Join Best Machine Learning Training in Mohali, Machine Learning Course in Mohali, Machine Learning Institute in Mohali

Curriculum for Machine Learning Training in Mohali

The Master Course of Machine Learning in Mohali features a cutting-edge curriculum designed in association with IBM that aligns to globally-recognized standards and global trends.This course has been designed by our Founder & CEO – Er. Karan Arora who is a Data Scientists and help you learn complex theory, algorithms and coding libraries in a simple way.

This Advanced Machine Learning Course is fun and exciting, but at the same time we dive deep into Machine Learning. It is structured the following way:

• Part 1 – Basics of Python including Database (MySQL)
• Part 2 – Data Preprocessing or Data Cleaning using Numpy,Pandas and Scikit-Learn Library
• Part 3 – Advanced Data Analysis Tips & Tricks using Numpy and Pandas Library
• Part 4 – Data Visualization using Matplotlib and Seaborn Library
• Part 5 – Advanced Web Scrapping Techniques
• Part 6 – Regression: Simple Linear Regression, Multiple Linear Regression, Polynomial Regression, Decision Tree Regression, Random Forest Regression and Support Vector Regression
• Part 7 – Classification: Logistic Regression, K-Nearest Neighbor(KNN), Support Vector Machine(SVM), Kernel SVM, Naive Bayes Classifier, Decision Tree Classification, Random Forest Classification
• Part 8 – Clustering: K-Means, Hierarchical Clustering
• Part 9 – Association Rule Learning: Apriori, Eclat
• Part 10 – Reinforcement Learning: Upper Confidence Bound, Thompson Sampling
• Part 11 – Natural Language Processing: Bag-of-words model and algorithms for NLP, Speech to Text, Text to Speech
• Part 12 – Deep Learning: Artificial Neural Networks, Convolutional Neural Networks
• Part 13 – Dimensionality Reduction: PCA, LDA, Kernel PCA
• Part 14 – Model Selection & Boosting: k-fold Cross Validation, Parameter Tuning, Grid Search and XGBoost

Introduction to Python

• Brief history
• Why Python?
• Where to use?
• Anaconda
• How to install anaconda

Python Basics

• The print statement
• Comments
• Python Data structure & Data types
• String operations in Python
• Simple Input & Output
• Output Formatting

Python Program Flow

• Indentation
• Conditional statements
  • if
  • if-else
  • if-elif-else
  • Nested if
• Loops
  • for
  • while
  • Nested loops
• The range statement
• break, continue and pass
• Assert
• Loop examples- Star patterns

List and Tuples

• About Sequences
• Indexing and Slicing
• Iterating through a sequence
• Sequence functions, keywords and operators
• List & its methods
• Tuples & its methods
• List Comprehensions
• Nested Sequences

Dictionaries and Sets

• About dictionaries
• Creating, accessing dictionaries
• Iterating through a dictionary
• Dictionary methods
• About sets
• Creating, accessing sets
• Set operations
• Frozen sets

Functions & Modules

• Function – definition, calling
• Types of functions
• Function parameters
• Variable arguments
• Scope of a function
• Function Documentation/ Docstrings
• Lambda function & map, filter, reduce
• Function exercise
• Create module
• Standard Modules
• Function caching
• Function Decorators

OOPs in Python

• Class & Objects
• Variable Type
• Static variable in class
• Create classes
• Instance methods
• Constructor and destructors
• Inheritance and its types
• Polymorphism
• Encapsulation
• Scope and visibility of variables

Exceptions

• Errors and its types
• Exception Handling with try
• Handling multiple Exceptions
• Writing own exception/ custom exceptions
• Raise an Exception

File Handling

• File Handling modes
• Reading files
• Writing & Appending to Files
• Handling file exceptions
• The with statement

Regular Expressions

• Simple Character Matches
• Special Characters
• Character classes
• Quantifiers
• The Dot Character
• Greedy Matches
• Grouping
• Matching at beginning or end
• Match Objects
• Substituting
• Splitting a String

Data Structures

• List Comprehensions
• Nested List comprehensions
• Dictionary comprehensions
• Iterators
• Generators
• The functions any and all
• The with statement
• Data Compression
• Closer
• Decorator

Writing GUI in Python

• Introduction
• Components and events
• An example GUI
• The root component
• Adding a button
• Entry widgets
• Text widgets
• Checkbuttons
• Radiobuttons
• Listboxes
• Frames
• Menus
• Binding Events to widgets

Thread in Python

• Thread life Cycle
• Thread Definition
• Thread Implementation

Networking Programming Introduction

• A Daytime server
• Clients and servers
• The Client Program
• The Server program

