Video description
Want to become a good Data Scientist? Then this is a right course for you.
This course has been designed by IIT professionals who have mastered in Mathematics and Data Science. We will be covering complex theory, algorithms and coding libraries in a very simple way which can be easily grasped by any beginner as well.
We will walk you step-by-step into the World of Machine Learning. With every tutorial you will develop new skills and improve your understanding of this challenging yet lucrative sub-field of Data Science from beginner to advance level.
We have solved few Kaggle problems during this course and provided complete solutions so that students can easily compete in real world competition websites.
What You Will Learn
- Master Machine Learning in Python
- Learn to use MatplotLib for Python Plotting
- Learn to use Numpy and Pandas for Data Analysis
- Learn to use Seaborn for Statistical Plots
- Learn All the Mathematics Required to understand Machine Learning Algorithms
- Implement Machine Learning Algorithms along with Mathematic intuitions
- Projects of Kaggle Level are included with Complete Solutions
- Learning End to End Data Science Solutions
- All Advanced Level Machine Learning Algorithms and Techniques like Regularisations, Boosting, Bagging and many more included
- Learn All Statistical concepts To Make You Ninza in Machine Learning
- Real-World Case Studies
- Model Performance Metrics
- Deep Learning
- Model Selection
Audience
Anyone who wants to build his career in Data Science / Machine Learning
About The Author
Teclov: Geekshub is an online education company in the field of big data and analytics. Their aim as a team is to provide the best skill-set to their customers to make them job-ready and prepare them to crack any challenge. They have the best trainers for cutting-edge technologies such as machine learning, deep learning, Natural Language Processing (NLP), reinforcement learning, and data science. Their instructors are people who graduated from IIT, MIT and Standford. They are passionate about teaching the topics using curated real-world case studies that calibrate the learning experience of students.
Table of contents
- Chapter 1 : Simple Linear Regression
- Chapter 2 : Multiple Linear Regression
- Chapter 3 : Hotstar, Netflix Real world Case Study for Multiple Linear Regression
- Chapter 4 : Gradient Descent
- Chapter 5 : KNN
- Chapter 6 : Model Performance Metrics
- Chapter 7 : Model Selection Part1
- Chapter 8 : Naive Bayes
- Chapter 9 : Logistic Regression
- Chapter 10 : Support Vector Machine (SVM)
- Chapter 11 : Decision Tree
- Chapter 12 : Ensembling
- Chapter 13 : Model Selection Part2
- Chapter 14 : Unsupervised Learning
- Chapter 15 : Dimension Reduction
- Chapter 16 : Advanced Machine Learning Algorithms
- Chapter 17 : Deep Learning
-
Chapter 18 : Project - Medical Treatment
- Introduction to Problem Statement
- Playing with Data
- Translating the Problem into Machine Learning World
- Dealing with Text Data
- Train, Test and Cross Validation Split
- Understanding Evaluation Matrix: Log Loss
- Building a Worst Model
- Evaluating a Worst ML Model
- First Categorical column Analysis
- Response Encoding and One Hot Encoder
- Laplace Smoothing and Calibrated classifier
- Significance of first categorical column
- Second Categorical column
- Third Categorical column
- Data pre-processing before building machine learning model
- Building Machine Learning model Part1
- Building Machine Learning model Part2
- Building Machine Learning model Part3
- Building Machine Learning model Part4
- Building Machine Learning model Part5
- Building Machine Learning model Part6
-
Chapter 19 : Project - Quora Project
- Quora Introduction
- Quora Data
- Quora Understanding ML
- Quora Data Distribution
- Quora Datalist
- Quora Basic Feature Engineering
- Quora Text
- Advanced Feature Engineering Part1
- Advanced Feature Engineering Part2
- Advanced Feature Engineering Part3
- Advanced Feature Engineering Part4
- Quora Advance Feature Analysis
- Featuring Text Data with TF-IDF Weighted Word2Vec
- Building Machine Learning Models - Part 1
- Building Machine Learning Models - Part 2
- Chapter 20 : Real World Problem - Investment Requirement Analysis for a Company
- Chapter 21 : Loan Analysis Project
- Chapter 22 : Car Project
- Chapter 23 : Stack Overflow Project - Facebook Recruitment
Product information
- Title: Machine Learning with Real World Projects
- Author(s):
- Release date: July 2019
- Publisher(s): Packt Publishing
- ISBN: 9781838985363
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