Machine Learning using Python
PRICE: 39,500
Sessions : 26       Hours: 208
Inclusive all of taxes
Machine Learning using Python. Machine learning is a type of artificial intelligence (AI) that provides computers with the ability to learn without being explicitly programmed. Machine learning focuses on the development of Computer Programs that can change when exposed to new data

Upcoming Batches

07/09/19

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Timings - 12:30 PM to 03:00 PM (IST)

12/10/19

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02/11/19

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Timings - 09:00 AM to 12:00 PM (IST)

07/12/19

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Timings - 12:30 PM to 03:30 PM (IST)

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Machine Learning using Python Curriculum

Introduction to Python

  • Overview
  • The Python Ecosystem
  • Why Python over R/SAS
  • What to expect after you learn Python

Getting Started

  • Understanding and choosing between different Python versions
  • Setting up Python on any machine (Windows/Linux/Mac)
  • Using Anaconda, the Python distribution
  • Exploring the different third-party IDEs (PyCharm, Spyder, Jupyter, Sublime)
  • Setting up a suitable Workspace
  • Running the first Python program

  • Python Syntax
  • Interactive Mode/ Script Mode Programming
  • Identifiers and Keywords
  • Single and Multi-line Comments
  • Data Types in Python (Numbers, String, List, Tuple, Set, Dictionary)
  • Implicit and Explicit Conversions
  • Understanding Operators in Python
  • Working with various Date and Time formats
  • Working with Numeric data types – int, long, float, complex
  • String Handling, Escape Characters, String Operations
  • Working with Unicode Strings
  • Local and Global Variables

  • Flow Control and Decision Making in Python
  • Understanding if else conditional statements
  • Nested Conditions
  • Working in Iterations
  • Understanding the for and while Loop
  • Nested Loops
  • Loop Control Statements– break, continue, pass
  • Understanding Dictionary- The key value pairs
  • List Comprehensions and Dictionary Comprehensions
  • Functions, Arguments, Return Statements
  • Packages, Libraries and Modules
  • Error Handling in Python

  • Reading data from files (TXT, CSV, Excel, JSON, KML etc.)
  • Writing data to desired file format
  • Creating Connections to Databases
  • Importing/Exporting data from/to NoSQL databases (MongoDB)
  • Importing/Exporting data from/to RDBMS (PostgreSQL)
  • Getting data from Websites
  • Manipulating Configuration files

  • Introduction to Numpy and its superior capabilities
  • Understanding differences between Lists and Arrays
  • Understanding Vectors and Matrices, Dot Products and Matrix Products
  • Universal Array Functions
  • Understanding Pandas and its architecture
  • Getting to know Series and DataFrames, Columns and Indexes
  • Getting Summary Statistics of the Data
  • Data Alignment, Ranking & Sorting
  • Combining/Splitting DataFrames, Reshaping, Grouping
  • Identifying Outliers and performing Binning tasks
  • Cross Tabulation, Permutations, the apply() function
  • Introduction to Data Visualization
  • Line Chart, Scatterplots, Box Plots, Violin Plots
  • Understanding Probability Distribution
  • Histograms, Heat maps and Clustered Matrices
  • Plotting Kernel Density Estimate Plots

  • Continuous and Discrete Variables
  • Understanding Distributions
  • Standard Deviation, Normal Distribution, Skewness
  • Mean, Median, Mode
  • Comparing Infinities
  • Cantor’s Diagonal Argument
  • Understanding the concept of Population and Samples
  • Sampling Distribution
  • Central Limit Theorem – Intuition & Visualization
  • Calculating Z-Score
  • Introduction to Hypothesis Testing
  • Assumptions, Rejection Region
  • Calculating Statistical Significance
  • Understanding Null Hypothesis and T-Distribution
  • Introduction to Pareto Principle

Introduction to Machine Learning

  • What is Machine Learning
  • Applications of Machine Learning
  • Setting up the working environment

Data Pre-Processing

  • Importing the Dataset
  • Handling missing data
  • Handling Categorical data
  • Splitting the dataset into training and test dataset
  • Feature Scaling

  • Simple Linear Regression
  • Multiple Linear Regression
  • Polynomial Regression
  • Support Vector Regression

  • Logistic Regression
  • K-Nearest Neighbours (K-NN)
  • Support Vector Machines
  • Kernel SVM
  • Naive Bayes Classifier
  • Decision Tree Classification
  • Random Forest Classification

Unsupervised Learning

  • Clustering – Intuition
  • K-Means Clustering
  • Hierarchical Clustering

Dimensionality Reduction

  • Principal Component Analysis (PCA)
  • Linear Discriminant Analysis (LDA)
  • Understanding Kernel PCA

Model Selection & Boosting

  • Understanding the need for Model Selection
  • What is Overfitting
  • Understanding Bias Variance Trade-off
  • K-Fold Cross Validation
  • Understanding and applying Grid Search

  • Introduction to Deep Learning
  • The Human Brain and how it works

  • Neurons
  • Understanding the Activation Function
  • How neural networks learn
  • Understanding Stochastic Gradient Descent
  • Concept of Backpropogation

  • Introduction to Convolutional Networks
  • Understanding Convolutional Operations
  • Understanding Pooling, Flattening
  • Softmax & Cross-Entropy

 

Machine Learning using Python Reviews

Palin provides good quality education in the field of data sciences and Machine Learning

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saurabh goel

Thanks for the Training & the Knowledge you delivered to me. This real time training is extremely helpful in my career growth. Best Infrastructure and the Best Team of Professional Trainers. Thanks for your backup Support as well that helped me out to clear my doubts.