Python MySQL Database Access Introduction

• Installation
• DB Connection
• Creating DB Table
• INSERT, READ, UPDATE, DELETE operations
• COMMIT & ROLLBACK operation
• Handling Errors

INTRODUCTION TO MACHINE LEARNING

• What is ML?
• Types of ML
• ML package: scikit-learn
• Anaconda
• How to install anaconda/ Jupyter Notebook

Numpy

• NumPy – Introduction
• NumPy – Environment
• NumPy – Ndarray Object
• NumPy – Data Types
• NumPy – Array Attributes
• NumPy – Array Creation Routines
• NumPy – Array from Existing Data
• Numpy – Array From Numerical Ranges
• NumPy – Indexing & Slicing
• NumPy – Advanced Indexing
• NumPy – Broadcasting
• 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 – Byte Swapping
• NumPy – Copies & Views
• NumPy – Matrix Library
• NumPy – Linear Algebra

Pandas

• Pandas – Introduction
• Pandas – Environment Setup
• Pandas – Introduction to Data Structures
• Pandas – Series
• Pandas – DataFrame
• Pandas – Panel
• Pandas – Basic Functionality
• Pandas – Descriptive Statistics
• Pandas – Function Application
• Pandas – Reindexing
• Pandas – Iteration
• Pandas – Sorting
• Pandas – Working with Text Data
• Pandas – Options & Customization
• Pandas – Indexing & Selecting Data
• Pandas – Statistical Functions
• Pandas – Window Functions
• Pandas – Aggregations
• Pandas – Missing Data
• Pandas – GroupBy
• Pandas – Merging/Joining
• Pandas – Concatenation
• Pandas – Date Functionality
• Pandas – Timedelta
• Pandas – Categorical Data
• Pandas – Visualization
• Pandas – IO Tools
• Pandas – Sparse Data
• Pandas – Caveats & Gotchas
• Pandas – Comparison with SQL

Matplotlib

• What Is Python Matplotlib?
• Line Plot
• Bar Graph
• Histogram
• Scatter Plot
• Area Plot
• Pie Chart
• Working With Multiple Plots

Importing data  

• Reading CSV files
• Saving in Python data
• Loading Python data objects
• Writing data to csv file

Manipulating Data  

• Selecting rows/observations
• Rounding Number
• Selecting columns/fields
• Merging data
• Data aggregation
• Data munging techniques

Statistics Basics  

• Central Tendency
  • Mean
  • Median
  • Mode
  • Skewness
  • Normal Distribution
• Probability Basics
  • What does mean by probability?
  • Types of Probability
  • ODDS Ratio?
• Standard Deviation
  • Data deviation & distribution
  • Variance
• Bias variance Trade off
  • Underfitting
  • Overfitting
• Distance metrics
  • Euclidean Distance
  • Manhattan Distance
• Outlier analysis
  • What is an Outlier?
  • Inter Quartile Range
  • Box & whisker plot
  • Upper Whisker
  • Lower Whisker
  • Scatter plot
  • Cook’s Distance
• Missing Value treatments
  • What is a NA?
  • Central Imputation
  • KNN imputation
  • Dummification
• Correlation
  • Pearson correlation
  • Positive & Negative correlation

Error Metrics  

• Classification
  • Confusion Matrix
  • Precision
  • Recall
  • Specificity
  • F1 Score
• Regression
  • MSE
  • RMSE
  • MAPE

Data Preprocessing

• Introduction
• Dealing with missing data
• Handling categorical data
  • Encoding class labels
  • One-not encoding
• Split data into training and testing sets
• Bringing features into same scale

Regression

• Introduction
• Simple Linear Regression
• Multiple Linear Regression
• Polynomial Regression
• Evaluate performance of a linear regression model
• Overfitting and Underfitting

K-Nearest Neighbours (KNN)

• KNN theory
• Implementing KNN with scikit-learn
• KNN parameters
  • n_neighbors
  • metric
• How to find nearest neighbors
• Writing own KNN classifier from scratch

Logistic Regression

• Logistic Regression theory
• Implementing Logistic Regression with scikit-learn
• Logistic Regression Parameters
• Multi-class classification
• MNIST digit dataset with Logistic Regression
• Predictive modelling on adult income dataset

Support Vector Machine (SVM)

• SVM theory
• Implementing SVM with scikit-learn
• SVM parameters:
  • C and gamma
• Plot hyperplane for linear classification
• Decision function

Decision Tree and Random Forest

• Theory behind decision tree
• Implementing decision tree with scikit-learn
• Decision tree parameters
• Combining multiple decision trees via Random forest
• How random forest works?

