Free ebook on machine learning and deep learning with Python, covering data analysis, neural networks, TensorFlow, deployment, and AI ethics.
Free ebook + audiobook content
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Introduction to Machine Learning
+ Exercise: Which of the following is a type of learning in Machine Learning as described in the text? 4 minutes
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Python Fundamentals for Data Science
+ Exercise: Which of the following statements about Python is true, according to the text provided? 5 minutes
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Configuring the Development Environment
+ Exercise: What is the recommended first step to setting up a suitable development environment for Machine Learning (ML) and Deep Learning (DL) with Python? 6 minutes
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Data Manipulation with Pandas
+ Exercise: What is the main purpose of the Pandas library in Python? 5 minutes
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Exploratory Data Analysis with Matplotlib and Seaborn
+ Exercise: Which of the following statements about exploratory data analysis (EDA) and visualization libraries in Python is correct? 4 minutes
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Importing libraries: Matplotlib and Seaborn
+ Exercise: What is the purpose of Exploratory Data Analysis (AED) and what libraries are mentioned in the text to accomplish this task? 5 minutes
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Initial data loading and inspection
+ Exercise: Which of the following statements about Exploratory Data Analysis (AED) is CORRECT? 5 minutes
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Data cleaning and preparation
+ Exercise: Which of the following correctly describes a step in exploratory data analysis (EDA) and a corresponding data visualization technique? 5 minutes
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Univariate analysis
+ Exercise: Which of the following statements is true about univariate analysis and the Python libraries mentioned in the text? 6 minutes
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Bivariate analysis
+ Exercise: Which of the following best describes the purpose of bivariate analysis in Exploratory Data Analysis (EDA)? 6 minutes
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Visualizing categorical data
+ Exercise: Which of the following statements about visualizing categorical data with Matplotlib and Seaborn is correct? 7 minutes
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Visualizing continuous data
+ Exercise: Which of the following statements is true about exploratory data analysis (AED) as described in the text? 6 minutes
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Histograms, boxplots and scatter plots
+ Exercise: Which of the following statements about exploratory data analysis (AED) is true based on the text provided? 6 minutes
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Line plots for time series
+ Exercise: Which of the following statements about creating line graphs for time series with Python is correct? 6 minutes
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Customizing graphs
+ Exercise: What method is used to add a title to a plot in Matplotlib? 6 minutes
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Correlation and heatmap analysis
+ Exercise: Which of the following statements is true about correlation analysis and creating heatmaps as described in the text above? 6 minutes
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Using pairplots to visualize multidimensional relationships
+ Exercise: Which statement is true about the use of pairplots in exploratory data analysis (AED)? 5 minutes
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Saving visualizations to files
+ Exercise: Which of the following methods is used to save data visualizations created with Matplotlib and Seaborn to files such as PNG, JPG, and others? 5 minutes
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Interpretation and conclusions from visualizations
+ Exercise: Which of the following statements about exploratory data analysis (AED) with Matplotlib and Seaborn is true? 4 minutes
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Basic Statistical Concepts for Machine Learning
+ Exercise: Which of the following statements is true about statistical concepts applied in Machine Learning (ML) and Deep Learning (DL)? 5 minutes
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Principles of Supervised Learning
+ Exercise: Which of the following principles is NOT mentioned as one of the foundations of supervised learning in the text provided? 4 minutes
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Definition of Supervised Learning
+ Exercise: Which of the following components is NOT mentioned in the text as one of the key components of Supervised Learning? 4 minutes
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Datasets: Training and Testing
+ Exercise: Which of the following statements is true about supervised learning and splitting data sets? 4 minutes
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Classification Algorithms
+ Exercise: Which of the following concepts is INCORRECT regarding supervised learning and classification algorithms, based on the text provided? 4 minutes
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Regression Algorithms
+ Exercise: Which of the following regression algorithms is known to add a penalty term to the Mean Squared Error (MSE) to prevent overfitting, especially in situations with multicollinearity? 5 minutes
