AI and ML BootCamp

Data Analytics across Domains

• Insurance, Automobile, Retail , Banking, Retial, Marketing, Aviation, Defence, Social Services, Computer Vision

What is Analytics?

• Insights, Reports, Historical Performance, Trend, Visualization

Types of Analytics

• Descriptive, Diagnostic, Predictive, Prescriptive, Exploratory

AI vs ML vs DL vs DS

• Differnece between AI, Machine Learning, Deep Learning and Data Science

Lab

Introduction to statistics

• Undertand difference between Population vs Sample, importance of statistical concepts in data science and ML models

Central Limit Theorem

• Know the foundation principal in statistics – Central Limit Theorem

Measures of Central Tendancies

• Understand the importance of Mean, Medium, Mode of a variable

Measures of Spread

• Understand the importance of Variance, Standard Deviation of a variable

Measuring Scales

• Different scales of measuring data – Nominal, Ordinal, Interval, Ratio

Descriptive Statistics

• Application of central tendencies for data analysis

Inferential Statistics

• Usage of correlation, regression concepts for data analysis

Lab

Types of Distribution

• Understand different types of data distribuitions – Uniform, Binomial, Poisson, Normal, Logarithmic, ExponentialHypoth

Hypothesis Testing

• Learn to Perform Null Hypothesis and p-value to find the significant variables

Statistical Tests

• Learn to perform t-test, z-test to measure the variance between the means of two samples or population

Analysis of Variance

• Learn techniques like ANOVA (1-way, 2-way, w/o replication), ANCOVA, f-test to compare the variance betweeen variables

Goodness of Fit test

• Perform Chi-square test to evaluate distribution of sample same as expected population under study

Probability Theory for Data Analytics

• Introduction to probability
• Types of events
• Marginal Probability
• Baye’s Theorem

Lab

Python Fundamentals and Programming

• What is Python?
• Why is Python essential for Data Science?
• Versions of Python
• How to install Python
• Anaconda Distribution
• How to use Jupyter Notebooks
• Command line basics
• GitHub overview
• How to execute Python scripts from command line
• Python Data Types
• Programming Concepts
• Python, Operators
• Conditional Statement, Loops
• Lists, Tuples, Dictionaries, Sets
• Methods and Functions
• Errors and Exception Handling
• Object Oriented Programming in Python
• Modules and Packages

Data Handling with NumPy and Pandas

• NumPy overview
• Arrays & Matrices
• NumPy basic operations, functions
• Data Visualization with MatplotLib
• Why visualize data?
• Importing MatplotLib
• Chart: Line Chart, Bar Charts and Pie Charts
• Plotting from Pandas object
• Object Oriented Plotting: Setting axes limits and ticks
• Multiple Plots
• Plot Formatting: Custom Lines, Markers, Labels, Annotations, Colors

Advanced Data Visualization with Seaborn

• Importing Seaborn
• Seaborn overview
• Distribution and Categorical Plotting
• Matrix plots & Grids
• Regression Plots
• Style & Color
• Review Session

Lab

Introduction To Data Science

• Key Terms in Data Science
• Introduction to Supervised Learning,Unsupervised Learning
• What is Reinforcement Learning?
• Regression
• Classification

End to End Data Science

• Data Science Life Cycle
• Data Science in cloud

Reading data from different Sources

• Structured
• Unstructuted
• Cloud

Exploratory Data Analysis

• Univariate
• Bivariate
• Multivariate

Data Science: Data Cleaning Feature Engineering

• Missing Values
• Outliers treatment
• imbalance Data Handeling
• Standardization / Normalization
• Project1

Data Science Fundamentals

• Data Science Library
• Scikit learn

Lab

Regression and Classification Algorithms:

• Linear Regression
• Understanding Regression
• Introduction to Linear Regression
• Linear Regression with Multiple Variables
• Disadvantage of Linear Models
• Interpretation of Model Outputs
• Assumption of Linear Regression
• Project 2: Predict Sales Revenue Using Multiple Regression Model

