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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