Machine Learning with Python, starts 29th July 2023. Only 15 seats vacant

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TRAINING | CERTIFICATION | PLACEMENT

Step-by-step guidance to Data Science and Machine Learning!

**100% Placement Assistance** – Learn Data Science and win job opportunities in no time!

Data Science & machine learning is acclaimed as one of the best career opportunities with application in practically every sector of professional activities – e.g., e-commerce, manufacturing, IOT and robotics, medical field, security to fashion. This Data Scientist course aims to accelerate your career in Data Science and provides you with world-class training and skills required to become successful in this field. The Data Scientist course offers extensive training on the most in-demand Data Science and Machine Learning skills with hands-on exposure to key tools and technologies including Python, Data Visualization, Statistics, concepts of Machine Learning and introduction of Deep Learning.

Our Data Science course is specially designed by industry experts with more than 15 years of industry experience, covering the complete Data Science concepts from Data Collection, Data Extraction, Data Cleansing, Data Exploration, Data Transformation, Feature Engineering, Data Integration, Data Mining, building Prediction models, Data Visualization and Model Deployment. Skills and tools ranging from Statistical Analysis, Text Mining, Regression Modelling, Hypothesis Testing, Predictive Analytics, Machine Learning, Deep Learning, Neural Networks, Natural Language Processing, Predictive Modelling, R Studio, Tableau, Python and R programming.

Python are covered extensively as part of this Data Science training. Python has become number one choice for data science, Machine Learning & Artificial Intelligence applications. The training is a practical oriented, step by-step guidance to Data Science using Python.

- Understand concept of Data Science, Machine Learning & AI
- New aspects of Data Science
- Data Science best practices
- Data Science tools and techniques
- Statistics for Data Science
- How to implement Data Science techniques
- How to extract insights from data
- Participants will be able to work on industry-based projects

Our Data Science course is specially designed by industry experts and if you are serious about a career pertaining to Data science, then you are at the right place. Infynas Learning Solutions is considered to be one of the best **Data Science training institutes**. We have built careers for lot of Data Science professionals in various MNCs. “Training to Job Placement” – is our forte. We do the necessary hand-holding until you are placed. Our expert trainers will help you with upskilling the concepts, to complete the assignments and live projects. Our trainers are experienced professionals and currently working with top MNCs who keep live projects that assist pupils to learn in a much better manner. We ensure that the students work on live jobs so that they learn and relate things quickly and improve their skills well. We have a dedicated placement team who work with partners to facilitate the interviews to help the participants getting placed.

- Specially designed curriculum
- Industry Expert Trainers
- Learn From Top Data Scientist
- Online learning session with live instructor-led training
- One-on-one student guidance support
- Learn at your own pace
- Easy & Convenient learning style

- All-time Academic support throughout the course
- Hassle-free access to course
- Opportunity to work on file formats on different data
- Excellent understanding of Data Science Algorithms
- Real-Time Projects & Case Studies
- Webinar / Workshop
- Internship & Placement opportunities

**Data Science course** as a next logical move to enhance their careers. We welcome Freshers from any stream, Any Graduates, Any Post-Graduate, Any Engineers, Working Professionals from any domain, Tech Enthusiasts, Entrepreneurs, and those who see the future in the Data Science industry. There’s a lot behind learning the Data science, because of its vast scope and it is expected to be the most attractive industry in the coming generations

- Introduction to the course
- Introduction to Data Science
- Introduction to Machine Learning
- Introduction to Analytics
- What does data science involves
- Life cycle of Data Science
- Design Thinking and Problem Statement
- Tools of Data Science
- Relationship between Artificial Intelligence, Machine Learning, and Data Science
- o Major classes of Learning Algorithms

- o Areas of implementation of Machine learning

- o Supervised vs. Unsupervised learning

** **

- Installation of necessary tools /libraries
- Using Jupyter Notebook

- Python environment Setup and Essentials
- Introduction to python
- Software installation
- Basic operators and functions
- Python Variables: int, float, string, bool, complex
- Data types with python
- Conditional statement
- Loops
- Python Collections: List, Tuple, Dictionary, Set, Frozenset
- Functions and Methods
- Class & Objects
- Error Handling
- Regular Expression

- Introduction to Numpy
- Datatypes
- Introduction to numpy arrays
- How to Access Array Elements?
- Indexing, Slicing, Iteration, Indexing with Boolean Arrays
- Dealing with Flat fles using numpy
- Mathematical functions
- Statistical functions (mean, median, average, standard deviation)
- Operations with arrays

- Scipy subpackage
- Linalg
- Special
- stats

- Types of statistics
- Types of Data
- The measure of Central Tendency
- Five number summery boxplot
- Measure of Dispersion
- Skewness and Kurtosis
- Covariance and Correlation
- Central Limit Theorem
- Introduction to Hypothesis Testing

- Data and Types
- Central Tendency: Mean, Median, Mode
- Deviation: Range, Variance, Standard Deviation
- BoxPlot and its importance
- Frequency distribution and its importance
- Scatter Plots and its importance

- Probability
- Discrete Probability Distribution: Binomial Distribution, Poisson
- Continuous Probability Distribution: Normal, t distribution, Exponential
- Correlation

**to communicate with a database**. According to ANSI (American National Standards Institute), it is the standard language for relational database management systems. SQL statements are used to perform tasks such as update data on a database, or retrieve data from a database.

