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MACHINE LEARNING

Machine Learning is a science of using algorithms to make pradictions based on previous abservations which enables businesses to make data driven decisions.

Core Skills

Python, Numpy, Advanced Mathematics, Neural Networks, Supervised / Unsupervised Learning

Additional Skills

Programming

Data Structures

SQL

Hands on Project

Technical Aptitude

Mathematical Aptitude

Logical Reasoning

Verbal Reasoning

Non-verbal Reasoning

Case Studies

Campus to Corporate & Business Etiquettes

Overview

Coding Assignments

Coding Assignments

Assignments help to Test

skills by Solving Practical

problems and gain confidence

for industry projects.

Total Duration

Total Duration

22 weeks of Learning includes

live Interaction with Experienced

faculty and significantly Improves

Technical skills and capability.

Effort

Effort

8 Hrs per Week of effort

to Reflect and Assimilate

the Classroom Learning and

Solve Coding Problems.

Key Highlights

Key Highlights

Live Classroom Session with focus

on Interaction. Convenience and

Flexibilty - Career Advancement

and 100% Placement.

Syllabus

Machine Learning

MACHINE LEARNING COURSE CONTENT

 

Basics of Python :

 

* Keywords and Identifiers

* Comments, Indentations, and Statements

* Variables and Data Types in Python

* Standard Input and Output

* Operators Control Flow: If Else

* Control Flow: White Loop

* Control Flow: For Loop, Control Flow: Break and Continue

* Lists. Tuples Part

* Tuples Part 2 : Sets

* Dictionary Strings

* Types of Functions and Function Arguments

* Recursive Functions, Lambda Functions, Modules

* Packages, File Handling

* Exception Handling, Debugging Python

 

Numpy :

 

* Numpy Introdution

* Numerical Operations on Numpy

 

Exploratory Data Analysis :

 

* Need and use of EDA

* Exploring the IRIRS Dataset

* 2D Scatter Plot/p>

* 3D Scatter Plot

* Pair Plots . Histogram

* PDF, Univariate Analysis using PDF

* CDF - Cumulative Distributive Function

* Mean, Variance, and Standard Deviation, Median

* Percentiles and Quantiles IQR ( Inter Quartile Range) and

* MAD ( Mean Absolute Deviation)

* Box- Plot with Whiskers, Violin Plots

* Univariate, Bivariate and Multi-Variate Analysis

* Multivariate Probability Density, Contour Plot

 

Review of Linear Algebra required for Machine Learning

 

Review of Probability and Statistics required for Machine Learning

 

Dimensionality Reduction and Visualisation :

 

* Introduction to Dimensionality reduction

* Representing Datasets using Row and Column Vectors

* Representation of Datasets as a Matrix

* Data Pre Processing - Feature Normalisation

* Mean of Data Matrix

* Column Standardization

* Co-Variance of Data Matrix

* PCA, PCA with a Code Example

 

Principle Component Analysis :

 

* Introduction and use of PCA, Geometric Intuition of PCA

* The mathematical objective function of PCA

* Distance Minimization

* Eigen Values and Eigen Vectors ( PCA): Dimensionality Reduction

* PCA for Dimensionality Reduction and Visualization

* Limitation of PCA and PCA with a Code Example

* Supervised Learning

 

Linear Regression :

 

* Geometric Intuition of Logical Regression

* Squashing using Sigmoid Function

* Objective Function mathematical formulation

* Weight Vector

* L2 Regularization: Overfitting and Underfitting

* L1 Regularization and sparsity

* Probabilistic Interpretation: Gaussian Naive Bayes Loss minimization representation

* Hyperparameter Search: Grid Search and Random Search

* Column Standardization

* Feature Importance and Model Interpretability

* Collinearity of Features

* Test/Run Time Space and Time Complexity

* Real World Cases

* Non-Linearly separable data and Feature Engineering

 

Logistic Regression :

 

* GridSearchCV, RandomSearchCV

* Extensions to Logistic Regression: Generalised Linear Models

 

Neural Networks :

 

* Working of Biological Neurons

* Growth of Biological Neural Networks

* Diagrammatic representation: Logistic Regression and Perceptron, Multi-Layered Perceptron(MLP)

* Notation, Training a Single-Neuron Model

* Training an MLP: Chain Rule Training an MLP: Memorization, Back Propagation, Activation Functions

* Vanishing Gradient Problem

* Bias-Variance tradeoff, Decision Surfaces, Playground

 

Decision Trees :

 

* Axis Parallel Hyperplanes, Sample Decision Tree

* Building a Decision Entropy

* Building a Decision Tree: Information Gain

* Building a Decision Tree: Gini Impurity

* Building a Decision Tree: Constructing a DT

* Building a Decision Tree: Splitting Numerical Features, Features Standardization

* Building a Decision Tree: Categorical Features with many possible values

* Overfitting and Underfitting

* Train and Run Time Complexity

* Regression using Decision Trees

* Cases, Code Samples

 

Naive Bayes :

 

* Conditional Probability

* Independent Vs Mutually Exclusive Events

* Bayes Theorem with Examples

* Exercise Problems on Bayes Theorem

* Naive Bayes Algorithm

* Toy Example: Train and Test stages

* Naive Bayes on Test Data

* Laplace/Additive Smoothing

* Log Probabilities for Numerical Stability

* Bias and Variance Tradeoff

* Feature Importance and Interpretability

* Imbalanced Data, Outliers, Missing Values

* Handling Numerical Features ( Gaussian NB)

* Multiclass Classification, Similarity or Distance Matrix

* Large Dimensionality, Best and Worst Cases

 

Support Vector Machines :

 

* Geometric Intuition

* Why we take values of +1 and -1 for Support Vector Planes

* Mathematical derivation

* Loss Function (Hinge Loss) based Interpretation

* Dual Form of SVM Formulation

* Kernel Trick, Polynomial Kernel, RBF-Kernel

* Domain-specific Kernels

* Trian and Run Time Complexities

* nu-SVM: Control Errors and Support Vectors

* SVM Regression Cases

 

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 initiate K-Means++.

