Machine learning & Data Science with R & Python for 2019
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Invest in yourself in 2019. Job market is changing like never before & without machine learning & data science skills in your cv, you can't do much.
You will get everything you need to start your career as data scientist.
Learn machine learning fundamentals, applied statistics, R & Python packages, data visualization with ggplot2, seaborn, matplotlib, pandas and build machine learning models with R, pandas, numpy & scikit-learn using rstudio & jupyter notebook.
More than 15 projects to build your portfolio, Code files included & 30 Days full money Refund guarantee.
Guided mentor-ship to groom you for the interviews & career.
30 Days Money Back (full refund) Guarantee if you don't like it.
Everybody else is learning machine learning & data science, don't fall behind. Join today.
Unlike most machine learning courses out there, the Complete Machine Learning & Data Science with R & Python-2019 is affordable and comprehensive. Here are some highlights of the program:
- Anaconda distribution & Jupyter notebook overview
- Pandas
- Numpy
- Scikit Learn library
- Seaborn & Matplotlib for visualization with Python
- Visualization with R for machine learning
- Applied statistics for machine learning
- Evaluation Metrics for Regression & Classification like MSE, RMSE, MAE etc.
- Machine learning fundamentals
- ANOVA Implementation with R
- Linear regression with R & Python
- Logistic Regression with R & Python
- Dimension Reduction Technique
- Tree-based machine learning techniques
- KNN Implementation
- Naïve Bayes
- Neural network machine learning technique
What you will get
- Career guidance on data scientist roles and how to get into it
- How to build your portfolio to show your skills
- Over 10 projects to add to your portfolio
- Quizzes and projects to sharpen your skills
What you need
- Laptop/desktop/mobile phone with internet to watch videos
- Laptop/desktop to practice your knowledge
- Desire to learn machine learn
Intended Audience
- Anybody who is looking to get into data science or machine learning roles
Course Details
Section 1- Introduction to course and machine learning
- Introduction to Artificial Intelligence, Data Science, Business Analytics & Machine learning
- Data Science career opportunities and job details in field of machine learning.
- Career planning guidance.
- How to build your machine learning portfolio for better job opportunities
Section 2- R Fundamentals
- Introduction to R and R studio
- How to download R and r studio
- R Fundamentals covering following topics;
- Vectors, Matrx & Data frames
- Data Types
- Variables & objects in R
- Various Operators in R
- Loops – while, for
- Conditional- if, else
- Functions
Section 3- Data Wrangling with R
- Reading files in various format
- Data selection with R in detail
- Data manipulation with R in details
- Handling missing values
Section 4- Visualization with R for machine learning
- Histogram Vs bar plots
- Bar plots and histograms in R
- Horizontal bar plots & plots
- Heatmaps in R
- Scatter plots
- Create maps
- Pair plots for coefficients
- Mfpar for better visualization
- Data visualization with ggplot2
- Data visualization with lattice & 3d plots with scatter3d plot
Section 5- Applied statistics for machine learning
- Introduction to statistics for machine learning
- Descriptive vs Inferential analysis
- Descriptive statistics- Mean, Median, Mode & Range
- Standard error
- Central limit theorem with confidence interval & p value
- Overview of T test & f ratio
- Hypothesis testing
Section 6- Machine learning fundamentals
- Machine learning fundamentals
- Regression fundamentals
- Classification fundamentals
- Clustering fundamentals
- Data Reduction technique with clustering and dimension reduction technique introduction with PCA.
Section 7- ANOVA Implementation with R
- ANOVA introduction and fundamentals
- 1 Way and 2 way anova implementation with R
Section 8- Linear regression with R
- Linear regression fundamentals and detailed explanation
- Linear regression implementation in R
Section 9- Logistic Regression
- Logistic regression fundamentals & detailed explanation
- Logistic regression implementation with R & Python.
Section 10- Dimension Reduction Technique
- Dimension reduction technique introduction with focus on PCA.
- Principal component analysis implementation with R
Section 11 -Clustering
- Clustering overview with K-means clustering explanation
- K-means implementation with R
Section 12- Tree based machine learning techniques
- CART model (classification & regression tree) Introduction
- CART implementation in R
- Introduction to ensemble techniques with focus on random forest
- Random forest machine learning model implementation with R
Section 13- KNN Implementation
- KNN Fundamentals (k nearest neighbor)
- KNN Implementation with R
Section 14- Naïve Bayes
- Introduction to naïve bayes and fundamentals
- Naïve bayes implementation with R
Section 15- Neural network machine learning technique
- Introduction to neural network technique
- Implementation of neural network with R
Section 16- Machine learning & Data science with Python
- Introduction to machine learning with python
- Walk through of anaconda distribution & Jupyter notebook
- Numpy
- Pandas
- Data analysis with Python & Pandas
Section 16- Data Visualization with Python
- Data Visualization with Pandas
- Data visualization with Matplotlib
Next update will include
- Image recognition with Keras & Python
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