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      • Machine Learning with Data Science using Python

      Machine Learning with Data Science using Python

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      Ramisha Rani K
      (4 reviews)
      ₹15,000.00 ₹499.00
      SocialMediaPostMaker_26092020_212727
      • Overview
      • Curriculum
      • Instructor
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      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
      Contradictory to Supervised Machine Learning, the unsupervised machine learning model tries to make predictions provided with unlabeled dataset. 
      • 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 798
      • Assessments Yes
      CoursesMachine Learning with Data Science using Python
      • Course Overview
        • Lecture1.1
          Welcome Note
        • Lecture1.2
          Download the Material
        • Lecture1.3
          The Three set of three secrets
      • Introduction to Artificial Intelligence
        • Lecture2.1
          What is Artificial Intelligence? 05 min
        • Lecture2.2
          What is Machine Learning? 03 min
        • Lecture2.3
          What is Deep Learning? 01 min
      • Anaconda Installation
        • Lecture3.1
          Download the Anaconda 03 min
        • Lecture3.2
          Create New Virtual Environment 06 min
        • Lecture3.3
          Install the Required Library 03 min
        • Lecture3.4
          Install Jupyter Notebook 03 min
      • Basics of Python: Oops
        • Lecture4.1
          Introduction to Programming 06 min
        • Lecture4.2
          Overview about class 04 min
        • Lecture4.3
          What is Oops
        • Lecture4.4
          Access operator/Dot Operator 09 min
        • Lecture4.5
          Python Basics-List,Dataset 08 min
        • Lecture4.6
          Python String 03 min
        • Lecture4.7
          Python Variable 04 min
      • Exploratory: Descriptive Analysis
        • Lecture5.1
          Introduction to Data Science 09 min
        • Lecture5.2
          Basic Terminology of Data Science: Key Term -1 10 min
        • Lecture5.3
          Basic Terminology of Data Science: Key Term -2
        • Lecture5.4
          Types of Variable 08 min
        • Lecture5.5
          Concepts under Data Science 09 min
        • Lecture5.6
          Topics Under Descriptive Analysis 05 min
        • Lecture5.7
          Data Science: Project Report 04 min
        • Lecture5.8
          What is outlier? 04 min
        • Lecture5.9
          What is Mean and Purpose of it 08 min
        • Lecture5.10
          What is Median and Purpose of it Copy 06 min
        • Lecture5.11
          Mean vs Median Copy 07 min
        • Lecture5.12
          Mode Copy 05 min
        • Lecture5.13
          Python: Dataset Explanation Copy 10 min
        • Lecture5.14
          Python: Mean, Median and Mode Copy 07 min
        • Lecture5.15
          Python: Quantitative and Qualitative Copy 11 min
        • Lecture5.16
          Python Class and Function Fair copy Copy 15 min
        • Lecture5.17
          Python Custom Table for Mean, Median and Mode Copy 11 min
        • Lecture5.18
          Percentile and Purpose of it Copy 11 min
        • Lecture5.19
          Python: Percentile and Lesser and greater range of outlier Copy 11 min
        • Lecture5.20
          Inter Quartile Range and Outlier 10 min
        • Lecture5.21
          Python IQR 09 min
        • Lecture5.22
          Python IQR: Lesser range outlier and Greater range outlier 09 min
        • Lecture5.23
          Python Fair Descriptive 08 min
        • Lecture5.24
          Python Detecting Lesser and Greater outlier 09 min
        • Lecture5.25
          Python Detecting Outlier Fair copy
        • Lecture5.26
          Cross Checking for replaced outlier 11 min
        • Lecture5.27
          Frequency , Relative Frequency and Cumulative Frequency 10 min
        • Lecture5.28
          Python: Frequency, Relative Frequency and Cumulative Frequency Copy 11 min
        • Lecture5.29
          Fair Frequency, Relative Frequency Copy 08 min
        • Lecture5.30
          Variance Copy 07 min
        • Lecture5.31
          Standard Deviation 09 min
        • Lecture5.32
          Python Standard Variance Copy 05 min
        • Lecture5.33
          Skewness and Kurtosis Copy 08 min
        • Lecture5.34
          Python Skewness, Kurt and Histogram Copy 05 min
      • Exploratory: Inferential Analysis
        • Lecture6.1
          What is Inference Analysis? 04 min
