Topics:

• What is Data Science and what does a data scientist do. 
• Various examples of Data Science in the industries and how Python
• is deployed for  
• Data Science applications 
• Various steps in Data Science process like data wrangling, data exploration and   selecting the model

Topics:

• Introduction to a basic construct in Python 
• Understanding indentation like tabs and spaces 
• Code comments like Pound # character, names and variables 
• Python built-in data types like containers (list, set, tuple and dict), numeric (float, complex, int), text sequence (string), constants (true, false, ellipsis) and others classes  
• instances, modules, exceptions and more) 
• Basic operators in Python like logical, bitwise, assignment,
• comparison and more, slicing and the slice operator 
• Loop and control statements like break, if, for, continue, else, range() and more.
• List List comprehension and Errors and Exception Handling
• Map and Filter
• File Handling

Topics:

• Introduction to mathematical computing in Python 
• What are arrays and matrices, array indexing, array math, ND-array object 
• Array, different ways of creating array, multidimensional arrays
• Reshaping arrays, mathematical operations with arrays
• Accessing component, transpose,insert, change values of arrays

Topics:

• Fundamentals of dataframes and Data types, Reading in data, size,
• shape, getting index & columns of data,Slicing data, replacing
• columns, filtering, joining, pivoting and sorting data.

Data Wrangling:

• Missing value handling, data formatting, data normalization, binning, Turning categorical variables to quantitative, Joins

EDA:

• Descriptive Statistic, Summarizing data, remove duplicates, group by on data, Correlation and methods on dataframe

Topics:

• Probability Refresher (Bayes Theorem)
• Descriptive Statistics - Mean,Mode,Median
• Variance and Standard Deviation
• Percentiles, Quartiles and Interquartile Range (IQR)
• Confidence Interval
• Central Limit Theorem
• Correlation and Covariance
• Probability Distribution (Normal, binomial)
• Hypothesis Testing and Statistical Significance
• Null and Alternative Hypothesis
• Z test, T test, Chi Square test

Topics:

• What is exploratory data analysis and building of hypothesis, plotting and other techniques.
• Getting Started with Matplotlib
• Pyplot
• Figure class
• Axes Class
• Setting Limits and Tick labels
• Multiple Plots
• What is a Legend?
• Creating Different Types of Plots
• Line Graph
• Bar chart
• Histograms
• Scatter Plot
• Pie Chart
• PairPlot
• Working with Images
• Customizing Plots in Matplotlib
• More articles on Matplotlib
• Exercises, Applications and Projects

Topics:

• Need of Machine Learning
• Introduction to Machine Learning
• Types of Machine Learning, such as supervised, unsupervised and
• reinforcement learning
• Why Machine Learning with Python and applications of Machine
• Learning.

Topics:

• What is supervised learning, classification 
• Decision Tree, algorithm for Decision Tree induction 
• Confusion Matrix 
• Random Forest 
• Naïve Bayes, working of Naïve Bayes, how to implement Naïve Bayes Classifier 
• Support Vector Machine, working process of Support Vector Mechanism 
• What is Hyper Parameter Optimization 
• Comparing Random Search with Grid Search

Topics:

• Introduction to supervised learning
• Types of supervised learning - regression and classification
• Introduction to regression
• Simple linear regression
• Multiple linear regression, 
• Assumptions in linear regression, and math behind linear regression.
• Hands-on Exercise – Linear Regression and Train-Test Implementation

Topics:

• Introduction to classification
• Linear regression vs logistic regression
• Math behind logistic regression with detailed formulas
• log it function and odds
• Confusion matrix and accuracy
• True positive rate v/s false positive rate
• Threshold evaluation with ROCR.
• Hands-on Exercise – Logistic regression, Confusion matrix Implementation

Topics:

• Introduction to tree-based classification
• Understanding a decision tree
• Impurity function and entropy to understand the concept of
• information gain for the right split of node
• Gini index 
• Overfitting
• Pruning, pre-pruning, post-pruning, cost-complexity pruning
• Introduction to ensemble techniques
• Understanding bagging
• Introduction to random forests
• Finding the right number of trees in a random forest.
• Hands-on Exercise – Decision tree Implementation and hyper
• parameters in the random forest.

Topics:

• How a save a model using Pickle

Topics:

• Introduction to unsupervised learning, use cases of unsupervised learning 
• What is K-means clustering, understanding the K-means clustering algorithm 
• Hierarchical clustering and K-means clustering and how does hierarchical clustering work 
• Types of unsupervised learning
• Clustering and dimensionality reduction
• Types of clustering
• Introduction to k-means clustering
• Math behind k-means.
• Hands-on Exercise – K-Means

Topics:

• Importance of Dimensions
• Why Dimensionality Reduction
• PCA and its implementation
• Hands On: - PCA

Topics:

• Role of Machine Learning in field of Artificial Intelligence
• Deep Learning v/s Machine Learning 
• Brief History of AI Recap: SL, UL and RL 
• Classification and regression in supervised learning, 
• Clustering and association in unsupervised learning
• Algorithms that are used in these categories
• Introduction to AI and neural networks
• Deep Learning: Successes Last Decade 
• What is AI and Deep Learning 
• Discussion: Self-Driving Car Object Detection 
• Applications of Deep Learning 
• Challenges of Deep Learning

Topics:

• Multi-layer network introduction Regularization
• Deep neural networks
• Multi-layer perceptron
• Overfitting and capacity
• Neural network hyperparameters
• Different activation functions used in neural networks: - ReLu, Softmax, Sigmoid, and hyperbolic functions
• Back propagation, forward propagation, convergence, hyper parameters

Topics:

• Various methods that are used to train artificial neural networks
• Gradient descent rule
• Tuning the learning rate
• Regularization techniques
• Optimization techniques
• Stochastic process
• Vanishing gradients
• Dropout layer 
• Regression techniques: - including Lasso L1 and Ridge L2

Topics:

• Understanding how Deep Learning works
• Activation functions
• Illustrating perceptron
• Perceptron training
• Multi-layer perceptron
• Key parameters of perceptron
• Pytorch
• Pytorch and Its Ecosystem
• Use-case implementation
• Batch normalization
• Neural network training process customization.
• Hands On: Build a Deep Learning Model Using Pytorch

Topics:

• Introduction to deep neural networks (DNNs)
• Several building blocks of artificial neural networks (ANNs), 
• The architecture of DNN and its building blocks
• various parameters, layers, and optimization algorithms in DNN
• activation functions.

Topics:

• What is a convolutional neural network?
• Understanding the architecture and use-cases of CNN
• Pooling layers 
• Visualize CNN
• How to fine-tune a convolutional neural network
• Transfer learning
• Kernel filter
• Feature maps, and pooling

Topics:

• Introduction to Natural Language Processing (NLP)
• Introduction to text mining
• Introduction to Sentiment  
• Natural Language Toolkit (NLTK) environment and text mining: its cleaning, pre-processing and text classification.
• Hands-on Exercise – NLTK implementation