Machine learning & Data Science with R & Python

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Invest in yourself. Job market is changing like never before & machine learning & data science skills are increasingly becoming very important for developers and managers alike. All of us need to have a intuition and fundamental knowledge of machine learning and data science.

Learn machine learning & deep learning fundamentals, applied statistics, R & Python packages, data visualization with ggplot2, seaborn, matplotlib, pandas, keras 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.

Guided mentor-ship to groom you for the interviews & career.

14 Days Money Back (full refund) Guarantee if you don't like it.

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

• Programmers and developers looking to learn machine learning and data science
• Managers who are looking to develop fundamental knowledge of the domain
• Business analysts
• Students (Tech/MBAs)

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

Section 16- Deep learning with Keras & Python

• Introduction to Deep learning
• Fundamentals of deep learning

• Deep learning methodology

• Deep learning architecture

• Why Activation function in deep learning

• Relu & Softmax activation functions with Keras introduction

• Image recognition with Python, Keras & MLP- Part 1

• Image recognition with Python, Keras & MLP- Part 2

• Image recognition with Python, Keras & MLP- Part 3

• Image recognition with Python, Keras & MLP- Part 4

Next update will include

• CNN with tensorflow