* Failure Cases/ Limitations

* K-Mediods?

* Determining the Right K

* Code Samples

* Time and Space Complexity

 

Hierarchical Clustering :

 

* Agglomerative and Divisive

* Dendrograms

* Agglomerative Clustering

* Proximity Methods: Advantages and Limitations

* Time and Space Complexity

* Limitations of Hierarchical Clustering

* Code Sample

 

DBSCAN Technique :

 

* 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

FAQ's

* Python, Numpy, Advanced Mathematics, Neural Networks, Supervised / Unsupervised Learning.

* Additional Skills learnt are Programming, Data Structure, Hands on Projects, Technical Aptitude, Mathematical Aptitude, Logical Reasoning, Verbal Reasoning, Non-Verbal Reasoning, Case Studies, Campus to Corporate and Business etiquettes.

* No. Live Classroom sessions methodology at ABC do not require any Textbooks. We recommend students to prepare Class notes for revisions.

* No. ABC shall start training from basics without any assumption of previous knowledge.

* We take students with BSc, BCA, MSc, MCA, BE, B.Tech, M.Tech & M.E.

* This course would be definitely helpful since students need to carry-out a Project in Masters Program which would involve all the concepts taught in the course.

* Mock Interviews are a simulation of actual placement interviews to assist students for better preparation.

* ABC has a Unique AI-enabled Test Tool to conduct daily and periodic tests. This will help students assess the gaps in knowledge on a regular basis and improve grip on the subject.

* Yes. Every student can attend 10 Demo Classes from the date of commencement of the course.

* We provide 3 Projects for the Course.

Admission FAQ's

* We request students to contact the nearest ABC Center for Admissions. For the list of ABC Centers click here. Also, for any admission related queries and support, please call +91 - 7676 - 500 - 600.

* We have the courses on Weekdays, Weekends, Evening Courses to suit students convenience. For a list of current and new batches, click here or call +91 - 7676 - 500 - 600.

* Visit the nearest ABC Center and attend 10 demo classes at no cost. Then, you can enroll for the course by filling the application form and payment of requisite fees. Kindly contact our Counselors for guidance on +91 - 7676 - 500 - 600.

* Students are expected to attend 90% of the classes, 90% of mock interviews, 90% of online tests, and 90% of the eligible placement drives. In case of students satisfying above-mentioned criteria and not getting placed within 6 months of course completion, their entire course fee would be refunded.

* Students will receive INR 1000 referral amount for recommending our courses to relatives and friends. Please contact Course Counselor for details.

Placement FAQ's

* Students can start attending the placement drive immediately upon formal admission to any course at ABC.

* Students will be provided placement opportunities for up to 2 years from the date of completion of graduation.

* Working Professionals will be provided placement opportunities for up to 6 months from the date of completion of the ABC Course.

* Mock Interviews are a simulation of actual placement interviews to assist students for better preparation. Also, ABC has a Unique AI-enabled Test Tool to conduct daily and periodic tests. This will help students assess the gaps in knowledge on a regular basis and improve grip on the subject.

* Please click here to see placement happened for previous batches.

Fees FAQ's

* Please Contact the course counsellor for admission by paying the requisite fee. Please find course fee Here

* Yes. Please click here for details. For details and clarifications please contact our Counselors for guidance on +91 - 7676 - 500 - 600.

* This policy is only for Freshers. Students are expected to attend 90% of the classes, 90% of mock interviews, 90% of online tests, and 90% of the eligible placement drives. In case of students satisfying above-mentioned criteria and not getting placed within 6 months of course completion, their entire course fee would be refunded.

General FAQ's

Yes. Labs will be open from 7 am to 7 pm for all Students.

We support students by providing them an option of Weekend Batch to complete the remainder of the Course.

You will have Access to Backup of Classroom Session to update yourself on the missed topics.

Class Timings depend on the Batches. For more information click here .

Click here to see the benefits for students of ABC for Technology Training.

* We work from 7 am - 8 pm.

POPULAR COMBINATION

Java training in Bangalore

JAVA AND TESTING

Java training in Bangalore

JAVA AND PYTHON

Java training in Bangalore

JAVA, PYTHON AND TESTING

POPULAR COURSES

JAVA

JAVA


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PYTHON

PYTHON


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AUTOMATION TESTING

AUTOMATION TESTING


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MACHINE LEARNING

MACHINE LEARNING


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MANUAL TESTING

MANUAL TESTING


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ABC FOR TECHNOLOGY TRAINING

Incorporated in the year 2013 , Aradhya’s Brilliance Center (ABC) for Technology Training is the leading Technology Skilling Organization operating in the space of Skilling, Reskilling and Upskilling freshers and working professionals.

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