cleaning Datasets
Assignment on Pandas & Data Extraction
Data Visualization- Intro, Basic Visualization, Different char Types, Data Visualisation Toolkit, Components of a Plot, Sub-Plots, Functionalities of Plots
Plotting Data Distributions- Introduction, Univariate Distributions, Univariate Distributions - Rug Plots, Bivariate Distributions, Bivariate Distributions - Plotting Pairwise Relationships
Plotting Categorical and Time-Series Data- Plotting Distributions Across Categories, Plotting Aggregate Values Across Categories, Time Series Data
Assignment On Data Visualization and Matplotlib
SQL - Introduction, An introduction to RDBMS and SQL, Data Retrieval with SQL, Compound Functions and Relational Operators, Pattern Matching with Wildcards, Basics of Sorting
Analytics and Problem Solving
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
Inferential Statistics- Basics of Probability, Discrete Probability Distributions, Continuous Probability Distributions, Central Limit Theorem, Applications of Sampling, Methods Concepts of Hypothesis Testing
EDA-Exploratory Data Analysis:Data Sourcing, Data Cleaning, Univariate Analysis, Segmented Univariate, Bivariate Analysis, Derived Metrics.
Assignment On EDA
Introduction, definition and history of Supervisory Control and Data Acquisition,
Introduction to Simple Linear Regression: Course Overview, Introduction, Introduction to Machine Learning, Regression Line, Best Fit Line, Strength of Simple Linear Regression,
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.
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
Project - Linear Regression_1
Univariate Logistic Regression: Introduction: Univariate Logistic Regression, Binary Classification, Sigmoid Curve, Finding the Best Fit Sigmoid Curve, Odds and Log Odds.
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
Parameterisation, Checking Load, rated Voltage & current
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.
Project – Logistic Regression
Bayes’ Theorem and Its Building Blocks: Introduction: Naive Bayes, Conditional Probability and Its Intuition, Bayes' Theorem.
Naive Bayes For Categorical Data: Introduction, Naive Bayes -With One Feature, Comprehension, Conditional Independence in Naive Bayes, Deciphering Naive Bayes.
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.
Principles of Model Selection: Introduction to Model Selection, Model and Learning Algorithm, Simplicity, Complexity and Overfitting, Bias-Variance Tradeoff, Regularization
Regularized Regression: Introduction, Regularized Regression, Ridge and Lasso Regression, Ridge and Lasso Regression in Python, Model Selection Criteria
Assignment – Advanced Regression
SVM - Maximal Margin Classifier: Introduction, Concept of a Hyperplane in 2D, Concept of a Hyperplane in 3D, Maximal Margin Classifier
SVM - Soft Margin Classifier: Introduction, The Soft Margin Classifier, The Slack Variable, Cost of Misclassification, SVM Python Lab
Kernels: Introduction to Kernels, Mapping Nonlinear Data to Linear Data, Feature Transformation, The Kernel Trick
Introduction to Decision Trees: Introduction to Decision Trees, Interpreting a Decision Tree, Decision Tree Classification in Python, Regression with Decision Trees
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
Random Forests: Introduction, Ensembles, Creating a Random Forest, OOB (Out-of-Bag) Error, Random Forests Python
Boosting and AdaBoost: Introduction, Introduction to Boosting, Weak Learners
Gradient Boosting: Introduction, Understanding Gradient Boosting, Gradient in Gradient Boosting, Gradient Boosting Algorithm, XGBoost
Project : Boosting and Bagging Python Lab
Introduction to Clustering: Introduction, Understanding Clustering, Practical Example of Clustering - Customer Segmentation K Means Clustering: Introduction, Steps of the Algorithm, K Means Algorithm
Hierarchical Clustering: Introduction, Hierarchical Clustering Algorithm, Interpreting the Dendrogram, Types of Linkages, Cutting the Dendrogram & Analyzing the Clusters
Fundamentals of PCA: PCA in Python, Introduction, Applying PCA using Python, Scree Plots, Dimensionality Reduction, Improving Model Performance
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
Assignment on Neural Networks
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
Building CNNs with Python and Keras: Introduction, Building CNNs in Keras - MNIST
Project: Facial Expression Recognition with CNN