- Introduction to Pandas
- Defining data structures
- Pandas datatypes
- Creating Data frames
- Data frame operations
- Indexing / reindexing –loc, iloc
- Grouping, Sorting, Slicing/Dicing operations
- Binning
- Aggregations
- Filtering
- Merging/Joining
- Date Functionality
- Importing /exporting data
- Importing data from csv excel json web database etc.

- Cleaning and preparing data
- Normalization and Standardization of data
- Encoding
- Handling missing data

- Quantitative Technique
- Graphical Technique
- Outlier Values in a Dataset
- Splitting the Dataset into the Training set and Test set
- Automated tools for EDA- pandas profiling ,sweetviz

- Data visualization with pandas
- Visualization using Matplotlib and Seaborn
- Bar Graph
- Histogram
- Scatter Plot
- Area Plot
- Pie Chart
- Special plots
- Violin Plot
- Boxplot
- Heatmap
- Facet Grid Pair grid
- Adding legend & text
- Color palette

**Regression Analysis**

- Regression Algorithms
- Linear Regression
- Logistic regression
- Regression Use Case
- Cost Function
- Evaluating Coefficients
- Evaluating Regression Models Performance
- Accuracy Matrix
- R-Squared
- Interpreting Linear Regression Coefficients

**Regularization – introduction**

- Lasso and Ridge Regression
- Elastic Net Regularization

**K-NN classification**

- Introduction and working
- Implementation using Scikit-Learn

**Naïve Bayesian classifiers**

- Intro to Bayesian theorem
- Implement NB through Scikit-Learn

**SVM – (Support Vector Machines)**

- Introduction to Support Vector Machines
- Python Kernel SVM
- The Kernel Trick
- Types of Kernel Functions
- SVM Algorithm for Classification
- SVM for Regression

**Decision Tree**

- Entropy and information gain
- Ginny impurity
- Visualizing Decision tree
- Regression tree and classification trees

**Clustering**

- Clustering
- K-means Clustering
- Finding Optimal Number of Clusters

**Association Rule Mining**

- Rule Mining Market Basket Analysis
- Rule Generation with Apriori Algorithm

**Model Selection**

- Cross Validation

**Hyper parameter tuning**

- Hyperparameter tuning techniques

**Time series modeling**

- ARMA
- ARIMA
- SARIMA

**Dimensionality Reduction Techniques**

- Component Analysis (PCA)
- LDA

**Ensemble Techniques**

- Intro to ensemble Techniques
- Bagging
- Boosting
- Stacking
- RandomForest
- AdaBoost
- GradientBoosting
- XGboost

**Working with imbalanced data**

- smote

**Explainable ML**

- Introduction and need for
- Getting insight with eli5 shap and lime

**Deploy your model**

- Deploy your model locally
- Basic of flask
- Deploy your model as flask web app
- Create dashboard with streamlit

**Visualization With Tableau**

- Introduction to Visualization
- Connect to your data
- Concepts: Filter, Join, Hierarchy, Groups, Set
- Explore your data geographically
- Drill down into the details
- Build a dashboard to show your insights
- Build a story to present

**Introduction to Deep Learning**

- TensorFlow
- Neural Network – ANN, CNN, RNN
- Autoencoders
- Long Short-term memory (LSTM)
- Restricted Boltzman Machine (RBM)
- NLP Overview

- Introduction to R and RStudio
- RStudio interface and basics
- Basic arithmetic and variable assignment
- Comparison and logical operators
- Data types
- Vectors
- Functions
- Loops and conditionals

Jira helps teams plan, assign, track, report, and manage work and brings teams together for everything from agile software development and customer support to start-ups and enterprises.

**a code hosting platform for version control and collaboration**. It lets you and others work together on projects from anywhere. This tutorial teaches you GitHub essentials like repositories, branches, commits, and pull requests.

- Case study-based approach
- 5 Projects in multiple domain
- Customer Segmentations on Sales Dataset.
- Financial Risk Modeling on German Credit Dataset
- House Pricing Prediction Modeling
- Loan Approval Prediction on Customer Dataset
- Predicting the price for used car

- Mock interview
- Interview Preparation
- Resume Preparation
- Placement Assistance

This course connects the academic and industry by bridging the gaps for the new aspirants.