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Performance Assessment Metrics
+ Exercise: Which of the following performance metrics of a Machine Learning model is especially useful when seeking a balance between precision and recall and there is an uneven distribution of classes? 4 minutes
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Cross Validation
+ Exercise: Which of the following statements about cross-validation is true based on the text provided? 5 minutes
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Overfitting and Underfitting
+ Exercise: Which of the following correctly describes the concept of overfitting in Machine Learning (ML) and a technique to avoid it? 4 minutes
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Regularization
+ Exercise: Which of the following statements about regularization is correct according to the given text? 4 minutes
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Model Selection
+ Exercise: Which technique is critical in model selection to provide a reliable estimate of model performance on unseen data and help detect overfitting or underfitting problems? 4 minutes
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Hyperparameter Optimization
+ Exercise: Which of the following statements is true about hyperparameter optimization in the context of supervised learning in machine learning (ML)? 5 minutes
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Feature Engineering
+ Exercise: Which of the following statements best describes the Feature Engineering process in the context of supervised learning? 5 minutes
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Class Balancing
+ Exercise: Which of the following statements about class balancing in machine learning supervised learning is correct? 4 minutes
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Model Interpretability
+ Exercise: Which of the following statements best describes the importance of interpretability in supervised learning models? 4 minutes
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Practical Applications
+ Exercise: Which of the following is a fundamental principle of supervised learning as described in the text? 4 minutes
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Simple and Multiple Linear Regression Models
+ Exercise: Which of the following statements about Linear Regression is correct according to the text provided? 5 minutes
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Cross Validation and Assessment Metrics
+ Exercise: What is the main purpose of cross-validation in machine learning? 5 minutes
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Classification Models: Decision Trees and K-NN
+ Exercise: Which of the following statements about Decision Trees and K-NN is true according to the given text? 4 minutes
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Evaluation of Classification Models
+ Exercise: Which of the following statements about evaluating classification models in Machine Learning (ML) and Deep Learning (DL) is CORRECT? 5 minutes
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Ensemble Learning: Bagging, Boosting and Random Forest
+ Exercise: Which of the following statements about Ensemble Learning techniques is correct? 4 minutes
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Hyperparameter Optimization
+ Exercise: Which of the following methods is known to use probabilistic models to predict which hyperparameters can result in better performances in hyperparameter optimization? 4 minutes
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Dimensionality Reduction and Principal Component Analysis (PCA)
+ Exercise: Which of the following statements is true about Principal Component Analysis (PCA)? 5 minutes
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Clustering Algorithms: K-Means and Hierarchical Clustering
+ Exercise: Which of the following is true about the K-Means clustering algorithm? 5 minutes
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Introduction to Deep Learning and Artificial Neural Networks
+ Exercise: Which of the following statements about Deep Learning and Artificial Neural Networks (ANNs) is correct, according to the text provided? 4 minutes
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Neuron Concepts and Activation Functions
+ Exercise: Which of the following statements about artificial neurons and activation functions is correct according to the text provided? 6 minutes
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Backpropagation and Training of Neural Networks
+ Exercise: Which of the following statements about the backpropagation algorithm is INCORRECT? 5 minutes
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What is Backpropagation
+ Exercise: Which of the following statements correctly describes the backpropagation method used in training neural networks? 3 minutes
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Gradient Calculation
+ Exercise: Which of the following statements about the backpropagation algorithm is correct? 5 minutes
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Chain Rule Derivation
+ Exercise: What is the purpose of the backpropagation algorithm in training deep neural networks? 5 minutes
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Weight Updates with Gradient Descent
+ Exercise: What is the role of the backpropagation algorithm in training neural networks? 4 minutes
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Activation Functions
