Data Science & Machine Learning using Python

About the Course

The job of a data scientist is one of the most lucrative jobs out there today – it involves analyzing large amounts of data, and gathering actionable business insights from it using a variety of tools. This course will help you take your first steps in the world of data science, and empower you to conduct data analysis and perform efficient machine learning using Python. Gain value from your data using the various data mining and data analysis techniques in Python, and develop efficient predictive models to predict future results. You will also learn how to perform large-scale machine learning on Big Data using Apache Spark. You don’t have to be an expert coder in Python to get the most out of this course – just a basic programming knowledge of Python is sufficient.

Style and Approach

This course is an enticing journey that starts from the very basics and gradually picks up pace as the story unfolds. Each concept is first succinctly defined in the larger context of things, followed by a detailed explanation of their application. Every concept is explained with the help of a project that solves a real-world problem and involves hands-on work, giving you a deep insight into the world of machine learning. With this simple yet rich language—Python—you will understand and be able to implement the examples with ease.

Feature

  • Take your first steps in the world of data science by understanding the tools and techniques of data analysis
  • Train efficient Machine Learning models in Python using the supervised and unsupervised learning methods
  • Learn how to use Apache Spark for processing Big Data efficiently

Training Duration: 40 Hours / 2 Months / 5 Days (Bootcamp for working candidates only).

Pre-requisite: Any programming language or logical mind

Level: Intermediate 

01- Introduction to Practical Machine Learning Using Python
02 – Python Programming
03 – Installing software and setting up
04 – Principles of Artificial Intelligence
05 – Introduction to machine learning algorithms
06 – Classification
07 – Clustering
08 – Constructing a Classifier
09 – Predictive Modeling
10 – Model Selection and Regularization
11 – Nonlinearity
12 – Clustering with Unsupervised Learning
13 – Supervised Machine Learning
14 – Reinforcement Learning
15 – Structured Prediction
16 – How to apply Machine Learning for Data Science
17 – Generalizing with data
18 – Dealing with Real-World Data
19 – Overfitting, underfitting and the bias-variance tradeoff
20 – Avoid overfitting with feature selection and dimensionality reduction
21 – Preprocessing, exploration, and feature engineering
22 – Best practices in the data preparation stage
23 – Best practices in the training sets generation stage
24 – Best practices in the model training, evaluation, and selection stage
25 – Best practices in the deployment and monitoring stage
26 – Visualizing Data
27 – Apache Spark – Machine Learning on Big Data
28 – Neural Networks
29 – Deep Learning
30 – Web Mining Techniques
31 – Labs
32 – Case Studies

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