Naïve Bayes Classification

• Theory Naive Bayes Algorithm
• Features extraction
  • Countvectorizer
  • TF-IDF
• Text Classification

Model Evaluation and Parameter Tuning

• Cross validation via K-Fold
• Tuning hyperparameters via grid search
• Confusion matrix
• Recall and Precision
• ROC and AUC

Clustering and Dimension Reduction

• K-means Clustering
• Elbow method
• Principal components analysis(PCA)
• PCA step by step
• Implementing PCA with scikit-learn
• LDA with scikit-learn

Ensemble Techniques

• Introduction
• Types of ensemble techniques
  • Bagging
  • Boosting
• Types of Boosting
  • AdaBoost
  • Gradient Tree Boosting
  • XGBoost

Natural Language Processing

• Install nltk
• Tokenize words
• Tokenizing sentences
• Stop words with NLTK
• Stemming words with NLTK
• Speech tagging
• Sentiment analysis with NLTK

OpenCV

• Basics of Computer Vision & OpenCV
• Image Manipulations
• Image segmentation
• Object detection
• Machine learning in Computer Vision

Basics of Neural Networks

• Definition of an artificial neural network
• Perceptron
• Minimizing cost function with Gradient descent
• Classifying MNIST Handwritten digits with Multilayer Perceptron

DEEP LEARNING

Introduction to Neural Networks

• What is Neural Network?
• How neural network works?
• Stochastic Gradient descent(SGD)
• Single Layer Perceptron
• Multi-layer perceptron
• Backpropagation

Building deep learning environment

• What is deep learning?
• Deep learning packages
• Deep learning applications
• DL environment setup
  • Installing Tensorflow
  • Installing Keras

Tensorflow Basics

• What is Tensorflow
• Difference between Tensorflow & numpy
• Variables, Placeholders and constants
• Computation graph
• Visualize graph with Tensorboard

Activation Functions

• What are activation functions?
• Hyperbolic tangent function (tanh)
• ReLU – Rectified Linear Unit
• Softmax function
• Vanishing Gradient problem

Build a feed forward neural networks

• Exploring the MNIST dataset
• Load MNIST dataset using Tensorflow
• Defining the hyperpameters
• Initialize weight & bias
• Model definition
• Defining lost/cost function
• Training a neural network

Improving a NN by optimizers & Regularization

• Types of Optimizers
  • SGD with momentum
  • Adagrad
• RmsProp
• Adam
• Dropout Layers and Regularization
• Batch Normalization

Build a Neural Network Using Keras

• What is Keras?
• Installing Keras
• Keras fundamentals for deep learning
• Keras sequential model & functional API
• Solve linear regression & classification problem with example
• Saving & Loading a Keras Model

Convolution Neural Networks(CNNs)

• Introduction to CNN
• Convolutional operations
• Pooling, stridge and padding operations
• Data Augmentation
• Autoencoders for CNN
• Pre-trained CNN models
  • LeNet
  • VGGNet
  • Residual Network
• Transfer learning

Word representation using Word2Vec

• Word Embedding
• Keras Embedding Layer
• Visualize word embedding
• Load Google Word2Vc Embedding
• Example of Using pre-trained glove embedding

Recurrent Neural Networks (RNNs)

• An overview of RNN
• RNN Architecture
• Types of RNN
• Implementing basic RNN in tensorflow
• Ned for LSTM or GRU
• Deep RNN
• Implementing RNN for spam prediction
• Sequence to sequence modelling
• Developing a prediction model for time-series data
• Text classification with LSTM

Top Reasons to join Itronix Solutions for Machine Learning Course in Mohali:

• We provide video tutorials of the classroom sessions, so in case if the candidate missed any class he/she can learn from those video tutorials by Er Karan Arora
• All our training programs are based on live case studies and industry oriented projects.
• Our training curriculum is approved by our data scientists and placement partners.
• Training/Coaching are going to be conducted on daily & weekly basis and conjointly.  We will customise the training schedule as per the candidate necessities.
• We have one of the biggest team of certified expertise with 8+ years of real industry experience.
• Training will be conducted by our Founder and Data Scientists.
• Our Labs are terribly well-equipped with upgraded version of hardware and software.
• Our classrooms are fully geared up with projectors, Smart Labs, Smart Tablets & Wi-Fi access.
• We provide free personality development classes which includes Fluency, Group Discussions, Job interviews Preparation & Presentation skills.
• You will get study material in form of E-Book’s, Jupyter Notebooks and 1500 Interview Questions.
• Worldwide Recognized Course Completion Certificate by IBM, once you’ve completed the course.
• Flexible Payment options such as Paytm, Cheques, Google Pay, Cash, Credit Card, UPI, Debit Card and Net Banking.
• Machine Learning training in Mohali  is designed according to current IT Standards.
• We offer the best Machine Learning and Artificial Intelligence training and placement in Mohali with well-defined training modules & curriculum
• 24×7 lab facility. Students/Professionals are free to access the labs, Desktops for  as per their own preferred or suitable timings.

