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- Machine Learning using Python
- Deep Learning
- Python with Django
- Python Programming Language
- Data Science using Python & R Programing
- Big Data & Hadoop
- Data Science
- Internet of Things
- Cloud Computing (AWS)
- C/C++ & Data Structures
- Programming Technique with C & C++
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- PHP
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- Manual Testing
- Selenium
- Ethical Hacking Cyber Security

- 6 months Training
- Extensive Practical
- Advance Training Certificate
- Project Certificate
- Industry Expert Faculty

**Prerequisite: **No expertise is required; however basic knowledge High School Mathematics and Statistics will be required.

Introduction to Machine Learning

- What is Machine learning
- The three different types of machine learning
- Supervised, unsupervised, reinforcement learning
- An introduction to the basic terminology and notations
- A roadmap for building machine learning systems
- Uses of Machine learning in real life example

Introduction to Python

- Introduction to Python language
- Environmental setup for Python, Anaconda, Jupyter, Spider etc
- Data types of Python, numbers, string
- Run a python program in jupyter/spider/ipython
- If, elif, Loops in python
- Functions in python
- Positional argument, keyword argument, default argument
- Lambda function
- Module of Python
- Build in and user defined module
- Import a module, call a function of a module

Object oriented Python

- Introduction to Object oriented language
- Class, object
- Create class and object in python
- Data hiding, method overloading
- Inheritance, method overriding

String manipulation and collections in python

- Creating and accessing strings
- Basic functions on strings
- Indexing and slicing on string
- Strings methods
- List and its methods
- Accessing lists
- Tuple, set, dictionary and their methods
- Indexing, slicing on list, tuple, set , dictionary
- List comprehension and its uses

Introduction Numeric and Data Analysis Modules of Python

- Introduction to Numpy
- Creating 1D and 2D Numpy array
- Slicing and indexing of Numpy Array
- Operations on Numpy array
- Introduction to Pandas
- Series and DataFrame
- Operations on series and dataframe
- Different functions on dataframe
- Pandas plotting functions
- Read external dataset using Pandas

Handling Data – Data Wrangling

- What is Data Wrangling
- Prepare data for use
- Introduction to sklearn module of python
- Find for missing value and impute
- Fillna() and Imputer() of sklearn
- Concepts of categorical variable and its problem
- Solution for categorical problem using dummy variable, LableEncoding
- Feature Scaling and its solution using StandardScalar

Visualization library of Python

- Using pandas plotting functions
- Introduction to Matplotlib and Seaborn
- Difference between the two
- Scatter plot using matplotlib
- Draw histogram, barchart, pie chart to any data
- Create pairplot using seaborn
- Concept of Box plot in seaborn
- Drawing of 3D graphs

Supervised Machine learning – Regression & Classification

- Explain supervised
- Difference between classification and regression
- Single and Multiple Linear Regression
- Mathematics behind linear regression
- Simple linear regression with example
- Multiple linear regression with Boston dataset
- R2 score and RMSE score
- Logistic regression to solve classification problem
- Apply logistic regression on titanic dataset
- Classification report and confusion matrix

Supervised Machine Learning – Regression revisited

- Problem for non linear regression
- Discussion on tree based algorithm
- Decision tree regressor with example
- Mathematics behind decision tree
- Pruning in decision tree
- Concepts of ensemble algorithm
- Random forest regressor with example
- Gradient descent algorithm
- Mathematics behind Gradient descent
- Understand the various elements of machine learning algorithm like parameters, hyper parameters, loss function, cost function and optimization.

Supervised Machine learning – Classification II

- Classification algorithm and its uses
- Different types of classifiers
- K Nearest Neighbour classifier (KNN)
- Mathematics behind KNN
- Apply KNN on IRIS dataset
- Accuracy test of classification
- Decision tree classifier for classification problem
- Random forest classifier with example

Supervised Machine learning – Advanced Classification

- What is naïve bayes classifier
- How naïve bayes work?
- Mathematics behind naïve bayes
- Text based classification using Naïve Bayes algorithm
- Classify email using Naïve Bayes classifier
- CountVectorizer and TFID algorithm to solve text classification problem

Dimensionality Reduction using PCA

- How to recognize pattern in machine learning
- Use of MNIST dataset to recognize some hand written digits
- Feature extraction and Feature elimination
- PCA to use Dimensionality reduction
- Problem of time complexity and visualization
- Solution using PCA
- Use PCA to recognize MNIST dataset

Model Selection

- Model selection and its uses
- Cross validation
- K- fold cross validation

Unsupervised learning - Clustering

- What is unsupervised learning
- Types of unsupervised learning
- Concepts of clustering
- K means clustering to cluster group of datas

SVM and Kernel trick

- What is support vector machine
- Linear SVM
- Solving non linear problem using SVM
- Kernel and kernel trick to solve non linear problem
- Polynomial and RBF kernel
- Gamma and C parameter in SVM
- Effect of gamma and C

Association Rules and Recommendation Engine

- What is association rule and its parameter
- Measure of effectiveness of the rule
- Market basket analysis for association
- Recommendation engine
- Content based filtering
- Collaborating filtering
- User-user and item –item collaboration
- Example using Movie Lens dataset

Time Series Analysis

- What is Time Series Analysis (TSA)
- Components of TSA
- ARMA and ARIMA model
- Movie Average(MA) model
- Choosing the model
- Model implementation in python
- Analysis of GDP growth of UK

Boosting

- Boosting algorithm and how it works
- Types of boosting algorithm
- AdaBoost and gradient boosting algorithm
- Mathematics behind boosting algorithm
- Boosting algorithm with example

Introduction to Deep Learning

- Concepts of neural network and deep learning
- Introduction to tensor flow
- Tensor flow constant, variable and place holder
- Artificial neural network and tensor flow
- ANN, RNN, and CNN

Projects

- House price prediction
- Big mart sales prediction
- Stock market analysis
- Loan prediction
- Handwritten digit recognition
- Email spam filtering
- Customer churn classification
- Black Friday sale analysis
- Heart disease prediction
- Movie recommendation system
- Face detection
- Object recognition
- Text classification
- Sentimental analysis

- Gain skills and competencies required in Industry by Experts.
- Work on Real-time Projects depending upon the course you select.
- Students work in a professional corporate environment.
- Get a globally recognized Certificate form WebTek with our partner logos.
- Global Brand recognition for Placements.

- Course Duration: 6 months
- Regular Batches: Online / Offline

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