ARTIFICIAL INTELLIGENCE- MACHINE LEARNING

Course Description:

Machine learning is an application of artificial intelligence (AI) that provides systems the ability to automatically learn and improve from experience without being explicitly programmed. Machine learning focuses on the development of computer programs that can access data and use it learn for themselves.

The process of learning begins with observations or data, such as examples, direct experience, or instruction, in order to look for patterns in data and make better decisions in the future based on the examples that we provide. The primary aim is to allow the computers learn automatically without human intervention or assistance and adjust actions accordingly.

Machine learning is the concept that a computer program can learn and adapt to new data without human interference. Machine learning is a field of artificial intelligence (AI) that keeps a computer’s built-in algorithms current regardless of changes in the worldwide economy.


Course Content:

GETTING STARTED
  • History & need of Python
  • Application of Python
  • Advantages of Python
  • Disadvantages of Python
  • Installing Python
  • Program structure
  • Interactive Shell
  • Executable or script files.
  • User Interface or IDE
PYTHON FUNDAMENTALS
  • Working with Interactive mode
  • Working with Script mode
  • Python Character Set
  • Python Tokens, Keywords, Identifiers, Literals, Operators
  • Variables and Assignments
  • Input and Output in Python
  • DATATYPES
  • Comments in Python
  • Docstrings
  • How Python sees variables
  • Datatypes in Python
  • Built-in datatypes
  • bool datatype
  • Sequences in Python
  • Sets
  • Literals in Python
  • Determining the datatype of a variable
  • characters in Python
  • User-defined datatypes
  • Constants in Python
  • Identifiers and Reserved words
  • Naming conventions in Python
  • OPERATORS
  • Arithmetic operators
  • Using Python interpreter as calculator
  • Assignment operators
  • Unary minus operator
  • Relational operators
  • Logical operators
  • Boolean operators
  • Membership operators
  • Identity operators
  • Operator precedence and associativity
  • Mathematical functions
  • INPUT AND OUTPUT
  • Output statements
  • Various formats of print()
  • Input statements
  • Command line arguments
  • CONTROL STATEMENTS
  • if statement
  • if … else statement
  • if … elif … else statement
  • for loop
  • Infinite loops
  • Nested loops
  • break statement
  • continue statement
  • pass statement
  • assert statement
  • return statement
  • ARRAYS
  • Creating an array
  • Importing the array module
  • Indexing and slicing on arrays
  • Types of arrays
  • Working with arrays using numpy
  • Creating arrays using linspace
  • Creating arrays using arange() function
  • Creating arrays using zeros() and ones() functions
  • Mathematical operations on arrays
  • Comparing arrays
  • Aliasing the arrays
  • Viewing and Copying arrays
  • Slicing and indexing in numpy arrays
  • Dimensions of arrays
  • Attributes of an array
  • reshape()
  • flatten()
  • Working with Multi-dimensional arrays
  • The array() function
  • ones() and zeros() functions
  • eye() function
  • reshape() function
  • Indexing in multi-dimensional arrays
  • Slicing the multi-dimensional arrays
  • Matrices in numpy
  • STRINGS AND CHARACTERS
  • Creating strings
  • Length of a string
  • Indexing in strings
  • Repeating the strings
  • Concatenation of strings
  • Checking membership
  • Comparing strings
  • Removing spaces from a string
  • Finding sub strings
  • Strings are immutable
  • Replacing a string with another string
  • Splitting and joining strings
  • Changing case of a string
  • Checking starting and ending of a string
