What you will learn:

By the end of this AI training, you will be able to accomplish the following:

• Understand the meaning, purpose, scope, stages, applications, and effects of Artificial Intelligence
• Design and build your own intelligent agents, applying them to create practical Artificial Intelligence projects, including games, machine learning models, logic constraint satisfaction problems, knowledge-based systems, probabilistic models, and agent decision-making functions
• Master the essential concepts of Python programming, including data types, tuples, lists, dicts, basic operators, and functions
• Learn how to write your own Python scripts and perform basic hands-on data analysis using Jupyter notebook
• Gain an in-depth understanding of Data Science processes: data wrangling, data exploration, data visualization, hypothesis building, and testing
• Perform high-level mathematical and technical computing using the NumPy and SciPy packages and data analysis with the Pandas package
• Master the concepts of supervised and unsupervised learning models, including linear regression, logistic regression, clustering, dimensionality reduction, K-NN and pipeline, recommendation engine, and time series modeling
• Understand the concepts of TensorFlow, its main functions, operations, and the execution pipeline
• Master advanced topics in Artificial Intelligence, such as convolutional neural networks, recurrent neural networks, training deep networks, and high-level interfaces

Course content

Introduction to Python

• Concepts of Python programming
• Configuration of Development Environment
• Variable and Strings
• Functions, Control Flow and Loops
• Tuple, Lists and Dictionaries
• Standard Libraries

Data Science Fundamentals

• Introduction to Data Science
• Real world use-cases of Data Science
• Walkthrough of data types
• Data Science project lifecycle

 Introduction to NumPy

• Basics of NumPy Arrays
• Mathematical operations in NumPy
• NumPy Array manipulation
• NumPy Array broadcasting

Data Manipulation with Pandas

• Data Structures in Pandas-Series and Data
• Frames
• Data cleaning in Pandas
• Data manipulation in Pandas
• Handling missing values in datasets
• Hands-on: Implement NumPy arrays and
• Pandas Data Frames

Data Visualization in Python

• Plotting basic charts in Python
• Data visualization with Matplotlib
• Statistical data visualization with Seaborn
• Hands-on: Coding sessions using Matplotlib,
• Seaborn package

Exploratory Data Analysis

• Introduction to Exploratory Data Analysis (EDA) steps
• Plots to explore relationship between two variables
• Histograms, Box plots to explore a single variable
• Heat maps, Pair plots to explore correlations

Introduction to Machine Learning

• What is Machine Learning?
• Use Cases of Machine Learning
• Types of Machine Learning – Supervised to Unsupervised methods
• Machine learning workflow

Linear Regression

• Introduction to Linear Regression
• Use cases of Linear Regression
• How to fit a Linear Regression model?
• Evaluating and interpreting results from Linear Regression models
• Predict Bike sharing demand

Logistic Regression

• Introduction to Logistic Regression
• Logistic Regression use cases
• Understand use of odds & Logic function to perform logistic regression
• Predicting credit card default cases

Decision Trees & Random Forest

• Introduction to Decision Trees & Random Forest
• Understanding criterion (Entropy & Information Gain) used in Decision Trees
• Using Ensemble methods in Decision Trees
• Applications of Random Forest

Model Evaluation Techniques

• Introduction to evaluation metrics and model selection in Machine Learning
• Importance of Confusion matrix for predictions
• Measures of model evaluation – Sensitivity, specificity, precision, recall & f-score
• Use AUC-ROC curve to decide best model

Dimensionality Reduction using PCA

• Introduction to Curse of Dimensionality
• What is dimensionality reduction?
• Technique used in PCA to reduce dimensions
• Applications of Principle component Analysis (PCA)
• Optimize model performance using PCA on SPECTF heart data

KNearestNeighbours

• Introduction to KNN
• Calculate neighbours using distance measures
• Find optimal value of K in KNN method
• Advantage & disadvantages of KNN

Naive Bayes Classifier

• Introduction to Naïve Bayes Classification
• Refresher on Probability theory
• Applications of Naive Bayes Algorithm in Machine Learning
• Classify spam emails based on probability

K-means Clustering

• Introduction to K-means clustering
• Decide clusters by adjusting centroids
• Understand applications of clustering in Machine Learning
• Segment hands in Poker data

Support Vector Machines

• Introduction to SVM
• Figure decision boundaries using support vectors
• Identify hyper plane in SVM
• Applications of SVM in Machine Learning

Time Series Forecasting

• Components of time series data
• Interpreting autocorrelation & partial autocorrelation functions
• Introduction to Time Series analysis
• Stationary vs non stationary data
• Stationary data and implement ARIMA model

Apriori Algorithm

• Applications of Apriori algorithm
• Understand Association rule
• Developing product recommendations using association rules
• Analyse online retail data using association rules

Recommendation Systems

• Introduction to Recommender systems
• Types of Recommender systems – collaborative, content based & Hybrid
• Types of similarity matrix (Cosine, Jaccard, Pearson correlation)
• Build Recommender systems on Movie data using KNN basics

Linear Discriminant Analysis

• Recap of dimensionality reduction concepts
• Types of dimensionality reduction
• Dimensionality reduction using LDA
• Apply LDA to determine Wine Quality

Anomaly Detection

• Introduction to Anomaly detection
• How Anomaly detection works?
• Types of Anomaly detection: Density based, Clustering etc. NET Based Commands
• Detect anomalies on electrocardiogram data

Ensemble Learning

• Introduction to Ensemble Learning
• What are Bagging and Boosting techniques?
• What is Bias variance trade off?
• Predict wage (annual income) classes from adult census data

Optimization

• Optimization techniques: Linear Programming using Excel solver
• Optimization in ML
• Neural networks
• Workflow of a Neural network & analogy with biological neurons

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