Get Ahead in Your Career with the future Industry Skills- AI/ML

CERTIFICATION IN MACHINE LEARNING

Yes! I am interested

Program Objective

Machine Learning is a subset of Artificial Intelligence that deals with the creation of algorithms that can learn from data and make predictions. Machine Learning is mainly used for predictive analytics, which means using historical data to predict future trends. However, it can also be used for other purposes such as classification, clustering, and anomaly detection. There are many different types of Machine Learning algorithms, but some of the most common are linear regression, logistic regression, decision trees, and support vector machines.

Machine learning is a field of artificial intelligence that uses algorithms to learn from data and make predictions. The benefits of learning machine learning from CQS include:

If you’re thinking about pursuing a career in machine learning or are already enrolled in a machine learning program, you might be wondering what skills you’ll need to succeed. Here are some of the top skills that students learn in machine learning programs:

1. Data mining and modeling

2. Algorithm development

3. Programming languages (such as Python and R)

4. Statistical analysis

5. Data visualization

6. Machine learning platforms (such as TensorFlow and Scikit-learn)

7. Deep learning

8. Natural language processing

Who Is This Program For?

12th Pass Students, Job-seekers, Graduates of Engineering/ undergraduates, Computer Science or IT working  professionals.

Top Skills You Will Learn

Data mining and modeling, Algorithm development, Programming languages (such as Python and R), Statistical analysis, Data visualization

Job Opportunities

Data scientist, Machine learning engineer, Research scientist, Software engineer

PROGRAM OVERVIEW

Machine learning is a process of teaching computers to make decisions on their own, based on data. It’s a branch of artificial intelligence, and it’s one of the most in-demand skills in the job market today.

Duration: 2 Months
Eligibility: 12th/ BE/BTech. (All Streams) ≥ 50% BCA, BSc (CS/IT) Degree ≥ 50% PG : MCA, ME/M.Tech

  • Traditional v/s Machine Learning Programming
  • Real life examples based on ML
  • Steps of ML Programming
  • Data Preprocessing revised
  • Terminology related to ML
  • Classification
  • Regression
  • Math behind KNN
  • KNN implementation
  • Understanding hyper parameters
  • Math behind KNN
  • KNN implementation
  • Understanding hyper parameters
  • Math behind regression
  • Simple linear regression
  • Multiple linear regression
  • Polynomial regression
  • Boston price prediction
  • Cost or loss functions
  • Mean absolute error
  • Mean squared error
  • Root mean squared error
  • Least square error
  • Regularization
  • Theory of logistic regression
  • Binary and multiclass classification
  • Implementing titanic dataset
  • Implementing iris dataset
  • Sigmoid and softmax functions
  • Theory of SVM
  • SVM Implementation
  • kernel, gamma, alpha
  • Theory of decision tree
  • Node splitting
  • Implementation with iris dataset
  • Visualizing tree
  • Random forest
  • Bagging and boosting
  • Voting classifier
  • Cross validation
  • Grid and random search for hyper parameter tuning
  • Content based technique
  • Collaborative filtering technique
  • Evaluating similarity based on correlation
  • Classification-based recommendations
  • K-means clustering
  • Hierarchical clustering
  • Elbow technique
  • Silhouette coefficient
  • Dendogram
  • Install nltk
  • Tokenize words
  • Tokenizing sentences
  • Stop words customization
  • Stemming and lemmatization
  • Feature extraction
  • Sentiment analysis
  • Count vectorizer
  • Tfidfvectorizer
  • Naive bayes algorithms
  • Principal component analysis(pca)
  • Reading images
  • Understanding gray scale image
  • Resizing image
  • Understanding haar classifiers
  • Face, eyes classification
  • How to use webcam in open cv
  • Building image data set
  • Capturing video
  • Face classification in video
  • Creating model for gender prediction

DATA SCIENCE USING PYTHON

Machine learning is a process of teaching computers to make decisions on their own, based on data. It’s a branch of artificial intelligence, and it’s one of the most in-demand skills in the job market today.

Duration: 6 Months
Eligibility: 12th/ BE/BTech. (All Streams) ≥ 50% BCA, BSc (CS/IT) Degree ≥ 50% PG : MCA, ME/M.Tech

  • Why Python
  • Application areas of python
  • Python implementations
  • Cpython
  • Jython
  • Ironpython
  • Pypy
  • Python versions
  • Installing python
  • Python interpreter architecture
  • Python byte code compiler
  • Python virtual machine(pvm)
  • Using interactive mode
  • Using script mode
  • General text editor and command Window
  • Idle editor and idle shell
  • Understanding print() function
  • How to compile python program explicitly
  • Character set
  • Keywords
  • Comments
  • Variables
  • Literals
  • Operators
  • Reading input from console
  • Parsing string to int, float
  • If statement
  • If else statement
  • If else if statement
  • If else if else statement
  • Nested if statement
  • While loop
  • For loop
  • Nested loops
  • Pass, break and continue keywords
  • Int, float, complex, bool, nonetype
  • Str, list, tuple, range
  • Dict, set, frozenset
  • What is string
  • String representations
  • Unicode string
  • String functions, methods
  • String indexing and slicing
  • String formatting
  • Creating and accessing lists
  • Indexing and slicing lists
  • List methods
  • Nested lists
  • List comprehension
  • Creating tuple
  • Accessing tuple
  • Immutability of tuple
  • How to create a set
  • Iteration over sets
  • Python set methods
  • Python frozenset
  • Creating a dictionary
  • Dictionary methods
  • Accessing values from dictionary
  • Updating dictionary
  • Iterating dictionary
  • Dictionary comprehension
  • Defining a function
  • Calling a function
  • Types of functions
  • Function arguments
  • Positional arguments, keyword arguments
  • Default arguments, non-default arguments
  • Arbitrary arguments, keyword arbitrary arguments
  • Function return statement
  • Nested function
  • Function as argument
  • Function as return statement
  • Decorator function
  • Closure
  • Map(), filter(), reduce(), any() functions