+ Exercise: Which of the following statements about the Backpropagation algorithm and activation functions in neural networks is correct? 4 minutes
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Learning Rate
+ Exercise: Which of the following statements about the learning rate in training neural networks is correct? 3 minutes
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Momentum and Other Optimization Methods
+ Exercise: Which of the following optimization methods is known to speed up the training of neural networks, helps prevent weights from getting stuck in suboptimal local minima, and incorporates the previous weight change into the current update? 4 minutes
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Vanishing and Exploding Gradient Problems
+ Exercise: Which of the following strategies is NOT mentioned in the text as a way to mitigate vanishing and exploding gradient problems when training neural networks? 4 minutes
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Weight Initialization
+ Exercise: What is the importance of initializing weights in training neural networks, as described in the text? 4 minutes
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Regularization: L1, L2 and Dropout
+ Exercise: Which of the following statements about regularization in neural networks is correct? 5 minutes
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Batch Normalization
+ Exercise: Which of the following statements about Batch Normalization is correct according to the text provided? 5 minutes
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Deep Neural Network Training
+ Exercise: Which of the following statements about backpropagation in deep neural networks is correct? 4 minutes
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Overfitting and Underfitting
+ Exercise: Which of the following techniques is NOT recommended to combat overfitting in neural networks? 4 minutes
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Cross Validation
+ Exercise: Which of the following statements about training neural networks is correct based on the text provided? 4 minutes
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Data Augmentation Techniques
+ Exercise: Which of the following statements is true about the process of backpropagation in neural networks? 4 minutes
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Transfer Learning
+ Exercise: Which of the following statements best describes the process of backpropagation in neural networks? 4 minutes
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Recurrent Neural Networks and Backpropagation Through Time
+ Exercise: Which of the following statements best describes the Backpropagation Through Time (BPTT) process used in training Recurrent Neural Networks (RNNs)? 4 minutes
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Long Short-Term Memory and Gated Recurrent Unit
+ Exercise: Which of the following statements about Long Short-Term Memory (LSTM) and Gated Recurrent Unit (GRU) is true, based on the text provided? 4 minutes
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Deep Learning Frameworks: TensorFlow, PyTorch and Keras
+ Exercise: What is the main function of the backpropagation algorithm in training neural networks? 5 minutes
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Optimizers and Regularization Strategies
+ Exercise: Which of the following statements about optimizers in Machine Learning (ML) and Deep Learning (DL) is correct? 6 minutes
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Building Neural Networks with Keras and TensorFlow
+ Exercise: Which of the following statements about building neural networks with Keras and TensorFlow is correct? 5 minutes
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Introduction to TensorFlow and Keras
+ Exercise: Which of the following statements is true based on the text provided? 4 minutes
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Installation and Environment Configuration
+ Exercise: What is the correct step to activate a Python virtual environment on Windows operating system? 4 minutes
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Fundamentals of Artificial Neural Networks
+ Exercise: Which of the following statements about building artificial neural networks with Keras and TensorFlow is true, according to the text provided? 6 minutes
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Building Sequential Models in Keras
+ Exercise: What is the activation function used in the last dense layer of the sequential model built with Keras, according to the text? 5 minutes
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Dense, Convolutional and Recurrent Layers
+ Exercise: Which of the following statements about building neural networks with Keras and TensorFlow is correct? 5 minutes
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Regularization and Normalization Techniques
+ Exercise: Which of the following statements about regularization and normalization techniques in neural networks is true, according to the text? 5 minutes
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Activation Functions and Weight Initializers
+ Exercise: Which of the following statements about building neural networks with Keras and TensorFlow is true? 5 minutes
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Compiling and Training Deep Learning Models
+ Exercise: Which of the following statements about building and training deep learning models with Keras and TensorFlow is correct? 6 minutes