Itronix Solutions Placement Assistance for Machine Learning Training in Mohali

Being one of the top Machine Learning and Artificial Intelligence Training Company and a Certified Microsoft Authorized Educatation Partner, Cisco Partners, Intel Technology Provider, Google Certified Professionals & IBM Certified. Itronix Solutions deals with 100% Job Placements for Eligible Students after successful completion of the course.

• Itronix Solutions helps to keep you updated with latest trends and technologies.
• Itronix Solutions helps in updating your resume according to the job or company requirement
• Itronix Solutions helps in providing placement assistance in top IT FIRMS. Many of our alumni are working in ValueCoders, ArStudiouz, PixelCrayons,Prolitus, Space-O Technologies, Technostacks Infotech Pvt. Ltd, Focaloid Technologies,RIT Solution, iPraxa Inc, Webtunix AI TCS, Amazon, Facebook, Sasken, CrossML, Infosys, Google, Uber and Wipro

TRAINER PROFILE: Er. KARAN ARORA – Machine Learning & AI Expert

BRIEF INTRODUCTION: Er Karan Arora is Founder & C.E.O of Itronix Solutions. He is a Chief Data Scientist and corporate trainer. More than 8 years of experience He Consistently recognized for strong leadership and high service levels impacting project successes, Problem Solver, Data Scientist with outstanding service oriented background in Big Data Analysis, Statistical Modeling,  Database Querying, Information Security Analyst, Network Security Analyst, Operations Reporting, Firmware and Boot Loader Developer, Project Management skills as well as excellent communication skills.

 LINKEDIN PROFILE: https://in.linkedin.com/in/erkaranarora

AWARDS & HONOURS:

• Awarded as “The BURJ CEO Awards 2019” in Sugar Beach Resort, Flic en Flac, Mauritius
• Evento Speaker – Bangalore ( For Tech Summits, Tech Talks)
• Nominated for Fast Growing CEO Awards 2017
• Nominated for Fast Growing CEO Awards 2018
• Member of Explore Bharat” Conference for Channel Partners conducted by Samsung
• He had been honored being a speaker at Chandigarh Tech Summit 2019 organized by Evento for Machine Learning.
• Given more than 200+ Workshops, Corporate Training, Faculty Development Programs in various Colleges and Universities like LPU, NIT, Chandigarh Group of Colleges, Daviet, DAV University, CT Group of Colleges, Shimla University, Career Point University and many more.
• Pathankot Management Association invites him for conducting a workshop on latest innovative technology, IOT on the theme. Article Published in AIMA News Magazine.
• He was a Judge for Tech Muse – 2017 in PCM S.D. College for Women, Jalandhar
• Google Certified Digital Marketing Professional.
• Cisco Certified Network Engineer
• IBM Certified Data Scientist
• Intel Certified IOT Security Expert
• Microsoft Certified Big Data Engineer

Specialities in Machine Learning Training/Course in Mohali:

• Machine Learning Algorithms such as Bayesian Classifiers, Decision Trees Classifiers, Random Forests, K means Clustering, Hierarchical Clustering, Support Vector Machines, Polynomial Regression, K Nearest Neighbor, Eclat, Principal Component Analysis, Apriori, Multiple Linear Regression, Logistic Regression, Generalized Linear Models
• Predictive Modeling, Forecasting Analytics and Text Analytics using various data mining tools, Web Scrapping
• Expert in IOT Automation  Security & Embedded Systems
• Expert in the field of Data Transformation Tools  (Big Data, Hadoop, PIG, Hive, Scoop, Flume, Hbase, NoSQL, Spark, Scala)
• Expert in Data Analysis and Predictive Tools (R, Python, TensorFlow, Theano, Keras, Pytorch.
• Worked on the Experimental design and statistical analysis of data such as Simulation, Mathematical Optimization, Economic Modeling, Neural Networks, Decision Analysis, Probability, Testing and Validation
• Highly experienced in designing high end embedded products based on Microprocessors(ARM9/11 & x86, Beaglebone, Friendly ARM, Raspberry Pi)
• Worked extensively in Linux kernel modules and kernel optimization techniques and IPC Programming.
• Expertise on Embedded Linux Kernel Porting, Device Drivers, Board support packages, Boot-loaders, Firmwares.
• Expert in Digital Marketing, Black Hat Tricks, SEO, Adwords, SMM, SMO.
• Information Security Analyst, Cryptanalyst ,Pen Tester, Hacktivist and Exploit Researcher