Data Science 2020: Data Science & Machine Learning in Python

*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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What you'll learn

- Go from total beginners to confident machine learning engineer
- Apply Machine Learning algorithm on 10+ dataset
- Refresh all basic statistics & Probability Concepts
- Get complete Environment ready with Google Colab Notebook
- Machine Learning with different kind of ML System
- Handle missing data, Grouping, Merging Joining and Concatenating Data wih Pandas Dataframe
- Transform your data with One Hot Encoding & Feature scaling
- Calculate Grades using Simple Linear Regression
- Predict Restaurant Profit with Multiple Linear Regression
- Apply SVR, SVM, Decision tree and Random Forest on Real Dataset
- Apply different classification Algorithm
- Classify Fashion clothes image with Artificial Neural Network + Keras
- Build Credit Card Fraud Detection with Convolution Neural Network
- Apply Natural Language Processing Technique like Tokenization, Stemming, Stop Words, Named Entity Recognition, Sentence Segmentation
- Classify IMDB Review using Recurrent Neural Network - LSTM

Requirements

- No prior knowledge or experience needed, only passion to learn

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

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