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pawan kajla

Really amazing experience with the data management quality of palin. I admire the knowledge and expertise of the experts working there. They are really incredible and responsible astonishing persons. Keep up the great work palin.

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Kage Arts

Experience was great,with regards to knowledge,what we learnt we are applying. The knowledge we gained was in depth. It has been amazing learning experience.Working on live project with high quality trainers.Thankyou Palin for enhance my knowledge.

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Swati Rana

My experience with this institute was nice faculty and management system is good and experienced . At least try this Palin analytics at once if any doubt .

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varun bhatt

Good training institute. Helped me in shaping my carrier

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Ankur Prashant

Machine Learning using Python Project

Features

EXPERIENCED TRAINER

Min 8+ years of industry Experience.For end to end implementation in analytics life cycle

HIGHEST PLACEMENT RATE

Training as per industry requirement with mock interviews training

ENDORSED CURRICULUM

Designed by professionals according to industry requirement

FLEXIBLE SCHEDULE

Choose the timimg as per your convenience

REAL TIME CASE STUDIES

For end to end implementation in analytics life cycle

CLASS RECORDINGS

Life time access for recorded sessions

Students Questions and answers

Any graduate can go for data science, its independent of qualification. Its more over towards logical rather than technical. We will use more conceptual knowledge rather than the technical knowledge in data science. So who so ever have good logical skills can go for data science.

Data science is a process of exploring large, complex and varied data sets to reveal hidden patterns, unknown correlations, market trends, customer preferences and other useful information that can help organizations to take calculated risks.

Annual demand for the fast-growing new roles of data scientist, data developers, and data engineers will reach nearly 700,000 openings by 2020. By 2020, the number of jobs for all US data professionals will increase by 364,000 openings to 2,720,000 according to IBM.

There is no such prerequisite for data science you might have seen that bachelor degree in statistics and machine learning are mostly data scientists but it is not a prerequisite to learn data science. However if we familiar with the basic concepts of Math and Statistics like Linear Algebra, Calculus, Probability, etc. will help to learn data science.

In both of the modes Palin arrange classes, can go in either of that classroom as well as online.

Data science introduction, diffrent data sources like RDBMS, NOSQL, MACHINE LESRNING, PREDICTIVE ANALYTICS, DEEP LEARNING, SUPERVISED & UNSUPERVISED, KNN CLASSIFICATIONS and one LIVE PROJECT

Everyone have their own opinion towards classroom or online training. In my point of view online classes are far better than classroom session. Online trainings have more much benefits comparative to classroom like we can easily record the session we can access that session whenever we want. In online session we just need a laptop and internet connection can easily access the classroom.

Most of the organizations are working on data analytics platform Like Mckinsey, Delloitte, mercer, TCS, Accenture, Cognizant, IBM, Amex, RBS, UHG, WNS and many more..

An Entry-Level Data Scientist, IT earns an average salary of Rs 579,714 per year. The highest paying skills associated with this job are Machine Learning, Python, and SQL.

Will cover whole process of data science like initially if we want to fetch some data from traditional database we should have the knowledge of SQL. After fetching Data for data mining, data cleaning or data preparation process. After that we import that data into python, after that for predictive analytics will cover basic stats or modelling techniques. will cover some machine learning algorithms. For report presentation n dashboards will use tableau. So data science includes SQL, Python, Predictive Analytics, Machine learning and tableau.

No, One can go for python directly. No need of backgroung in analytics as well as SAS, R or any other statistics tool

Will work on live data set of ecommerce industry, one can take any industry data set. Will decide a goal statement and will follow the flowchart to achieve that

Artificial intelligence is an umbrella term which include technologies like MACHINE LERARNING, DEEP LEARNING, BIG DATA, VIRTUAL AGENTS, SPEECH RECOGINITION to name a few

In any domain we can use data science like agriculture, BFSI, Manufacturing, Healthcare, Oil and Gas, Transportation, Automobiles, Retails and others

Predictive analytics is the branch of the advanced analytics which is used to make predictions about future events. Predictive analytics uses many techniques from data mining, Statistics, Modelling, Machine Learningand artificial intelligence to analyze current data to make predictions about future

Pictorial Representation of data with an aim to allow more room for understanding is what data visualization is all about.

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