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Model Performance Assessment and Optimization
+ Exercise: Which of the following techniques is NOT mentioned in the text as a strategy for optimizing the performance of neural networks? 6 minutes
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Callbacks and Saving Models
+ Exercise: Which of the following statements about using callbacks and saving models in Keras is correct? 5 minutes
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Fine-Tuning and Transfer Learning
+ Exercise: Which of the following statements correctly describes the concept of "fine-tuning" in Deep Learning, as explained in the text? 5 minutes
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Neural Networks for Classification and Regression
+ Exercise: Which of the following statements about building neural networks using Keras and TensorFlow is correct? 6 minutes
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Applications in NLP and Computer Vision
+ Exercise: Which of the following statements about building neural networks with Keras and TensorFlow is true according to the text provided? 5 minutes
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Integrating TensorFlow with the Python Data Ecosystem
+ Exercise: Which of the following statements is true about the integration of TensorFlow with Keras as described in the text? 4 minutes
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Deep Learning Models for Computer Vision
+ Exercise: Which of the following convolutional neural network (CNN) architectures was the first to be designed specifically for recognizing digits in bank checks and set the standard for future convolutional networks? 5 minutes
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Convolutional Neural Networks (CNNs)
+ Exercise: Which of the following statements about Convolutional Neural Networks (CNNs) is true? 5 minutes
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Transfer Learning and Fine-Tuning
+ Exercise: Which of the following steps is NOT part of the standard Transfer Learning process? 4 minutes
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Definition of Transfer Learning
+ Exercise: Which of the following statements best describes the concept of Transfer Learning (TL) in the context of Artificial Intelligence (AI)? 5 minutes
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Benefits of Transfer Learning
+ Exercise: Which of the following benefits is associated with using Transfer Learning in machine learning and deep learning? 4 minutes
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Application Scenarios
+ Exercise: Which of the following best describes the Fine-tuning process in the context of Transfer Learning? 4 minutes
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Pre-trained Neural Networks
+ Exercise: Which of the following statements best describes the concept of Transfer Learning in Deep Learning? 3 minutes
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Feature Extraction
+ Exercise: What is the purpose of using a pre-trained model as a feature extractor in the context of transfer learning? 5 minutes
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Fine-Tuning of Layers
+ Exercise: Which of the following steps is NOT correct according to the fine-tuning process described in the text? 4 minutes
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Layer Freezing
+ Exercise: Which of the following statements about Layer Freezing in the context of Transfer Learning and Fine-tuning in Deep Learning is true? 5 minutes
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Adapting Models to New Domains
+ Exercise: Which of the following statements best describes the Fine-tuning process in Machine Learning and Deep Learning? 4 minutes
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Datasets and Data Augmentation
+ Exercise: Which of the following statements best describes the Fine-tuning process in deep learning models? 4 minutes
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Optimizers and Learning Rates
+ Exercise: Which of the following statements about optimizers and learning rates in Transfer Learning is correct? 4 minutes
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Regularization and Avoiding Overfitting
+ Exercise: Which of the following statements about Transfer Learning and Fine-tuning is correct? 5 minutes
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Deep Learning Frameworks: TensorFlow, Keras and PyTorch
+ Exercise: Which of the following statements is true regarding Transfer Learning and Fine-tuning techniques in the context of Deep Learning? 4 minutes
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Model Evaluation and Cross Validation
+ Exercise: Which of the following statements correctly describes the Fine-tuning process in deep learning models? 4 minutes
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Transfer Learning in Computer Vision
+ Exercise: Which of the following statements about Transfer Learning in computer vision is true? 6 minutes
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Transfer Learning in Natural Language Processing
+ Exercise: Which of the following statements about Transfer Learning in Natural Language Processing (NLP) is correct? 4 minutes
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Challenges and Limitations of Transfer Learning
+ Exercise: Which of the following is a common challenge faced when using Transfer Learning? 4 minutes
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Natural Language Processing with Deep Learning
