# Professional Certificate in Data Science

Professional Certificate in Data Science

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*Professional Certificate in Data Science Learn All the Skills to Become a Data Scientist [ Machine Learning,Deep Learning, CNN, DCGAN, Python, Java, Algorithms] New*

What you'll learn

- Python Programming Basics For Data Science
- Machine Learning - [A -Z] Comprehensive Training with Step by step guidance
- Supervised Learning - (Univariate Linear regression, Multivariate Linear Regression, Logistic regression, Naive Bayes Classifier, Trees, Support Vector Machines, Random Forest)
- Unsupervised Learning - Clustering, K-Means clustering
- Evaluating the Machine Learning Algorithms : Precision, Recall, F-Measure, Confusion Matrices,
- Data Pre-processing - Data Preprocessing is that step in which the data gets transformed, or Encoded, to bring it to such a state that now the machine can easily parse it.
- Algorithm Analysis For Data Scientists
- KERAS Tutorial - Developing an Artificial Neural Network in Python -Step by Step
- Deep Learning -Handwritten Digits Recognition [Step by Step] [Complete Project ]
- Deep Convolutional Generative Adversarial Networks (DCGAN)
- Java Programming For Data Scientists
- Kaggle - Covid 19- Classification (Chest X-ray.) - Covid-19 & Pneumonia
- Developing a CNN From Scratch for CIFAR-10 Photo Classification

Description

At the end of the Course you will have all the skills to become a Data Science Professional. (The most comprehensive Data Science course )

1) Python Programming Basics For Data Science - Python programming plays an important role in the field of Data Science

2) Introduction to Machine Learning - [A -Z] Comprehensive Training with Step by step guidance

3) Setting up the Environment for Machine Learning - Step by step guidance

4) Supervised Learning - (Univariate Linear regression, Multivariate Linear Regression, Logistic regression, Naive Bayes Classifier, Trees, Support Vector Machines, Random Forest)

5) Unsupervised Learning

6) Evaluating the Machine Learning Algorithms

7) Data Pre-processing

8) Algorithm Analysis For Data Scientists

9) Deep Convolutional Generative Adversarial Networks (DCGAN)

10) Java Programming For Data Scientists

#### Course Learning Outcomes

To provide awareness of the two most integral branches (Supervised & Unsupervised learning) coming under Machine Learning

Describe intelligent problem-solving methods via appropriate usage of Machine Learning techniques.

To build appropriate neural models from using state-of-the-art python framework.

To build neural models from scratch, following step-by-step instructions.

To build end - to - end solutions to resolve real-world problems by using appropriate Machine Learning techniques from a pool of techniques available.

To critically review and select the most appropriate machine learning solutions

To use ML evaluation methodologies to compare and contrast supervised and unsupervised ML algorithms using an established machine learning framework.

Beginners guide for python programming is also inclusive.**Introduction to Machine Learning - Indicative Module Content**

Introduction to Machine Learning:- What is Machine Learning ?, Motivations for Machine Learning, Why Machine Learning? Job Opportunities for Machine Learning

Setting up the Environment for Machine Learning:-Downloading & setting-up Anaconda, Introduction to Google Collabs

Supervised Learning Techniques:-Regression techniques, Bayer’s theorem, Naïve Bayer’s, Support Vector Machines (SVM), Decision Trees and Random Forest.

Unsupervised Learning Techniques:- Clustering, K-Means clustering

Artificial Neural networks [Theory and practical sessions - hands-on sessions]

Evaluation and Testing mechanisms :- Precision, Recall, F-Measure, Confusion Matrices,

Data Protection & Ethical Principles

Setting up the Environment for Python Machine Learning

Understanding Data With Statistics & Data Pre-processing (Reading data from file, Checking dimensions of Data, Statistical Summary of Data, Correlation between attributes)

Data Pre-processing - Scaling with a demonstration in python, Normalization , Binarization , Standardization in Python,feature Selection Techniques : Univariate Selection

Data Visualization with Python -charting will be discussed here with step by step guidance, Data preparation and Bar Chart,Histogram , Pie Chart, etc..

Artificial Neural Networks with Python, KERAS

KERAS Tutorial - Developing an Artificial Neural Network in Python -Step by Step

Deep Learning -Handwritten Digits Recognition [Step by Step] [Complete Project ]

Naive Bayes Classifier with Python [Lecture & Demo]

Linear regression

Logistic regression

Introduction to clustering [K - Means Clustering ]

K - Means Clustering

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