Artificial Intelligence Training

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