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## Online Instructor Led Live Project based training in Artificial Intelligence(AI), Machine Learning(ML) & Deep Learning |

- Learn Python from scratch
- Use Python for Data Science and Machine Learning
- Implement Machine Learning Algorithms
- Learn to use NumPy for Numerical Data
- Learn to use Pandas for Data Analysis
- Learn to use Matplotlib for Python Plotting
- Learn to use Seaborn for statistical plots
- Use Plotly for interactive dynamic visualizations
- Use SciKit-Learn for Machine Learning Tasks
- Make predictions using linear regression, polynomial regression, and multivariate regression
- Logistic Regression
- K-Means Clustering
- Random Forest and Decision Trees
- Support Vector Machines
- Neural Networks

**Goal: **In this module, you’ll get a complete knowledge of python and it’s libraries which are going to be used in better understanding in problem solving of Machine Learning(ML).

- Getting Started with python
- Data Types and Variables
- Operators
- Structural Data Types-Lists, Tuples, Strings & Dictionaries
- Conditional Code
- Loops and Iterations
- Functions
- Files I/O
- Accessing Web Data

**Introduction to NumPy**

- Understanding Data Types in Python
- Fixed-Type Arrays in Python
- Creating Arrays from Python Lists
- Creating Arrays from Scratch
- NumPy Array Attributes
- Reshaping of Arrays
- Computation on NumPy Arrays: Universal Functions
- Fancy Indexing
- Sorting Arrays
- Structured Data: NumPy’s Structured Arrays

- Installing and Using Pandas
- Introducing Pandas Objects
- Data Indexing and Selection
- Operating on Data in Pandas
- Handling Missing Data
- Hierarchical Indexing
- Combining Datasets: Concat and Append
- Combining Datasets: Merge and Join
- Aggregation and Grouping
- Pivot Tables
- High-Performance Pandas: eval() and query()

- Importing matplotlib
- Simple Line Plots
- Simple Scatter Plots
- Visualizing Errors
- Density and Contour Plots
- Histograms, Binnings, and Density
- Multiple Subplots
- Text and Annotation
- Other Python Libraries for Data Scientists-
- Scipy
- Scikit-learn
- Seaborn
- Reading Data; Selecting and Filtering the Data; Data manipulation, sorting, grouping, rearranging
- Plotting the data
- Descriptive statistics
- Inferential statistics

**The Math behind Machine Learning: Linear Algebra**- Scalars
- Vectors
- Matrices
- Tensors
- Hyperplanes
- Probability
- Conditional Probabilities
- Posterior Probability
- Distributions
- Samples vs Population
- Resampling Methods
- Selection Bias
- Likelihood

- What is it and where is it used ?
- Major Applications and the companies using it
- Overview of Types of ML

- Model Overview : training and testing
- Hypothesis formation
- Understanding the prameters
- COST function (derivation and application)
- Types of Errors (SSE,SSR)
- Computing Cost by hand
- Computing Cost with numpy
- Gradient Descent (derivation and types)
- Computing Gradient descent with numpy

- Linear Regression with single Variable
- OLS (ordinary least square)Estimator
- multi Variable Linear Regression
- Normal Equation
- R2 score

- House Price Prediction
- Implementation in numpy
- Implementation in scikit-learn

- sigmoid function
- Decision boundary
- Cross-validation

- IRIS Dataset Prediction
- Implementation in numpy
- Implementation in scikit-learn

- Intro and Types
- ID3 algo from scratch derviation
- Regression and Classification Cases
- Random Forests Algorithm

Project case study

- Implementation in numpy
- Implementation in scikit-learn

- Unsupervised
- Features and data vectors
- Various steps of algo
- Understanding of Clusters and various types of
- Applying K-Means on datasets and their practical use
- Applications of Clustering and the algorithm

- K-MEANS clustering
- Generative Modeling Through Baysian Sampling

- understanding image structure
- creating color pallete with kmeans
- Generating Handwritten Digits

- What’s a Neural Network?
- Various Structures of NN
- Understanding Fundamentals and Various parameters of NN
- ANN,CNN and RNN
- Deep Dive with the Implementaion of NN on various datasets
- Applying CNN on Images

Applications and its complexities over other algorithms Project:- Smart Machine Learning System

**Goal : **In this module, you’ll about classical image analysis techniques such as Edge

detection,watershed,distance transformations using the OpenCV library .Here you’ll explore the evolution of image analysis ,from classical deep learning techniques.

**Objectives - **At the end of this module, you should be able to:

- Introduction to computer vision and Image Processing
- Image processing using OpenCV
- Video processing and Image extraction using OpenCV
- Convolutional Features for visual recognition
- Object ,Face and Gestures Detection using Haar Cascade Classifier
- Object Tracking and Action Recognition

This is all about summer, winter and regular training in Machine Learning (ML) using Python at Goeduhub Technologies-Jaipur. Apart from this student will also complete some real time projects during training.

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