Data Science 2020: Data Science & Machine Learning in Python
4.3 (14 ratings)
Course Ratings are calculated from individual students’ ratings and a variety of other signals, like age of rating and reliability, to ensure that they reflect course quality fairly and accurately.
112 students enrolled

Data Science 2020: Data Science & Machine Learning in Python

Data Science, Machine Learning Python, Deep Learning, TensorFlow 2.0, NLP, Statistics for Data Science, Data Analysis !
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4.3 (14 ratings)
Course Ratings are calculated from individual students’ ratings and a variety of other signals, like age of rating and reliability, to ensure that they reflect course quality fairly and accurately.
112 students enrolled
Last updated 8/2020
English
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Current price: $139.99 Original price: $199.99 Discount: 30% off
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This course includes
  • 18.5 hours on-demand video
  • 13 articles
  • Full lifetime access
  • Access on mobile and TV
  • Certificate of Completion
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What you'll learn
  • Go from total beginners to confident machine learning engineer
  • Apply Machine Learning algorithm on 10+ dataset
  • Get complete Environment ready with Google Colab Notebook
  • Machine Learning Overview, Types of Machine Learning System
  • Compare Machine Learning with Traditional system of Computing
  • Different Machine Learning Algorithm, Machine Learning Workflow
  • Transform your data with One Hot Encoding & Feature scaling
  • Apply Natural Language Processing Technique like Tokenization, Stemming, Stop Words, Named Entity Recognition, Sentence Segmentation
  • Classify Fashion clothes image with Artificial Neural Network + Keras
  • Importing Libraries, Importing Dataset, Working with missing data
  • Build Credit Card Fraud Detection with Convolution Neural Network
  • Theory behind Convolutional Neural Networks, Different layers in Convolutional Neural Networks
  • The Neuron, Activation Function, Cost Function, Gradient Descen
  • Theory behind Recurrent Neural Networks, Vanishing Gradient Problem
  • Classify IMDB Review using Recurrent Neural Network - LSTM
  • Pandas Series, DataFrames, Multi-index and index hierarchy
  • Working with Missing Data, Groupby Function, Merging Joining and Concatenating DataFrames
  • Pandas Operations, Reading and Writing Files
  • Predict Restaurant Profit with Multiple Linear Regression
  • Calculate Grades using Simple Linear Regression
  • Apply SVR, SVM, Decision tree and Random Forest on Real Dataset
  • Get Hands-on with Python Crash Course
  • Refresh concept of Data analysis and Visualization with NumPy, Pandas & Matplotlib
  • Plots different types of Chart like Scatter Plot, Bar plot, Histogram, Pie Chart
Course content
Expand all 164 lectures 18:18:37
+ Welcome to the Course !
4 lectures 05:35
What is inside course?
00:41
Udemy Review Updates
01:26
Course FAQs
00:25
+ Machine Learning Overview
5 lectures 24:20
Types of Machine Learning System
06:25
Machine Learning vs Traditional system of Computing
05:00
Different Machine Learning Algorithm
02:16
Machine Learning Workflow
03:04
+ Statistics Basic
8 lectures 51:42
Data
05:24
Levels of Measurement
07:24
Measures of Central Tendency
07:30
Population vs Sample
06:06
Probability based Sampling methods
05:46
Non Probability based Sampling method
05:39
Measures of Dispersion
08:03
Quartiles and IQR
05:50
+ Probability
8 lectures 35:20
Introduction to Probability
06:49
Permutations
05:37
Combinations
04:03
Intersection, Union and Complement
03:57
Independent and Dependent Events
03:50
Conditional Probability
03:34
Addition and Multiplication Rules
03:47
Bayes’ Theorem
03:43
+ Data Pre-Processing
7 lectures 52:21
Importing the dataset
13:13
Working with the missing data
07:51
Encoding the categorical data
08:23
Splitting the dataset into train and test set
05:55
Feature scaling
07:51
Requirements
  • No prior knowledge or experience is needed
Description

According to an IBM report, Data Science jobs would likely grow by 30 percent. The estimated figure of job listing is 2,720,000 for Data Science in 2020

And according to the US Bureau of Labor Statistics, about 11 million jobs will be created by 2026


Data Science, Machine Learning and Artificial Intelligence are hottest and trending technologies across the globe, almost every multinational organization is working on it and they need a huge number people who can work on these technologies


By keeping all the industry requirements in mind we have designed this course, with this single course you can start your journey in the field of Data Science


In this course we tried to cover almost everything that is comes under the umbrella of Data Science,


Topics covered:

1) Machine Learning Overview: Types of Machine Learning System, Machine Learning vs Traditional system of Computing, Different Machine Learning Algorithm, Machine Learning Workflow

2) Statistics Basic: Data, Levels of Measurement, Measures of Central Tendency, Population vs Sample, Probability based Sampling methods, Non Probability based Sampling method, Measures of Dispersion, Quartiles and IQR

3) Probability: Introduction to Probability, Permutations, Combinations, Intersection, Union and Complement, Independent and Dependent Events, Conditional Probability, Addition and Multiplication Rules, Bayes’ Theorem

4) Data Pre-Processing: Importing Libraries, Importing Dataset, Working with missing data, Encoding categorical data, Splitting dataset into train and test set, Feature scaling

5) Regression Analysis: Simple Linear Regression, Multiple Linear Regression, Support Vector Regression, Decision Tree, Random Forest Regression

6) Classification Techniques: Logistic Regression, KNN, Support Vector Machine, Decision Tree, Random Forest Classification

7) Natural Language Processing: Tokenization, Stemming, Lemmatization, Stop Words, Vocabulary and Matching, Parts of Speech Tagging, Named Entity Recognition, Sentence Segmentation

8) Artificial Neural Networks (ANNs): The Neuron, Activation Function, Cost Function, Gradient Descent and Back-Propagation, Building the Artificial Neural Networks, Binary Classification with Artificial Neural Networks

9) Convolutional Neural Networks (CNNs): Theory behind Convolutional Neural Networks, Different layers in Convolutional Neural Networks, Building Convolutional Neural Networks, Credit Card Fraud Detection with CNN

10) Recurrent Neural Network (RNNs): Theory behind Recurrent Neural Networks, Vanishing Gradient Problem, Working of LSTM and GRU, IMDB Review Classification with RNN - LSTM

11) Data Analysis with Numpy: NumPy Arrays, Indexing and Selection, NumPy Operations

12) Data Analysis with Pandas: Pandas Series, DataFrames, Multi-index and index hierarchy, Working with Missing Data, Groupby Function, Merging Joining and Concatenating DataFrames, Pandas Operations, Reading and Writing Files

13) Data Visualization with Matplotlib: Functional Method, Object Oriented Method, Subplots Method, Figure size, Aspect ratio and DPI, Matplotlib properties, Different type of plots like Scatter Plot, Bar plot, Histogram, Pie Chart

14) Python Crash Course: Part 1: Data Types,  Part 2: Python Statements, Part 3: Functions, Part 4: Object Oriented Programming


Learn Data Science to advance your Career and Increase your knowledge in a fun and practical way !


Regards,

Vijay Gadhave

Who this course is for:
  • Anyone who wants to learn Data Science and Machine Learning
  • Professionals who want to start a new career in Machine Learning
  • Anyone who is interested in Machine Learning and Data science