  • String testing methods
  • Formatting the strings
  • Sorting strings
  • LISTS
  • Creating lists using range() function
  • Updating the elements of a list
  • Concatenation of two lists
  • Repetition of lists
  • Membership in lists
  • Aliasing and cloning lists
  • Methods to process lists
  • Nested lists
  • List comprehensions
  • TUPLES
  • Creating tuples
  • Accessing the tuple elements
  • Basic operations on tuples
  • Functions to process tuples
  • Nested tuples
  • DICTIONARIES
  • Operations on dictionaries
  • Dictionary methods
  • Using for loop with dictionaries
  • Sorting the elements of a dictionary using lambdas
  • Converting lists into dictionary
  • Converting strings into dictionary
  • Ordered dictionaries
  • SET
  • Creation of set objects
  • Important functions of set
  • Mathematical Operations on set
  • Membership Operators (in, not in)
  • Set Comprehension
  • FUNCTIONS
  • Built in Functions
  • User Defined Functions
  • Parameters
  • Return Statement
  • Returning Multiple values from a function
  • Types of Arguments
  • Case study
  • Types of Variables
  • Global Keyword
  • Recursive Functions
  • Anonymous Functions
  • Normal Function
  • MODULES
  • Renaming a Module at time of import
  • (Module Aliasing)
  • from…import
  • Various Possibilities of import
  • Member Aliasing
  • Reloading a Module
  • Finding members of module by using dir()
  • The special Variable__name__
  • Working with math module
  • Working with random module
  • REGULAR EXPRESSIONS
  • Sequence characters in regular expressions
  • Quantifiers in regular expressions
  • Special characters in regular expressions
  • Using regular expressions on files
  • Retrieving information from a HTML file
  • INTRODUCTION TO DATA ANALYTICS
  • Why Analytics?
  • Traditional Data Management
  • Analytical tools
  • Types of Analytics
  • Hind sight, ore sight and insight
  • Dimensions and measures
  • Why learn Python for data analysis?
  • Using the IPython notebook
  • LIBRARIES FOR DATA ANALYTICS
  • Numpy
  • Scipy
  • Pandas
  • Matplotlib
  • Seaborn
  • Scikit-learn
  • JUPYTER NOTEBOOK
  • Create Documentation
  • Code mode
  • Markdown mode
  • STATISTICS: Descriptive Statistics
  • Understanding Types of Data
    • Discrete Data
    • Continuous Data
    • Nominal Data
    • Ordinal Data
  • Experiencing Measures of Central Tendency
    • Mean
    • Median
    • Mode
  • Experiencing Measures of Dispersion
    • Range ,IQR
    • Variance
    • Standard Deviation
    • Covariance
    • Skewness
    • kurtosis
  • Confidential Intervals
  • Inferential Statistics
  • Correlation
  • Understanding Statistical Sampling
  • Understanding Statistical Inference
  • Univariate and multivariate analysis
  • What is Hypothesis Testing?
  • Null and Alternate Hypothesis Concepts
  • What is T-Value and P-Value?
  • Understanding Confidence Intervals
    • Experiencing Hypothesis Test
    • Chi-Square
    • T-Test
    • Analysis of Variance
    Data Distributions
  • Probability distribution function
  • Binomial Distributions
  • Normal Distributions
  • NUMPY
  • NumPy - Ndarray Object
  • NumPy - Data Types
  • NumPy – Array Attributes
  • NumPy - Array Creation Routines
  • NumPy - Array from Existing Data
  • Array From Numerical Ranges
  • NumPy - Indexing & Slicing
  • NumPy - Advanced Indexing
  • NumPy - Iterating Over Array
  • NumPy - Array Manipulation
  • NumPy - Binary Operators
  • NumPy - String Functions
  • NumPy - Mathematical Functions
  • NumPy - Arithmetic Operations
  • NumPy - Statistical Functions
  • Sort, Search & Counting Functions
  • NumPy - Copies &Views
  • NumPy - Matrix Library
SCIPY
  • Introduction to SciPy
  • Create function
  • modules of SciPy
  • PANDAS