Anonymous or lambda function

  • Introduction to file handling
  • File modes
  • Functions and methods related to file handling
  • Understanding with block
  • Introduction to file handling
  • File modes
  • Functions and methods related to file handling
  • Understanding with block
  • Procedural v/s object oriented programming
  • OOP principles
  • Defining a class & object creation
  • Object attributes
  • Inheritance
  • Encapsulation
  • Polymorphism
  • Difference between syntax errors and exceptions
  • Keywords used in exception handling
  • try, except, finally, raise, assert
  • Types of except blocks
  • Procedural v/s object oriented programming
  • OOP principles
  • Defining a class & object creation
  • Object attributes
  • Inheritance
  • Encapsulation
  • Polymorphism
  • Need of regular expressions
  • Re module
  • Functions /methods related to regex
  • Meta characters & special sequences
  • Introduction to tkinter programming
  • Tkinter widgets
  • Tk, label, Entry, Textbox, Button
  • Frame, messagebox, filedialogetc
  • Layout managers
  • Event handling
  • Displaying image
  • Multi-processing v/s Multi-threading
  • Need of threads
  • Creating child threads
  • Functions /methods related to threads
  • Thread synchronization and locking

Introduction to Statistics

  • Sample or population
  • Measures of central tendency
  • Arithmetic mean
  • Harmonic mean
  • Geometric mean
  • Mode
  • Quartile
  • First quartile
  • Second quartile(median)
  • Third quartile
  • Standard deviation

Probability Distributions

  • Introduction to probability
  • Conditional probability
  • Normal distribution
  • Uniform distribution
  • Exponential distribution
  • Right & left skewed distribution
  • Random distribution
  • Central limit theorem

Hypothesis Testing

  • Normality test
  • Mean test
  • T-test
  • Z-test
  • ANOVA test
  • Chi square test
  • Correlation and covariance
  • Difference between list and numpy array
  • Vector and matrix operations
  • Array indexing and slicing

Introduction to pandas

  • Labeled and structured data
  • Series and dataframe objects

How to load datasets

  • From excel
  • From csv
  • From html table

Accessing data from Data Frame

  • at &iat
  • loc&iloc
  • head() & tail()
  • describe()
  • groupby()
  • crosstab()
  • boolean slicing / query()
  • Map(), apply()
  • Combining data frames
  • Adding/removing rows & columns
  • Sorting data
  • Handling missing values
  • Handling duplicacy
  • Handling data error
  • Label Encoding
  • One Hot Encoding
  • Handling Date and Time
  • Scatter plot, lineplot, bar plot
  • Histogram, pie chart,
  • Jointplot, pairplot, heatmap
  • Outlier detection using boxplot
  • Introduction To Machine Learning

    • Traditional v/s Machine Learning Programming
    • Real life examples based on ML
    • Steps of ML Programming
    • Data Preprocessing revised
    • Terminology related to ML

    Supervised Learning

    • Classification
    • Regression

    Unsupervised Learning

    • clustering

    KNN Classification

    • Math behind KNN
    • KNN implementation
    • Understanding hyper parameters

    Performance metrics

    • Math behind KNN
    • KNN implementation
    • Understanding hyper parameters

    Regression

    • Math behind regression
    • Simple linear regression
    • Multiple linear regression
    • Polynomial regression
    • Boston price prediction
    • Cost or loss functions
    • Mean absolute error
    • Mean squared error
    • Root mean squared error
    • Least square error
    • Regularization

    Logistic Regression for classification

    • Theory of logistic regression
    • Binary and multiclass classification
    • Implementing titanic dataset
    • Implementing iris dataset
    • Sigmoid and softmax functions

    Support Vector Machines

    • Theory of SVM
    • SVM Implementation
    • kernel, gamma, alpha

    Decision Tree Classification

    • Theory of decision tree
    • Node splitting
    • Implementation with iris dataset
    • Visualizing tree

    Ensemble Learning

    • Random forest
    • Bagging and boosting
    • Voting classifier

    Model Selection Techniques

    • Cross validation
    • Grid and random search for hyper parameter tuning

    Recommendation System

    • Content based technique
    • Collaborative filtering technique
    • Evaluating similarity based on correlation
    • Classification-based recommendations

    Clustering

    • K-means clustering
    • Hierarchical clustering
    • Elbow technique
    • Silhouette coefficient
    • Dendogram

    Text Analysis

    • Install nltk
    • Tokenize words
    • Tokenizing sentences
    • Stop words customization
    • Stemming and lemmatization
    • Feature extraction
    • Sentiment analysis
    • Count vectorizer
    • Tfidfvectorizer
    • Naive bayes algorithms

    Dimensionality Reduction

    • Principal component analysis(pca)

    Open CV

    • Reading images
    • Understanding gray scale image
    • Resizing image
    • Understanding haar classifiers
    • Face, eyes classification
    • How to use webcam in open cv
    • Building image data set
    • Capturing video
    • Face classification in video
    • Creating model for gender prediction

    Projects

     

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career Opportunities After Learning Machine Learning

There are many job roles in machine learning, including:

  • Data scientist

  • Machine learning engineer

  • Research scientist

  • Data Scientist

  • Software engineer

Each of these job roles has different responsibilities, but they all involve working with data and using machine learning algorithms to build models that can learn from data. There are many career opportunities in machine learning, so if you’re interested in this field, there’s sure to be a job role that’s a perfect fit for you.

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