Data Science Training

Description

Hello everyone and welcome to this course on Data Science !

With this course you will have the Complete Understanding of Data Science and Machine Learning from Very Beginning !

Main topics covered in this course,

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

Please ask questions in QNA section if you need any assistance !