Python with Artificial Intelligence

• Introduction to the course 
• Fundamentals of Programming
• Python for Data Science Introduction (2 hrs to 4 hrs)
• Python, Anaconda and relevant packages installations 
• Why learn Python?
• Keywords and Identifiers
• Comments, Indentation, and Statements
• Variables and Datatypes in Python
• Standard Input and Output
• Operators
• Control flow: If...else
• Control flow: while loop
• Control flow: for loop
• Control flow: break and continue
• Python for Data Science: Data Structures (2 hrs)
• Lists
• Tuples
• Sets
• Dictionary
• Strings
• Python for Data Science: Functions (2 hrs)
• Introduction
• Types of function
• Function arguments
• Recursive functions
• Lambda functions
• Modules
• Packages
• File Handling
• Exception Handling
• Debugging Python
• Python for Data Science: Numpy (1 hr)
• Numpy Introduction
• Numerical operations on Numpy
• Python for Data Science: Matplotlib (1 hr)
• Getting started with Matplotlib
• Python for Data Science: Pandas (1 hr)
• Getting started with Pandas
• Data Frame basics
• Key Operations on Data Frames
•  Python for Data Science: Computational Complexity (1 hr)
• Space and Time Complexity: Finding largest number in the list
• Binary search
• Find element common in two lists
• SQL
• Introduction to Database
• Why SQL?
• Execution of an SQL statement
• IMDB Dataset
• Installing MySQL
• Load IMDB data
• Use, Describe, Show table
• Select
• Limit, Offset
• Order By
• Distinct
• Where, Comparison Operators, NULL
• Logic Operators
• Aggregate Functions: COUNT, MIN, MAX, AVG, SUM
• Group By
• Having
• Order of Keywords
• Join and Natural Join
• Inner, Left, Right, and Outer Joins
• Sub Queries/Nested Queries/Inner Queries
• DML: INSERT
• DML: UPDATE, DELETE
• DML: CREATE,TABLE
• DDL: ALTER, ADD, MODIFY, DROP
• DDL: DROP TABLE, TRUNCATE, DELETE
• Data Control Language: GRANT, REVOKE
• Learning Resources
• Exploratory Data Analysis and Data Visualization
• Plotting for Exploratory Data Analysis (EDA)
• Introduction to Iris dataset and 2D scatter-plot
• 3D Scatter-plot
• Pair plots
• Limitations of Pair plots
• Histogram and introduction to PDF(Probability Density Function)
• Univariate analysis using PDF
• CDF(Cumulative distribution function)
• Variance, Standard Deviation
• Median
• Percentiles and Quantiles
• IQR(InterQuartile Range), MAD(Median Absolute Deviation)
• Box-plot with whiskers
• Violin plots
• Summarizing plots, Univariate, Bivariate, and Multivariate analysis
• Multivariate probability density, contour plot
• Probability and Statistics
• Introduction to Probability and Statistics
• Population & Sample
• Gaussian/Normal Distribution and its PDF(Probability Density Function)
• CDF(Cumulative Density Function) of Gaussian/Normal Distribution
• Symmetric distribution, Skewness, and Kurtosis
• Standard normal variate (z) and standardization
• Kernel density estimation
• Sampling distribution & Central Limit Theorem
• Q-Q Plot: Is a given random variable Gaussian distributed?
• How distributions are used?
• Chebyshev’s inequality
• Discrete and Continuous Uniform distributions
• How to randomly sample data points. [Uniform Distribution]
• Bernoulli and Binomial distribution
• Log-normal
• Power law distribution
• Box-Cox transform
• Application of Non-Gaussian Distributions?
• Co-variance
• Pearson Correlation Coefficient
• Spearman Rank Correlation Coefficient
• Correlation vs Causation
• How to use Correlations?
• Confidence Intervals(C.I) Introduction
• Computing confidence-interval has given the underlying distribution
• C.I for the mean of a normal random variable
• Confidence Interval using bootstrapping
• Hypothesis Testing methodology, Null-hypothesis, p-value
• Hypothesis testing intuition with coin toss example
• Resampling and permutation test
• K-S Test for the similarity of two distributions
• Code Snippet K-S Test
• Hypothesis Testing: another example
• Resampling and permutation test: another example
• How to use Hypothesis testing?
• Proportional Sampling
• Dimensionality reduction and Visualization
• What is dimensionality reduction?
• Row vector, and Column vector
• How to represent a dataset?
• How to represent a dataset as a Matrix
• Data preprocessing: Feature Normalization
• Mean of a data matrix
• Data preprocessing: Column Standardization
• Co-variance of a Data Matrix
• MNIST dataset (784 dimensional)
• Code to load MNIST data set
• Principal Component Analysis
• Why learn it.
• Geometric intuition
• Mathematical objective function
• Alternative formulation of PCA: distance minimization
• Eigenvalues and eigenvectors
• PCA for dimensionality reduction and visualization
• Visualize MNIST dataset
• Limitations of PCA
• Code example
• PCA for dimensionality reduction (not-visualization)
• T-distributed stochastic neighborhood embedding (t-SNE)
• What is t-SNE?
• Neighborhood of a point, Embedding
• Geometric intuition
• Crowding problem
• How to apply t-SNE and interpret its output (distill.pub)
• t-SNE on MNIST
• Code example
• Foundations of Machine Learning
• Classification and Regression Models: K-Nearest Neighbors
• Classification algorithms in various situations
• Performance measurement of models
• Naive Bayes
• Logistic Regression
• Linear Regression
• Solving optimization problems
• Machine Learning- II (Supervised Learning Models)
• Support Vector Machines (SVM)
• Decision Trees
• Ensemble Models
• Data Mining(Unsupervised Learning)
• Unsupervised learning/Clustering
• What is Clustering?
• Unsupervised learning
• Applications
• Metrics for Clustering
• K-Means: Geometric intuition, Centroids
• K-Means: Mathematical formulation: Objective function
• K-Means Algorithm
• How to initialize: K-Means++
• Failure cases/Limitations
• K-Medoids
• Determining the right K
• Time and Space complexity
• Hierarchical clustering Technique
• Agglomerative & Divisive, Dendrograms
• Agglomerative Clustering
• Proximity methods: Advantages and Limitations
• Time and Space Complexity
• Limitations of Hierarchical Clustering
• Code sample
• DBSCAN (Density based clustering)
• Density based clustering
• MinPts and Eps: Density
• Core, Border and Noise points
• Density edge and Density connected points
• DBSCAN Algorithm
• Hyper Parameters: MinPts and Eps
• Advantages and Limitations of DBSCAN
• Time and Space Complexity
• Code samples
• Case Study 1
• Case Study 2