+ Exercise: Which neural network architecture introduced in 2017 has been instrumental in significant advances in various Natural Language Processing (NLP) tasks such as text understanding and language generation? 4 minutes
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Recurrent Neural Networks and LSTM
+ Exercise: Which of the following statements about Recurrent Neural Networks (RNNs) and Long Short-Term Memory (LSTM) is correct? 4 minutes
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Introduction to Reinforcement Learning
+ Exercise: Which of the following components is NOT essential in the Reinforcement Learning (RL) framework? 5 minutes
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Sequence-to-Sequence Models and Attention Mechanisms
+ Exercise: Which of the following statements about sequence-to-sequence (Seq2Seq) models and attention mechanisms is true? 4 minutes
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End-to-End Machine Learning Project Development
+ Exercise: Which of the following steps is considered crucial to better understand data characteristics in a Machine Learning (ML) and Deep Learning (DL) project? 5 minutes
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Strategies for Dealing with Imbalanced Data
+ Exercise: Which of the following techniques is recommended for dealing with imbalanced datasets in machine learning and deep learning problems? 6 minutes
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Interpretability of Machine Learning Models
+ Exercise: Which of the following statements about the interpretability of Machine Learning (ML) models is true according to the text provided? 4 minutes
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Ethics and Bias in Machine Learning
+ Exercise: Which of the following is NOT a recommended approach to mitigating bias in Machine Learning, according to the text? 4 minutes
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Deployment of Machine Learning Models in Production
+ Exercise: Which of the following is an important step in the process of deploying Machine Learning models to production? 5 minutes
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Monitoring and Maintenance of Models in Production
+ Exercise: Which of the following is NOT a best practice for monitoring and maintaining Machine Learning (ML) and Deep Learning (DL) models in production? 4 minutes
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Introduction to Deep Reinforcement Learning Techniques
+ Exercise: Which of the following components is NOT one of the main elements of a Deep Reinforcement Learning (DRL) system, as described in the text? 5 minutes
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Case Studies and Applications of Machine Learning and Deep Learning
+ Exercise: Which of the following applications of Machine Learning (ML) and Deep Learning (DL) is highlighted in the healthcare sector, according to the text? 5 minutes
About the free ebook with audio
Machine Learning and Deep Learning with Python
Build practical artificial intelligence skills with this free online ebook for aspiring data scientists, developers, and technology professionals. Using Python as the core language, it connects essential data work with machine learning, neural networks, and real-world model deployment.
From data to intelligent applications
Start by preparing a productive Python environment and working confidently with datasets through Pandas. Learn to inspect, clean, transform, and visualize data with Matplotlib and Seaborn, then use statistical reasoning to make stronger modeling decisions.
The ebook explains supervised learning workflows, including regression, classification, cross-validation, performance metrics, feature engineering, class balancing, regularization, and hyperparameter optimization. It also introduces clustering, dimensionality reduction, ensemble methods, and model interpretability.
Deep learning in practice
Progress from neural-network foundations to backpropagation, activation functions, optimizers, normalization, and regularization. Apply TensorFlow and Keras to build, train, evaluate, save, and improve deep learning models for classification and regression.
Explore modern applications such as computer vision, convolutional neural networks, natural language processing, recurrent networks, LSTM, attention, transfer learning, and reinforcement learning. The ebook also addresses ethical bias, imbalanced data, deployment, monitoring, maintenance, and end-to-end project development.
What you will gain
- A structured Python-based machine learning workflow
- Practical methods for evaluating and improving models
- Core deep learning concepts and implementation skills
- Awareness of production, monitoring, ethics, and bias considerations
Which Python libraries are used for data analysis and visualization?
The ebook uses Pandas for data manipulation and Matplotlib and Seaborn for exploratory visualization.
Does the ebook cover TensorFlow and Keras neural network development?
Yes. It covers configuring TensorFlow and Keras, building models, training, evaluation, callbacks, and model saving.
Are transfer learning and computer vision included?
Yes. It explains CNNs, pre-trained networks, feature extraction, fine-tuning, and computer vision applications.
This ebook/audiobook includes:
9 hours and 42 minutes of audio content
Digital certificate of course completion (Free)
Exercises to train your knowledge
100% free, from content to certificate
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