Logistics regression

• Understanding classification
• Introduction to Logistic Regression.
• Odds Ratio
• Logit Function/ Sigmoid Function
• Cost function for logistic regression
• Application of logistic regression to multi-class classification.
• Assumption in Logistics Regression
• Evaluation Matrix : Confusion Matrix, Odd’s Ratio And ROC Curve
• Advantages And Disadvantages of Logistic Regression.
• Project 3: Advertisement indicating whether or not a particular internet user clicked on an Advertisement on a company website.

Decision Trees And Ensamble Methods

• Understanding Decision Tree
• Building Decision Tree
• Using ID3 / Entropy
• CART model – Gini index
• Stopping Criteria And Pruning
• Hyperparameter Tunning for Decision Tree
• overfitting Problem
• Tradeoff between bias and variance
• Ensamble methods
• BaggingBoostingRandom Forest
• Grid Serach CV
• Hyperparameter Tunning for Random forest
• Feature inmportance
• Project 4: Cardiovascular Disease prediction

Naive Bayes

• Conditional Probability
• Bayes Theorem
• Building model using Naive Bayes
• Naive Bayes Assumption
• Laplace Correction
• NLP with Naive Bayes
• Project 5: Sentiment Analysis

Support Vector Machine ( SVM)

• Basics of SVM
• Margin Maximization
• Kernel Trick
• RBF / Poly / Linear
• Project 6:Wine Quality Prediction

k-Nearest Neighbors (KNN)

• Distance as Calssifier
• Euclidean Distance
• Manhattan Distance
• KNN Basics
• KNN for Regression & Classification
• Project7: Predicting diabetics in a person using KNN algorithm
• Lab

Hierarchical Clustering

• Clustering Methods
• Agglomerative Clustering
• Divisive Clustering
• Dendogram
• Project 8

K Means

• Basics of KMeans
• Finding value of optimal K
• Elbow Method
• Silhouette Method
• Project 9

Principal Component Analysis(PCA)

• Eigenvalues and Eigenvectors
• Orthogonal Transformation
• Using PCA
• Project 10
• Lab

Artificial Intelligence

Neural Networks using Tensors and Keras

• The Neuron Diagram
• Neuron Models & Neural Network step function
• Functioning of Neurons Activation functions Gradient Descent, Stochastic Descent, ramp function, sigmoid function, Gaussian function
• Perceptron, multilayer network, backpropagation, introduction to deep neural network Installing Libraries
• Creating ANN Python Training the model
• Basics of Tensor Flow
• Basics of Keras

Project: Convolutional Neural Networks (CNN)

• Introduction to OpenCV
• Basics of Image Processing
• Learning Basic Image manipulations
• CNN: Introduction to terms and terminologies
• Math behind the algorithm
• CNN using Keras: Building CNN for Image Classification
• Convolution Operation Pooling,Flattening Building a CNN using Python Training the model
• Project : Building Face Detecting Model

Recurrent Neural Networks

• Introduction to RNN
• Sequence prediction of RNN

ProjectLong short-term memory (LSTM)

• Introduction to LSTM
• Sequence prediction using LSTM
• Project
• Lab

Natural Language Processing Basics

• Basics of NLP
• Removing Stop Words
• Stemming & lemmatization
• Parts of speech tagging
• TFIDF vectorizer
• Senmiment Analysis
• Word Embeddings and Topic Models
• Project
• Lab

• Capstone Project: Choose a Capstone Project from your desired domain. Focuses on the end-to-end implementation of the whole course.

Subtopics:

Foundation & Personal Branding

• Career Vision & Mapping
• Resume Mastery
• LinkedIn Optimisation
• Portfolio & GitHub showcase

Job Market Readiness

• Job search strategy
• Mock Interviews (Behavioural)
• Mock Interviews (Technical)