        • Lecture6.2
          Univariate ,Bivariate and Multivariate 06 min
        • Lecture6.3
          Concept Under each Analysis 03 min
        • Lecture6.4
          Co-Variance 07 min
        • Lecture6.5
          Correlation 08 min
        • Lecture6.6
          Python Covariance, Correlation 14 min
        • Lecture6.7
          Multicollinearity 11 min
        • Lecture6.8
          Variance Inflation Factor and Homoscedasticity 06 min
        • Lecture6.9
          Homoscedasticity and Heteroscedasticity 06 min
        • Lecture6.10
          Introduction univariate 10 min
        • Lecture6.11
          Probability Density Curve 05 min
        • Lecture6.12
          Python PDF 14 min
        • Lecture6.13
          Python CDF 05 min
        • Lecture6.14
          Normal Distribution 10 min
        • Lecture6.15
          Z-Score C 11 min
        • Lecture6.16
          Python Z-Score 03 min
        • Lecture6.17
          Types of Test 06 min
        • Lecture6.18
          Paired & Unpaired T-Test 06 min
        • Lecture6.19
          Python T Test Copy 05 min
        • Lecture6.20
          F Test Copy 02 min
        • Lecture6.21
          Hypothesis Testing Copy 15 min
        • Lecture6.22
          Analysis of Variance and One way Testing Copy 10 min
        • Lecture6.23
          Python Anavo One Way Testing Copy 02 min
        • Lecture6.24
          Anavo Two Analysis Copy 05 min
        • Lecture6.25
          Python Anavo Two way Testing Copy 05 min
        • Lecture6.26
          Python Two Post hoc 07 min
      • Data Visualization: Matplotlib
        • Lecture7.1
          Python_Matplotlib Copy 09 min
        • Lecture7.2
          Python_Line Plot Copy 02 min
        • Lecture7.3
          Python_Bar Plot Copy 08 min
        • Lecture7.4
          Python_Histogram Copy 05 min
        • Lecture7.5
          Python_Label Copy 03 min
        • Lecture7.6
          Python 3D-Meshgrid Copy 04 min
        • Lecture7.7
          Python Gradient Copy 04 min
      • Data Visualization: Seaborn
        • Lecture8.1
          Python_Seaborn_color Copy 03 min
        • Lecture8.2
          Dist & JointPlot Copy 06 min
        • Lecture8.3
          Python-Pairplot Copy 05 min
        • Lecture8.4
          Python Strip, Swarmplot Copy 05 min
        • Lecture8.5
          Python VoilinPlot & BoxPlot Copy 03 min
        • Lecture8.6
          Factorplot & Regression Copy 05 min
        • Lecture8.7
          LMplot Copy 04 min
      • Data Visualization: Tableau
        • Lecture9.1
          Download the tableau Copy 01 min
        • Lecture9.2
          How to load the file in Tableau Copy 03 min
        • Lecture9.3
          Bar Chart Copy 04 min
        • Lecture9.4
          Problem Statement-1 Copy 04 min
        • Lecture9.5
          Problem Statement-2 Copy 08 min
        • Lecture9.6
          Save the worksheet Copy 02 min
        • Lecture9.7
          Title for the chart Copy 02 min
      • Data Preprocessing
        • Lecture10.1
          Pre-processing Introduction Copy 06 min
        • Lecture10.2
          Dropping Unwanted the column Copy 02 min
        • Lecture10.3
          Spelling mistake check Copy 02 min
        • Lecture10.4
          Normalization Copy 02 min
        • Lecture10.5
          Nominal One hot Encoder Copy 04 min
        • Lecture10.6
          Ordinal Labelencoder Copy 02 min
        • Lecture10.7
          Python Null Check Copy 03 min
        • Lecture10.8
          Python Filling Null Values Copy 03 min
        • Lecture10.9
          Converting nominal to numbers Copy 03 min
        • Lecture10.10
          Python Categorical Imputer Copy 01 min
      • Introduction to Machine Learning-Must Watch
        • Lecture11.1
          Topics Under Machine Learning 03 min
        • Lecture11.2
          Demo of Chronic Kidney Disease 05 min
        • Lecture11.3
          Types of problem Statement 02 min
        • Lecture11.4
          Problem identification of Supervised Learning 03 min
        • Lecture11.5
          Problem identification of Unsupervised Learning 02 min
        • Lecture11.6
          Problem identification of Semisupervised Learning 05 min
        • Lecture11.7
          Problem Identification of Regression and Classification 03 min
        • Lecture11.8
          Algorithm Segregation 02 min
      • Regression: Supervised
        • Lecture12.1
          What is Simple Linear Regression 06 min
        • Lecture12.2
          Simple Linear Problem Identification 07 min
        • Lecture12.3
          Simple Linear Regression- Weight and Bais 05 min
