Machine Learning with Data Science using Python
Machine Learning with Data Science
Machine Learning is a utilization of Artificial Intelligence (AI) which makes the machines to learn on their own without clear-cut programming but loads algorithms to examine and generate decisions relying on the given input data. Data Science is the study of data involving the development of models and methods to juice out the required result. There is an enormous professional opportunity accessible in the field of data science. The vast majority of the up-and-comers are searching for a worthwhile vocation in this specific area.
What you will learn ?
- Introduction to Artificial Intelligence
Get introduced to the Buzz word Artificial Intelligence, Machine Learning and Deep Learning.
- Tools
Anaconda, Spyder, Jupyter Notebook, Virtual Environment.
- Basics of Python
Object Oriented Programming Language, Basics of Python
- Exploratory Data Analysis
Explores the data from different perspectives and tunes them finely thereby giving a cumulative result of the main characteristics obtained by analyzing the data set.
- Descriptive Data Analysis
Brings out the basics of data and by which the same suggests gives the entire description of the data. Under Descriptive Data Analysis, you’ll study various measures, samples and the procedures to work with them in detail.
- Inferential Data Analysis
Specifies the inferences that can be made from and within the data. In easy words, Inferential Data Analysis sorts out the various methodologies in forecasting the data.
- Data Pre-processing
Technique used to transform the unstructured, messy and raw data into structured, neat and understandable format in a stepwise manner.
- Supervised Machine Learning
Takes a set of accepted data to train the model and makes predictions based on the new data inputted.
- Unsupervised Machine Learning
- Dimensional Reduction
Phenomena to reduce the count of variables in a dataset required for machine learning.
- Feature Selection
One of the sub-divisions of Dimensionality Reduction which helps in processing the selection of data to train the model based on certain features.
- Web Development
To showcase your project and learning, you can host it use web development(Django Python)
Course Outcomes:
- By the end of your happy learning of data science with us,
- You’ll be able to build your programming skills.
- You’ll be able to perform statistical analysis of data.
- You’ll be able to construct your own models and analyze them in real-world contexts.
- You’ll be confident enough to work with the algorithms and tools required for data science.
- You’ll become a pro in data management.
- You’ll have a better understanding of the basic issues and objections of machine learning.
- You’ll learn to work with various machine learning approaches.
- You’ll be able to design and train your own models in real-time.
And what not? MUCH MORE!!!!!
Aren’t you enticed to be a DATA SCIENTIST?
Yes, right!!!
We are here to lead you!!!!!
Course Features
- Lectures 234
- Quizzes 0
- Students 812
- Assessments Yes
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Course Overview
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Introduction to Artificial Intelligence
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Anaconda Installation
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Basics of Python: Oops
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Exploratory: Descriptive Analysis
- Introduction to Data Science
- Basic Terminology of Data Science: Key Term -1
- Basic Terminology of Data Science: Key Term -2
- Types of Variable
- Concepts under Data Science
- Topics Under Descriptive Analysis
- Data Science: Project Report
- What is outlier?
- What is Mean and Purpose of it
- What is Median and Purpose of it Copy
- Mean vs Median Copy
- Mode Copy
- Python: Dataset Explanation Copy
- Python: Mean, Median and Mode Copy
- Python: Quantitative and Qualitative Copy
- Python Class and Function Fair copy Copy
- Python Custom Table for Mean, Median and Mode Copy
- Percentile and Purpose of it Copy
- Python: Percentile and Lesser and greater range of outlier Copy
- Inter Quartile Range and Outlier
- Python IQR
- Python IQR: Lesser range outlier and Greater range outlier
- Python Fair Descriptive
- Python Detecting Lesser and Greater outlier
- Python Detecting Outlier Fair copy
- Cross Checking for replaced outlier
- Frequency , Relative Frequency and Cumulative Frequency
- Python: Frequency, Relative Frequency and Cumulative Frequency Copy
- Fair Frequency, Relative Frequency Copy
- Variance Copy
- Standard Deviation
- Python Standard Variance Copy
- Skewness and Kurtosis Copy
- Python Skewness, Kurt and Histogram Copy
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Exploratory: Inferential Analysis
- What is Inference Analysis?