  • Python Pandas – Series
  • Python Pandas –DataFrame
  • Python Pandas – Panel
  • Python Pandas – Basic Functionality
  • Function Application
  • Python Pandas –Reindexing
  • Python Pandas – Iteration
  • Python Pandas – Sorting
  • Working with Text Data
  • Options & Customization
  • Indexing & Selecting Data
  • Statistical Functions
  • Python Pandas - Window Function
  • Python Pandas - Date Functionality
  • Python Pandas –Timedelta
  • Python Pandas – Categorical Data
  • MATPLOTLIB
  • Scatter plot
  • Bar charts, histogram
  • Stack charts
  • Legend title Style
  • Figures and subplots
  • Plotting function in pandas
  • Labelling and arranging figures
  • Save plots
  • SEABORN
  • Style functions
  • Color palettes
  • Distribution plots
  • Categorical plots
  • Regression plots
  • Axis grid objects
  • MACHINE LEARNING
  • Introduction to Machine Learning
  • Applications of Machine Learning
  • Supervised Machine Learning
  • Unsupervised Machine Learning
    • Regression problems
    • Classification Problems
    • Clustering Problems
    DATA PREPROCESSING
  • Why data Preprocessing in ML?
  • Steps in Data Preprocessing in ML
    • Acquiring Data set
    • Importing Libraries
    • Import data set
    • Identifying and handling Missing Values
    • Encoding categorical Data
    • Splitting Data sets
    • Feature Scaling
    DATA TRANSFORMATIONS
  • Encoding Categorical data
    • Label Encoding
    • One Hot Encoding
    • Column Transformer
    • Dummy Variable
  • Feature Scaling
    • Min-Max Scaler
    • Standard Scaler
    REGRESSION ALGORITHMS
  • Linear Regression
  • Multi Linear Regression
  • Polynomial Regression
  • Decision Tree Regression
  • Random Forest Regression
  • Support Vector Regression
  • REGRESSION PERFORMANCE MEASURES
  • Mean Absolute Error
  • Mean Square Error
  • Root Mean Square Error
  • R2 Score
  • MODEL PERFORMANCE DEFICIENCIES
  • Under fitting
  • Over Fitting
  • Bias and variance
  • CLASSIFICATION ALGORITHMS
  • Logistic Regression
    • Introduction to Logistic Regression
    • Sigmoid Function
  • Types of Logistic Regression
    • Binary
    • Multi-Linear
    • K Nearest Neighbors (KNN)
  • Introduction to KNN
  • Applications of KNN
  • Procs and Cons of KNN
  • Decision Tree
    • Introduction to Decision Tree
    • Approaches to build Decision Tree
    • Entropy
    • Information Gain
    • Applications of Decision Tree
    • Procs and cons
    • Visualizing Decision Tree
  • Naive Bayes
    • Introduction to Naïve Bayes
    • Gaussian Naïve Bayes
    • Multinomial Naïve Bayes
    • Bernoulli’s Naïve Bayes
  • SVM
    • Introduction to SVM
    • SVM Kernels
    • Support Vectors
    • Hyper plane
    • Margin
    • Ensemble Techniques
      • Bagging (Bootstrap Aggregation)
      • Random Forest
      • Boosting
      • Applications of Random Forest
      • How Random Forest Algorithm Works
      • Decision Tree VS Random Forest
      • HyperParameter Tuning
      • Procs and cons
    PERFORMANCE MEASURES-CLASSIFICATION PROBLEMS
  • Confusion Matrix
  • Accuracy
  • Classification Report
    • Precision
    • Recall
    • F1-Score
    • Specificity
    • ML ALGORITHMS-CLUSTERING
    • K Means
      • Introduction to K-Means
      • Selecting K Clusters
      • Applications of K-Means
    • Hierarchical clustering
      • Concept of Hierarchical clustering
      • Types of Hierarchical Clustering
      • Measure of Distance (similarity)
      • Linkage Criteria
      Dimensionality reduction
    • PCA-Principal Component Analysis
    • ASSOCIATION RULE MANAGEMENT
    • Graph based Algorithm
    • Pattern based Algorithm
      • FP Growth algorithm
      • Apriori algorithm
    • Collaborative filtering
      • User based CF
      • Item based CF