Data Sciences Courses Online

Data Science is an interdisciplinary field with statistical analysis, mathematics, algorithms, machine learning and utilising data visualisation in order to draw data-driven decisions and insights. You can identify data, trends and patterns to make calculative and informed decisions. Data Science requires to use of tools, techniques and programming languages like R, SQL, and Python to analyse large databases and use machine learning algorithms to make predictions, and classify data and patterns in large data repositories. Almost every industry requires a data scientist, including all industries like healthcare, finance, marketing, retail and real estate.

How will Learnfly help you become a better Data Scientist?

Under the guidance of experts and professionals in the domain, you will learn the foundations of data analysis and gain data analytics certification like:

• Learn the concepts of Python, Machine learning, Deep Learning, and Time series. Implement Real World Projects with Proof Of Concept.
• Mathematical and statistical concepts such as linear algebra, probability, calculus
• Data wrangling and cleaning
• Creating informative visualisation of data
• Predictive modelling
• Machine learning
• Coding and Programming language

Course Overview:

This course has been designed by two professional Data Scientists to share knowledge and help in learning complex theory, algorithms, and coding libraries in a simple way.

Below are a few Applications of Machine Learning in Practical Real World

1. Machine learning can help with the diagnosis of diseases. Many physicians use chatbots with speech recognition capabilities to discern patterns in symptoms. Real-world examples for medical diagnosis: Assisting in formulating a diagnosis or recommending a treatment option.
2. Google Maps uses machine learning in combination with various data sources including aggregate location data, historical traffic patterns, local government data, and real-time feedback from users, to predict traffic.

Python leads the pack, with 57% of data scientists and machine learning developers using it and 33% Prioritising it for development. So, In this course also you will able learn the Basics of Python to Advance State of Art Techniques of Deep Learning Models.

There are 4 different sections in this course for a complete understanding of all the concepts in Artificial Intelligence such as Python, Machine Learning, Deep Learning, and Time Series Analysis. This course is fun and exciting, but at the same time, we dive deep into Machine Learning. It is structured in the following way:

1. PYTHON -

• Data Structures, List, Tuples, Dictionary, Libraries, Functions, Operators etc
• Data Cleaning and Preprocessing

2. MACHINE LEARNING -

• Regression: Simple Linear Regression, , SVR, Decision Tree, Random Forest,
• Clustering: K-Means, Hierarchical Clustering Algorithms
• Classification: Logistic Regression, Kernel SVM, Naive Bayes, Decision Tree Classification, Random Forest Classification
• Natural Language Processing: Bag-of-words model and algorithms for NLP

3. DEEP LEARNING -

• Artificial Neural Networks,
• Convolutional Neural Networks,
• Recurrent Neural Networks,
• Long short-term Memory,
• Vgg16 ,
• Transfer learning,
• Web-Based Flask Application.

Moreover, the course is packed with practical exercises that are based on real-life examples. So not only will you learn the theory, but you will also get some hands-on practice building your own models.

What are the expected prerequisites?

• There is no specific prerequisite to learning machine learning. But you need to be from an engineering/science/Maths/Stats background to understand the theory and the techniques used. You need to be good at mathematics. If you are not, still you can machine learning, but you will face difficulty when solving complex real-world problems.
• No prior experience is required as the course is taught from the basics.

Targetted Audience:

• Data Scientists, Researchers, Entrepreneurs, Instructors, College Students, Engineers and Programmers
• Anyone who wants to analyze the data
• Beginner in Machine Learning
• Career transition from Non-Technical into Data Science
• Fresher to get a job as Machine Learning Engineer
• Beginner into Python
• Fresher to get a job as Machine Learning Engineer

What you’ll learn after this course?

• Data Structures (Lists, Tuple, Dictionary) and String Manipulations
• Implementation Of Lambda, Recursion, Functions
• Understand Of Libraries, Exploratory Data Analysis, Descriptive Analysis
• Classical Machine Learning Algorithm Logistic Regression
• Word Embedding Techniques BoW, TF -IDF, W2V etc
• Text Cleaning and Preprocessing with Amazon Reviews Data
• Classical Machine Learning Algorithm Linear Regression
• Geometric Intuition of Ensembles Models and Flask Project
• Data Analysis on Loan Approval Status
• Unsupervised Learning Algorithms K means Cluster Techniques.
• Introduction to Neural Networks and Basics of MLP, BACKPROP
• In-Depth Understanding of RNN and LSTM with Examples
• Intuition Behind the Computer Vision and CNN Algorithm
• Convolutional Neural Networks with Pizza and CIFAR Projects
• Practical Examples of Transfer Learning for Vgg16 Model
• Web-Based Flask Framework for Wild Animal Recognition with CNN

How to sign-up for the course?

You need to purchase the courses offered. When you sign up, you'll immediately have unlimited viewing of thousands of expert courses, paths to guide your learning, tools to measure your skills and hands-on resources like exercise files. There’s no limit on what you can learn and you can cancel at any time