Acts as an enabler to the working professionals to boost up their careers, and provide an extra edge

- Introduction to the course
- Introduction to Data Science
- Introduction to Machine Learning
- Introduction to Analytics
- What does data science involves
- Life cycle of Data Science
- Design Thinking and Problem Statement
- Tools of Data Science
- Relationship between Artificial Intelligence, Machine Learning, and Data
- Science
- Areas of implementation of Machine learning
- Major classes of Learning Algorithms
- Supervised vs. Unsupervised learning

- Installation of necessary tools /libraries
- Using Jupyter Notebook

- Python environment Setup and Essentials
- Introduction to python
- Software installation
- Basic operators and functions
- Python Variables: int, float, string, bool, complex
- Data types with python
- Conditional statement
- Loops
- Python Collections: List, Tuple, Dictionary, Set, Frozenset
- Functions and Methods
- Class & Objects
- Error Handling
- Regular Expression

- Introduction to Numpy
- Datatypes
- Introduction to numpy arrays
- How to Access Array Elements?
- Indexing, Slicing, Iteration, Indexing with Boolean Arrays
- Dealing with Flat fles using numpy
- Mathematical functions
- Statistical functions (mean, median, average, standard deviation)
- Operations with arrays

- Scipy subpackage
- Linalg
- Special
- stats

- Types of statistics
- Types of Data
- The measure of Central Tendency
- Five number summery boxplot
- Measure of Dispersion
- Skewness and Kurtosis
- Covariance and Correlation
- Central Limit Theorem
- Introduction to Hypothesis Testing

- Data and Types
- Central Tendency: Mean, Median, Mode
- Deviation: Range, Variance, Standard Deviation
- BoxPlot and its importance
- Frequency distribution and its importance
- Scatter Plots and its importance

- Probability
- Discrete Probability Distribution: Binomial Distribution, Poisson
- Continuous Probability Distribution: Normal, t distribution, Exponential
- Correlation

- Introduction to Pandas
- Defining data structures
- Pandas datatypes
- Creating Data frames
- Data frame operations
- Indexing / reindexing –loc, iloc
- Grouping, Sorting, Slicing/Dicing operations
- Binning
- Aggregations
- Filtering
- Merging/Joining
- Date Functionality
- Importing /exporting data
- Importing data from csv excel json web database etc.

- Cleaning and preparing data
- Normalization and Standardization of data
- Encoding
- Handling missing data

- Quantitative Technique
- Graphical Technique
- Outlier Values in a Dataset
- Splitting the Dataset into the Training set and Test set
- Automated tools for EDA- pandas profiling ,sweetviz

- Data visualization with pandas
- Visualization using Matplotlib and Seaborn
- Bar Graph
- Histogram
- Scatter Plot
- Area Plot
- Pie Chart
- Special plots
- Violin Plot
- Boxplot
- Heatmap
- Facet Grid Pair grid
- Adding legend & text
- Color palette

**Regression Analysis**

- Regression Algorithms
- Linear Regression
- Logistic regression
- Regression Use Case
- Cost Function
- Evaluating Coefficients
- Evaluating Regression Models Performance
- Accuracy Matrix
- R-Squared
- Interpreting Linear Regression Coefficients

**Regularization – introduction**

- Lasso and Ridge Regression
- Elastic Net Regularization

**K-NN classification**

- Introduction and working
- Implementation using Scikit-Learn

**Naïve Bayesian classifiers**

- Intro to Bayesian theorem
- Implement NB through Scikit-Learn

**SVM – (Support Vector Machines)**

- Introduction to Support Vector Machines
- Python Kernel SVM
- The Kernel Trick
- Types of Kernel Functions
- SVM Algorithm for Classification
- SVM for Regression

**Decision Tree**

- Entropy and information gain
- Ginny impurity
- Visualizing Decision tree
- Regression tree and classification trees

**Clustering**

- Clustering
- K-means Clustering
- Finding Optimal Number of Clusters

**Association Rule Mining**

- Rule Mining Market Basket Analysis
- Rule Generation with Apriori Algorithm

**Model Selection**

- Cross Validation

**Hyper parameter tuning**

- Hyperparameter tuning techniques

**Time series modeling**

- ARMA
- ARIMA
- SARIMA

**Dimensionality Reduction Techniques**

- Component Analysis (PCA)
- LDA

**Ensemble Techniques**

- Intro to ensemble Techniques
- Bagging
- Boosting
- Stacking
- RandomForest
- AdaBoost
- GradientBoosting
- XGboost

**Working with imbalanced data**

- smote

**Explainable ML**

- Introduction and need for
- Getting insight with eli5 shap and lime

**Deploy your model**

- Deploy your model locally
- Basic of flask
- Deploy your model as flask web app
- Create dashboard with streamlit

- Case study-based approach
- 5 Projects in multiple domain
- Customer Segmentations on Sales Dataset.
- Financial Risk Modeling on German Credit Dataset
- House Pricing Prediction Modeling
- Loan Approval Prediction on Customer Dataset
- Predicting the price for used car

- Mock interview
- Interview Preparation
- Resume Preparation
- Placement Assistance

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