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Course type: Online Instructor led Course

Platform: Zoom

Course ID: 45392

What is this course about ?

Equip students to use Python and R for performing the different statistical data analysis and visualization tasks both prior to and after data modelling.

Introduce some of the most important machine learning concepts to students in the most practical way such that the students can apply these concepts for practical data analysis and interpretation.

Students will get a strong background in some of the most important statistical modelling, machine learning,deep learning concepts along with basic exploratory data analysis (grouping visualization) skills.

Students will work with real life data - data from different sources and get acquainted with the modalities of working with actual data.

The syllabus for the entire course is given below :

__Python for Data Science:-__

- Introduction to Python Data Science tool
- Introduction to Python Data Science environment
- IPython Interpreter
- Different types of data used in Statically and Machine Learning analysis
- Different types of data used programmatically
- Python Data science packages to be used
- Numpy Introduction
- Create Numpy Arrays
- Numpy operations
- Matrix Arithmetic and Linear systems
- Numpy for basic Vector Arithmetic
- Numpy for Basic Matrix Arithmetic
- Broadcasting with NumPy
- Solve equations with Numpy
- NumPy for statistical Operations
- Data structures in Python
- Read in Data
- Read in CSV data using Pandas
- Read in excel data using Pandas
- Read in JSON data
- Read in HTML data
- Removing NAs/No Value from our data
- Basic Data Handling:Starting with conditional Data selection
- Drop Column/Row
- Subset and Index data
- Basic data grouping based on Qualitative attributes
- Cross tabulations
- Reshaping
- Pivoting
- Rank and sort data
- Concatenate
- Merging and joining Data Frames
- What is Data Visualization
- Some theoretical concepts behind data visualizations
- Histograms â?? Visualize the Distribution of Continuous Numerical Variables
- Boxplots - â?? Visualize the Distribution of Continuous Numerical Variables
- Scatter Plot-Visualize the Relationship between 2 continuous Variables
- Barplot
- Pie Chart
- Line Chart

__MACHINE LEARNING - HANDS ON PYTHON AND R IN DATA SCIENCE__

- Applications of Machine Learning
- Why Machine Learning is the future
- Installing Python and Anaconda
- Installing R and R studio
- Data Pre-processing
- Regression
- Linear Regression in Python
- Linear Regression in R
- Single Linear Regression
- Multiple Linear Regression in Python
- Multiple Regression in R
- What is the P-Value?
- Polynomial Regression
- Polynomial Regression in Python
- Polynomial Regression in R
- R Regression Template
- Support Vector Regression (SVR)
- SVR in Python
- SVR in R
- Decision Tree Regression
- Decision Tree Regression in Python
- Decision Tree Regression in R
- Random Forest Regression
- Random Forest Regression in Python
- Random Forest Regression in R
- Evaluating Regression Model Performance
- R-Squared intuition
- Adjusted R-Squared Intuition
- Interpreting Linear Regression Coefficients
- Logistic Regression
- Logistic Regression in Python
- Logistic Regression in R
- K-Nearest Neighbours (K-NN)
- K-NN in Python
- K-NN in R
- Support Vector Machine (SVM)
- SVM in Python
- SVM in R
- Kernel SVM
- Kernel SVM Intuition
- Mapping to a higher dimension
- The Kernel Trick
- Types of Kernel Functions
- Kernel SVM in Python
- Kernel SVM in R
- Bayes Theorem
- Naive Bayes Intuition
- Naive Bayes In Python
- NaÃ¯ve Bayes in R
- Decision Tree Classification in Python
- Decision Tree Classification in R
- Random Forest Classification
- Random Forest Classification in Python
- Random Forest Classification in R
- Evaluating Positives and False Negatives
- Confusion Matrix
- Accuracy Paradox
- Cap Curve
- Cap Curve Analysis
- K-Means Clustering
- K-Means Random Initialization Trap
- K-Means Selecting the number of Clusters
- K-Means Clustering in Python
- K-Means Clustering in R
- Hierarchical Clustering
- Hierarchical Clustering using Dendograms
- Hierarchical Clustering in Python
- Hierarchical Clustering in R
- Apriori
- Apriori in Python
- Apriori in R
- Eclat in R
- Upper Confidence Bound
- The Multi Armed Bandit problem
- Upper Confidence Bound in Python
- Upper Confidence Bound in R
- Thomson Sampling
- Thomson Sampling in Python
- Thomson Sampling in R
- Natural Language Processing
- Natural Language Processing in Python
- Natural Language Processing in R
- Deep Learning
- Activation Neural Networks
- The Neuron
- The Activation Framework
- How do Neural Networks work?
- How do Neural Networks learn?
- Gradient Descent
- Stochastic Gradient Descent
- Backpropagation
- ANN in Python â?? Installing Theano,Tensorflow and Keras
- ANN in Python
- ANN in R
- Convolutional Neural Networks
- What are convolutional neural networks
- Convolution operation
- ReLU Layer
- Pooling
- Flattening
- Full Connection
- Softmax and Cross-Entropy
- CNN in Python
- CNN in R
- Dimensionality Reduction
- Principal Component Analysis (PCA)
- PCA in Python
- PCA in R
- Linear Discriminant Analysis (LDA)
- LDA in Python
- LDA in R
- Kernel PCA
- Kernel PCA in Python
- Kernel PCA in R
- Model Selection and Boosting
- K-fold Cross Validation in Python
- K-fold cross validation in R
- Grid Search in Python
- Grid Search in R
- XG Boost
- XG Boost in Python
- XG Boost in R

Not decided yet.

3 Avg Rating

8 Reviews

15 Students

3 Courses

Data Scientist with 20 years of technology experience in leading IT Multinationals like IBM, British Telecom, AT&T in India and abroad.

Conduct Corporate training on Python, Data Science, Machine Learning, Artificial Intelligence and Deep Learning.

Achievement - Recently conducted a 12 weeks (480 hours ) of Data Science , Machine Learning and Deep Learning with Python Corporate training at Leeds in United Kingdom for a well known Software services company in UK.

Also conducted Data Science training at different corporates in India and abroad.

Conducts online and class room training for Engineering graduates and working professional.

My strengths - hands on learning through lots of practical examples tough subjects like Machine Learning, Data Science algorithms, Deep Learning , Artificial Intelligence using Python libraries.

Join my demo class to know me more. Most of my students are well placed in TCS, IBM, ESPN etc as Data Scientists.

Conduct Corporate training on Python, Data Science, Machine Learning, Artificial Intelligence and Deep Learning.

Achievement - Recently conducted a 12 weeks (480 hours ) of Data Science , Machine Learning and Deep Learning with Python Corporate training at Leeds in United Kingdom for a well known Software services company in UK.

Also conducted Data Science training at different corporates in India and abroad.

Conducts online and class room training for Engineering graduates and working professional.

My strengths - hands on learning through lots of practical examples tough subjects like Machine Learning, Data Science algorithms, Deep Learning , Artificial Intelligence using Python libraries.

Join my demo class to know me more. Most of my students are well placed in TCS, IBM, ESPN etc as Data Scientists.

1

Average Rating

V

Vijaykumar

He is the worst teacher I have meet. Also cheater. First of all he does not know python basic coding like simple interest function.

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