- Univariate ,Bivariate and Multivariate
- Concept Under each Analysis
- Co-Variance
- Correlation
- Python Covariance, Correlation
- Multicollinearity
- Variance Inflation Factor and Homoscedasticity
- Homoscedasticity and Heteroscedasticity
- Introduction univariate
- Probability Density Curve
- Python PDF
- Python CDF
- Normal Distribution
- Z-Score C
- Python Z-Score
- Types of Test
- Paired & Unpaired T-Test
- Python T Test Copy
- F Test Copy
- Hypothesis Testing Copy
- Analysis of Variance and One way Testing Copy
- Python Anavo One Way Testing Copy
- Anavo Two Analysis Copy
- Python Anavo Two way Testing Copy
- Python Two Post hoc
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Data Visualization: Matplotlib
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Data Visualization: Seaborn
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Data Visualization: Tableau
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Data Preprocessing
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Introduction to Machine Learning-Must Watch
- Topics Under Machine Learning
- Demo of Chronic Kidney Disease
- Types of problem Statement
- Problem identification of Supervised Learning
- Problem identification of Unsupervised Learning
- Problem identification of Semisupervised Learning
- Problem Identification of Regression and Classification
- Algorithm Segregation
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Regression: Supervised
- What is Simple Linear Regression
- Simple Linear Problem Identification
- Simple Linear Regression- Weight and Bais
- Simple Linear Regression -Model & Bestfitline
- Simple Linear Regression-Validation-SSE
- Simple Linear regression-Error
- Simple Linear Regression-Validation-SSR-SST
- Simple Linear Regression-R-Square
- Simple Linear Regression-Adjusted R-Square
- The purpose of training and test set
- Assumption for linear Regression
- Steps to be followed for Machine Learning model.
- Python-SLR-before the model
- Python SLR Model Bulid
- Python SLR Prediction
- Python SLR after the Model
- Multiple Linear Regression
- Python Multiple Linear before the model
- Python Multiple Linear After the Model
- Polynomial Regression
- Problem Statement for Non Linear Algorithm
- Types of Fitting
- Python Polynomial
- Support Vector Machine for Linear
- Support Vector Machine for Non-Linear
- Python Support Vector Regression
- Decision Tree Entropy
- Decision Tree Information Gain
- Python Decision Tree
- Random Forest
- Python Random Forest
- Lasso, Ridge and Elastic
- Python Lasso, Ridge and Elastic
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Classification: Supervised
- Introduction to classification
- Demo for Classification Statement
- Difference between Regression and Classification
- Confusion Matrix
- Confusion Matrix_type1 & Tpye 2 Error
- Confusion Accuracy
- Classification Algorithm
- Logistic Algorithm
- K-Nearest Neighbour
- KNN-2
- Navie Bayes-1
- Navie Bayes-2
- Python Logistic
- Python Logistic-2
- Python SVM_Linear
- Python SVM Nonlinear
- Python Knn
- Python Navie Baye
- Python Decision Tree
- Python Random Forest
- Python All in one Algorithm
- Python Simplified
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Clustering: Unsupervised
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Feature Selection
- Why Feature Selection
- Feature Selection Vs Dimensionality Reduction
- Example for Feature Selection and Dimensionality Reduction
- Algorithm
- SelectK
- Recurssive Feature Elimination
- Feature Importance
- Blueprint for SelectKbest
- Python Selectkbest-1
- Python SelectKbest-2
- Python Selectkbest -Classification 3
- Python Selectkbest -Classification 4
- Python Selectkbest -Classification 5
- Python Selectkbest -Regression
- Python Selectkbest -Regression2
- Python RFE -Classification-1
- Python RFE -Classification-2
- Python RFE-Regression-1
- Python RFE-Regression2
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Dimensionality Reduction
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End to End Project: Classification
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End to End Project: Regression
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Web Development
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Soundarya S
Soundarya
Very easy to understand and grasp
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Arunachalam PL
Very informative and useful course
The course was very useful and got to know many new terms in the concept
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KARTHICKKUMAR M
Informative course
It was informative and thought provoking course
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Keshav Kumar P
Really well compiled course
The course was really easy grasp the basics and importance of DS and ML
