**The Complete A-Z Machine Learning Course with Python in Mohali**

** Machine Learning, a prominent topic in Artificial Intelligence domain, has been in the spotlight for quite some time now. This area may offer an attractive opportunity, and starting a career in it is not as difficult as it may seem at first glance. Even if you have zero-experience in math or programming, it is not a problem. The most important element of your success is purely your own interest and motivation to learn all those things. **Itronix Solutions provides

**Best Machine Learning Training in Mohali**as per the current industry standards. Itronix Solutions is one of the most recommended

**Machine Learning Training Institute in Mohali**that offers hands-on practical knowledge/ practical implementation on live case studies and will ensure the job with the help of advanced level

**Machine Learning Training Courses**. At Itronix Solutions Machine Learning Training in Mohali is conducted by specialist working certified corporate professionals having 10+ years of experience in implementing real-time

**Machine Learning projects and case studies.**

**What you will learn in Machine Learning Course in Mohali ?**

- Master Machine Learning & Deep Learning using Python
- Make Accurate
**Prediction**s and Powerful Analysis to build Robust**Machine Learning Models** - Handle specific topics like
**Supervised**Machine Learning,**Unsupervised**Machine Learning,**Reinforcement**Learning,**Natural Language Processing**and**Deep Learning**& Dimensional Reduction - Itronix Solutions’s
**Machine Learning & Artificial Intelligence**Program in Mohali has been designed to administer you an intensive data and active expertise in spearheading Machine Learning models and investing your innovative ability to form strategically vital structure choices. - You will be able to deploy trained models to a web application and evaluate the super performance of your models.

**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: A**rtificial 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,**J**ob 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