Machine learning with Python
10000 Learners
Course Objective:
Objective: Professionals who are want to learn ML with Python training but have no prior knowledge of Python, need to begin with of the following course.
Prerequisite: No expertise is required; however basic knowledge High School Mathematics and Statistics will be required.
Course Content:
TOPICS TO BE COVERED:
- What is Machine learning
- The three different types of machine learning
- Supervised, unsupervised, reinforcement learning
- An introduction to the basic terminology and notations
- A roadmap for building machine learning systems
- Uses of Machine learning in real life example
- Introduction to Python language
- Environmental setup for Python, Anaconda, Jupyter, Spider etc
- Data types of Python, numbers, string
- Run a python program in jupyter/spider/ipython
- If, elif, Loops in python
- Functions in python
- Positional argument, keyword argument, default argument
- Lambda function
- Module of Python
- Build in and user defined module
- Import a module, call a function of a module.
- Understand Jupyter notebook & Customize Settings
- Concept of Packages/Libraries - Important packages(NumPy, SciPy, scikit-learn, Pandas, Matplotlib, etc)
- Installing & loading Packages & Name Spaces
- Data Types & Data objects/structures (strings, Tuples, Lists, Dictionaries)
- List and Dictionary Comprehensions
- Variable & Value Labels – Date & Time Values
- Basic Operations - Mathematical - string - date
- Reading and writing data
- Simple plotting
- Control flow & conditional statements
- Debugging & Code profiling
- How to create class and modules and how to call them?
- Introduction to Object oriented language
- Class, object
- Create class and object in python
- Data hiding, method overloading
- Inheritance, method overriding
- Creating and accessing strings
- Basic functions on strings
- Indexing and slicing on string
- Strings methods
- List and its methods
- Accessing lists
- Tuple, set, dictionary and their methods
- Indexing, slicing on list, tuple, set , dictionary
- List comprehension and its uses
- Basic Statistics - Measures of Central Tendencies and Variance
- Building blocks - Probability Distributions - Normal distribution - Central Limit Theorem
- Inferential Statistics -Sampling - Concept of Hypothesis Testing
- Statistical Methods - Z/t-tests( One sample, independent, paired), Anova, Correlations and Chi-square
- Important modules for statistical methods: Numpy, Scipy, Pandas
- Introduction to Numpy
- Creating 1D and 2D Numpy array
- Slicing and indexing of Numpy Array
- Operations on Numpy array
- Introduction to Pandas
- Series and DataFrame
- Operations on series and dataframe
- Different functions on dataframe
- Pandas plotting functions
- Read external dataset using Pandas
- What is Data Wrangling
- Prepare data for use
- Introduction to sklearn module of python
- Find for missing value and impute
- Fillna() and Imputer() of sklearn
- Concepts of categorical variable and its problem
- Solution for categorical problem using dummy variable, LableEncoding
- Feature Scaling and its solution using StandardScalar
- Using pandas plotting functions
- Introduction to Matplotlib and Seaborn
- Difference between the two
- Scatter plot using matplotlib
- Draw histogram, barchart, pie chart to any data
- Create pairplot using seaborn
- Concept of Box plot in seaborn
- Drawing of 3D graphs
- Concept of Conditional Probability
- Bayes Theorem and Its Applications
- Naïve Bayes for classification
- Applications of Naïve Bayes in Classifications.
- Explain supervised
- Difference between classification and regression
- Single and Multiple Linear Regression
- Mathematics behind linear regression
- Simple linear regression with example
- Multiple linear regression with Boston dataset
- R2 score and RMSE score
- Logistic regression to solve classification problem
- Apply logistic regression on titanic dataset
- Classification report and confusion matrix
Projects:
- House price prediction
- Big mart sales prediction
- Stock market analysis
- Loan prediction
- Problem for non linear regression
- Discussion on tree based algorithm
- Decision tree regressor with example
- Mathematics behind decision tree
- Pruning in decision tree
- Concepts of ensemble algorithm
- Random forest regressor with example
- Gradient descent algorithm
- Mathematics behind Gradient descent
- Understand the various elements of machine learning
algorithm like parameters, hyper parameters, loss
function, cost function and optimization.
- Classification algorithm and its uses
- Different types of classifiers
- K Nearest Neighbour classifier (KNN)
- Mathematics behind KNN
- Apply KNN on IRIS dataset
- Accuracy test of classification
- Decision tree classifier for classification problem
- Random forest classifier with example
- What is naïve bayes classifier
- How naïve bayes work?
- Mathematics behind naïve bayes
- Text based classification using Naïve Bayes algorithm
- Classify email using Naïve Bayes classifier
- CountVectorizer and TFID algorithm to solve text classification problem
- How to recognize pattern in machine learning
- Use of MNIST dataset to recognize some hand written digits
- Feature extraction and Feature elimination
- PCA to use Dimensionality reduction
- Problem of time complexity and visualization
- Solution using PCA
- Use PCA to recognize MNIST dataset
- Model selection and its uses
- Cross validation
- K- fold cross validation
- What is unsupervised learning
- Types of unsupervised learning
- Concepts of clustering
- K means clustering to cluster group of datas
Projects:
- Handwritten digit recognition
- Email spam filtering
- Customer churn classification
- Black Friday sale analysis
- Heart disease prediction
- What is support vector machine
- Linear SVM
- Solving non linear problem using SVM
- Kernel and kernel trick to solve non linear problem
- Polynomial and RBF kernel
- Gamma and C parameter in SVM
- Effect of gamma and C
- What is association rule and its parameter
- Measure of effectiveness of the rule
- Market basket analysis for association
- Recommendation engine
- Content based filtering
- Collaborating filtering
- User-user and item –item collaboration
- Example using Movie Lens dataset
- What is Time Series Analysis (TSA)
- Components of TSA
- ARMA and ARIMA model
- Movie Average(MA) model
- Choosing the model
- Model implementation in python
- Analysis of GDP growth of UK
- Boosting algorithm and how it works
- Types of boosting algorithm
- AdaBoost and gradient boosting algorithm
- Mathematics behind boosting algorithm/li>
- Boosting algorithm with example
- Concept of Ensembling
- Manual Ensembling Vs. Automated Ensembling
- Methods of Ensembling (Stacking, Mixture of Experts)
- Bagging (Logic, Practical Applications)
- Random forest (Logic, Practical Applications)
- Boosting (Logic, Practical Applications)
- Ada Boost
- Gradient Boosting Machines (GBM)
- XGBoost.
- Concepts of neural network and deep learning
- Introduction to tensor flow
- Tensor flow constant, variable and place holder
- Artificial neural network and tensor flow
- ANN, RNN, and CNN
Projects:
- Movie recommendation system
- Face detection
- Object recognition
- Text classification
- Sentimental analysis