        • Lecture12.4
          Simple Linear Regression -Model & Bestfitline 08 min
        • Lecture12.5
          Simple Linear Regression-Validation-SSE 05 min
        • Lecture12.6
          Simple Linear regression-Error 03 min
        • Lecture12.7
          Simple Linear Regression-Validation-SSR-SST 04 min
        • Lecture12.8
          Simple Linear Regression-R-Square 02 min
        • Lecture12.9
          Simple Linear Regression-Adjusted R-Square 04 min
        • Lecture12.10
          The purpose of training and test set 07 min
        • Lecture12.11
          Assumption for linear Regression 03 min
        • Lecture12.12
          Steps to be followed for Machine Learning model. 02 min
        • Lecture12.13
          Python-SLR-before the model 09 min
        • Lecture12.14
          Python SLR Model Bulid 08 min
        • Lecture12.15
          Python SLR Prediction 03 min
        • Lecture12.16
          Python SLR after the Model 08 min
        • Lecture12.17
          Multiple Linear Regression 04 min
        • Lecture12.18
          Python Multiple Linear before the model 08 min
        • Lecture12.19
          Python Multiple Linear After the Model 04 min
        • Lecture12.20
          Polynomial Regression 07 min
        • Lecture12.21
          Problem Statement for Non Linear Algorithm 03 min
        • Lecture12.22
          Types of Fitting 03 min
        • Lecture12.23
          Python Polynomial 08 min
        • Lecture12.24
          Support Vector Machine for Linear 03 min
        • Lecture12.25
          Support Vector Machine for Non-Linear 11 min
        • Lecture12.26
          Python Support Vector Regression 11 min
        • Lecture12.27
          Decision Tree Entropy 08 min
        • Lecture12.28
          Decision Tree Information Gain 07 min
        • Lecture12.29
          Python Decision Tree 06 min
        • Lecture12.30
          Random Forest 06 min
        • Lecture12.31
          Python Random Forest 05 min
        • Lecture12.32
          Lasso, Ridge and Elastic 05 min
        • Lecture12.33
          Python Lasso, Ridge and Elastic 06 min
      • Classification: Supervised
        • Lecture13.1
          Introduction to classification 03 min
        • Lecture13.2
          Demo for Classification Statement 04 min
        • Lecture13.3
          Difference between Regression and Classification 06 min
        • Lecture13.4
          Confusion Matrix 05 min
        • Lecture13.5
          Confusion Matrix_type1 & Tpye 2 Error 05 min
        • Lecture13.6
          Confusion Accuracy 03 min
        • Lecture13.7
          Classification Algorithm 03 min
        • Lecture13.8
          Logistic Algorithm 06 min
        • Lecture13.9
          K-Nearest Neighbour 08 min
        • Lecture13.10
          KNN-2 04 min
        • Lecture13.11
          Navie Bayes-1 07 min
        • Lecture13.12
          Navie Bayes-2 08 min
        • Lecture13.13
          Python Logistic 08 min
        • Lecture13.14
          Python Logistic-2 05 min
        • Lecture13.15
          Python SVM_Linear 04 min
        • Lecture13.16
          Python SVM Nonlinear 03 min
        • Lecture13.17
          Python Knn 03 min
        • Lecture13.18
          Python Navie Baye 02 min
        • Lecture13.19
          Python Decision Tree 02 min
        • Lecture13.20
          Python Random Forest 02 min
        • Lecture13.21
          Python All in one Algorithm 01 min
        • Lecture13.22
          Python Simplified 01 min
      • Clustering: Unsupervised
        • Lecture14.1
          Clustering 02 min
        • Lecture14.2
          Target Marketing 03 min
        • Lecture14.3
          K-Means 06 min
        • Lecture14.4
          K-Means-2 04 min
        • Lecture14.5
          Hierarchical Clustering 03 min
        • Lecture14.6
          Python K-Means-1 06 min
        • Lecture14.7
          Python K-Means-2 03 min
        • Lecture14.8
          Python K-Mean-3 03 min
        • Lecture14.9
          Conversion Unsupervised to Supervised Learning 02 min
        • Lecture14.10
          Python Hierachical 03 min
      • Feature Selection
        • Lecture15.1
          Why Feature Selection 07 min
        • Lecture15.2
          Feature Selection Vs Dimensionality Reduction 03 min
        • Lecture15.3
          Example for Feature Selection and Dimensionality Reduction 03 min
        • Lecture15.4
          Algorithm 02 min
        • Lecture15.5
          SelectK 04 min
        • Lecture15.6
          Recurssive Feature Elimination 04 min
        • Lecture15.7
          Feature Importance 03 min
        • Lecture15.8
          Blueprint for SelectKbest 06 min
        • Lecture15.9
          Python Selectkbest-1 06 min
        • Lecture15.10
          Python SelectKbest-2 07 min
        • Lecture15.11
          Python Selectkbest -Classification 3 08 min
        • Lecture15.12
          Python Selectkbest -Classification 4 06 min
        • Lecture15.13
          Python Selectkbest -Classification 5 06 min
        • Lecture15.14
          Python Selectkbest -Regression 05 min
        • Lecture15.15
          Python Selectkbest -Regression2 06 min
        • Lecture15.16
          Python RFE -Classification-1 07 min
        • Lecture15.17
          Python RFE -Classification-2 04 min
        • Lecture15.18
          Python RFE-Regression-1 01 min
        • Lecture15.19
          Python RFE-Regression2 05 min
      • Dimensionality Reduction
        • Lecture16.1
          Dimensionality Reduction and Scalar 03 min
        • Lecture16.2
          What is Vector 04 min
        • Lecture16.3
          Dimension 05 min
        • Lecture16.4
          Introduction to PCA 05 min
        • Lecture16.5
          Eigen Values & Eigen Vector 08 min
        • Lecture16.6
          LDA & Kernel PCA 08 min
        • Lecture16.7
          Python_PCA 08 min
        • Lecture16.8
          Python_LDA 04 min
        • Lecture16.9
          Python_kernal_pca 03 min
      • End to End Project: Classification
        • Lecture17.1
          Classification Problem Statement 07 min
        • Lecture17.2
          Classification Explaination 06 min
        • Lecture17.3
          How to choose final Model 03 min
        • Lecture17.4
          Final Model 06 min
      • End to End Project: Regression
        • Lecture18.1
          Regression Problem Statement
        • Lecture18.2
          Choosing the Model 04 min
        • Lecture18.3
          Fair Copy of Final Model Creation 07 min
      • Web Development
        • Lecture19.1
          Web Development 04 min
        • Lecture19.2
          Web Development Demo 03 min
        • Lecture19.3
          Create Project and App 07 min
        • Lecture19.4
          Setup Must watch 05 min
        • Lecture19.5
          Settings 06 min
        • Lecture19.6
          forms 03 min
        • Lecture19.7
          Models 03 min
        • Lecture19.8
          Input Html page 07 min
        • Lecture19.9
          Views 04 min
        • Lecture19.10
          Get and Put method 03 min
        • Lecture19.11
          Backend View 04 min
        • Lecture19.12
          Processed output 02 min
        • Lecture19.13
          Output Page 05 min
        • Lecture19.14
          App urls 05 min
        • Lecture19.15
          Project url 03 min
        • Lecture19.16
          Base Menu 03 min
        • Lecture19.17
          Database comment and local server 09 min
        • Lecture19.18
          Classification walkthrough 06 min
      author avatar
      Ramisha Rani K

      I am Ramisha Rani K.I have completed my M.Tech. in Communication Engineering at VIT,(Chennai Campus),Chennai. l have secured Second Topper of the VIT. Due to my interest and passion I have towards Artificial Intelligence, I serving as a Data Scientist. I focusing much on the field of Python, Machine Learning, Deep Learning, Natural Language Processing and Data Science.

      Artificial Intelligence Trainer with four years of experience which allowed me to know and to discuss with Industry Trends, Market Analysis and Research oriented opportunities.

      Belive in"Dreaming Big!Daring to spare!! Amaze yourself!!!" connecting passionate, enthusiastic and smart people to help them to find the best platfom.

      I have trained nearly 5000+ students, 500+Faculities in this domain.

      Reviews

      Average Rating

      5
      4 ratings

      Detailed Rating

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      • Soundarya S

        Soundarya

        Very easy to understand and grasp

      • Arunachalam PL

        Very informative and useful course

        The course was very useful and got to know many new terms in the concept

      • KARTHICKKUMAR M

        Informative course

        It was informative and thought provoking course

      • Keshav Kumar P

        Really well compiled course

        The course was really easy grasp the basics and importance of DS and ML

      • Overview
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      • Instructor
      • Reviews
      ₹15,000.00 ₹499.00

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